抑郁症的MRI脑影像研究

No abstract available

Background: Suicide risk is a major concern for patients with major depressive disorder (MDD). Neuroimaging studies have demonstrated that patients with MDD with suicidal ideation or suicide attempt (MDD-S) are accompanied by neurostructural or functional abnormalities, but there is no consensus of opinion on neural substrate alterations involved in MDD-S. Methods: We performed a whole-brain multimodal meta-analysis of existing magnetic resonance imaging (MRI) studies to identify conjoint and separate alterations of grey matter volume (GMV) and spontaneous brain activity characteristics (regional homogeneity and amplitude of low-frequency fluctuations) between patients with MDD-S and patients with MDD without suicidal ideation or suicidal attempt (MDD-NS) via the seed-based d mapping software. We excluded studies that used other modalities, had overlapping data, or had insufficient information. Results: Our systematic search identified 13 structural MRI studies (471 patients with MDD-S and 508 patients with MDD-NS) and 16 resting-state functional MRI studies (704 patients with MDD-S and 554 patients with MDD-NS) published up to Dec. 5, 2023. Compared with patients with MDD-NS, those with MDD-S showed increased GMV with hypoactivity in the left postcentral gyrus, decreased GMV with hypoactivity in the right inferior parietal gyri, decreased GMV with hyperactivity in the right insula, and separate GMV and functional changes within the bilateral parietal, occipital, and frontal lobes, and the left thalamus. Limitations: We were unable to analyze the association between brain features and clinical detail because of a lack of data. Included studies showed considerable heterogeneity and publication bias. Conclusion: These findings provide a comprehensive overview of brain morphological and spontaneous functional impairments linked to impulsivity, impaired positive reward modulation, emotional disturbances, abnormal emotional processing, and cognitive deficits in MDD-S. These results support an understanding of the relationship between neural substrates and clinical symptoms in MDD-S, and these alterations provide useful insight into pathophysiological mechanisms and intervention strategies to decrease suicide risk in MDD.

Accurate diagnosis of Major Depressive Disorder (MDD) is critical for effective clinical intervention. However, traditional diagnostic methods heavily rely on subjective assessments, which increases the risk of misdiagnosis. T1-weighted magnetic resonance imaging (MRI) has shown great promise in MDD research due to its stability and interpretability. Nevertheless, automatic classification remains challenging due to the heterogeneity of MDD and the complex structural characteristics of the brain. To address this issue, we propose a hierarchical multi-scale feature fusion network for multi-center MDD classification with T1-weighted MRI. This model separately extracts gray matter (GM) and white matter (WM) features with 3D re-parameterized Vision Transformer (3D RepViT), and fuses multi-scale structural information of GM and WM via the 3D hierarchical multi-scale feature fusion (3D HMSFF) module. The 3D RepViT combines CNN-based local feature extraction with Transformer-based global modeling, while re-parameterization improves computational efficiency. The 3D HMSFF module extracts and fuses hierarchical multi-scale features across four stages. Experimental results on the multi-center, large-scale REST-meta-MDD dataset, comprising 2,226 subjects, demonstrate that our method achieves an overall accuracy of 74.89%, a sensitivity of 0.7850, a specificity of 0.7077, and an AUC of 0.8525, outperforming existing methods. These results suggest that our method may serve as an efficient and generalizable solution for automatic MDD classification, with potential clinical applicability.Clinical Relevance—This study proposes an automated MDD classification model using T1-weighted MRI, validated on a multi-center, large-scale dataset (REST-META-MDD) with 2,226 subjects. The model achieves overall accuracy of 74.89% and AUC of 0.8525, demonstrating its potential for pathological interpretability research and clinical-assisted diagnosis of MDD

Abstract Background Compelling evidence claims that gut microbial dysbiosis may be causally associated with major depressive disorder (MDD), with a particular focus on Alistipes. However, little is known about the potential microbiota–gut–brain axis mechanisms by which Alistipes exerts its pathogenic effects in MDD. Methods We collected data from 16S rDNA amplicon sequencing, untargeted metabolomics, and multimodal brain magnetic resonance imaging from 111 MDD patients and 102 healthy controls. We used multistage linked analyses, including group comparisons, correlation analyses, and mediation analyses, to explore the relationships between the gut microbiome (Alistipes), fecal metabolome, brain imaging, and behaviors in MDD. Results Gut microbiome analysis demonstrated that MDD patients had a higher abundance of Alistipes relative to controls. Partial least squares regression revealed that the increased Alistipes was significantly associated with fecal metabolome in MDD, involving a range of metabolites mainly enriched for amino acid, vitamin B, and bile acid metabolism pathways. Correlation analyses showed that the Alistipes-related metabolites were associated with a wide array of brain imaging measures involving gray matter morphology, spontaneous brain function, and white matter integrity, among which the brain functional measures were, in turn, associated with affective symptoms (anxiety and anhedonia) and cognition (sustained attention) in MDD. Of more importance, further mediation analyses identified multiple significant mediation pathways where the brain functional measures in the visual cortex mediated the associations of metabolites with behavioral deficits. Conclusion Our findings provide a proof of concept that Alistipes and its related metabolites play a critical role in the pathophysiology of MDD through the microbiota–gut–brain axis.

No abstract available

Chronic pain (CP) is closely related with major depressive disorder (MDD) and anxiety disorders (ANX), with high comorbidity and shared risk factors. Prior studies have demonstrated common neural correlates across the three disorders, but their neuroanatomic basis is not fully clear. Hence, the preregistered meta-analysis (CRD42019119709) intended to explore common alterations in cortical thickness among CP, MDD, and ANX, a widely used parameter for quantitatively assessing various cerebral conditions with high sensitivity to pathology in neuropsychology. A total of 68 studies comprising 3072 patients and 3427 healthy controls were finally included. Across the disorders, four common clusters with a significant reduction in cortical thickness were identified, including right insula, left anterior cingulate (AC), triangular part of the left inferior gyrus (IFG), and left middle temporal gyrus (MTG). Our findings suggested the shared cortical deficits involving ACC-insula/IFG circuit and left MTG in CP, MDD and ANX, revealing common neural correlates for cognitive and emotional processing in these highly comorbid disorders.

High suicide risk represents a serious problem in patients with major depressive disorder (MDD), yet treatment options that could safely and rapidly ameliorate suicidal ideation remain elusive. Here, we tested the feasibility and preliminary efficacy of the Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT) in reducing suicidal ideation in patients with MDD. Thirty-two MDD patients with moderate to severe suicidal ideation participated in the current study. Suicidal ideation and depression symptoms were assessed before and after 5 days of open-label SAINT. The neural pathways supporting rapid-acting antidepressant and suicide prevention effects were identified with dynamic causal modelling based on resting-state functional magnetic resonance imaging. We found that 5 days of SAINT effectively alleviated suicidal ideation in patients with MDD with a high response rate of 65.63%. Moreover, the response rates achieved 78.13% and 90.63% with 2 weeks and 4 weeks after SAINT, respectively. In addition, we found that the suicide prevention effects of SAINT were associated with the effective connectivity involving the insula and hippocampus, while the antidepressant effects were related to connections of the subgenual anterior cingulate cortex (sgACC). These results show that SAINT is a rapid-acting and effective way to reduce suicidal ideation. Our findings further suggest that distinct neural mechanisms may contribute to the rapid-acting effects on the relief of suicidal ideation and depression, respectively.

Morphological changes in the hippocampal, thalamic, and amygdala subfields have been suggested to form part of the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented in-depth examinations at the subfield level, precluding a fine-grained understanding of these subfields and their involvement in MDD pathophysiology. We uniquely employed ultra-high field MRI at 7.0 Tesla to map hippocampal, thalamic, and amygdala subfields in MDD. Fifty-six MDD patients and 14 healthy controls (HCs) were enrolled in the final analysis. FreeSurfer protocols were used to segment hippocampal, thalamic, and amygdala subfields. Bayesian analysis was then implemented to assess differences between groups and relations with clinical features. While no effect was found for MDD diagnosis (i.e., case–control comparison), clinical characteristics of MDD patients were associated with subfield volumes of the hippocampus, thalamus, and amygdala. Specifically, the severity of depressive symptoms, insomnia, and childhood trauma in MDD patients related to lower thalamic subfield volumes. In addition, MDD patients with typical MDD versus those with atypical MDD showed lower hippocampal, thalamic, and amygdala subfield volumes. MDD patients with recurrent MDD versus those with first-episode MDD also showed lower thalamic subfield volumes. These findings allow uniquely fine-grained insights into hippocampal, thalamic, and amygdala subfield morphology in MDD, linking some of them to the clinical manifestation of MDD.

Whereas meta-analytical data highlight abnormal frontocortical macrostructure (thickness/surface area/volume) in Major Depressive Disorder (MDD), the underlying microstructural processes remain uncharted, due to the use of conventional MRI scanners and acquisition techniques. We uniquely combined Ultra-High Field MRI at 7.0 Tesla with Quantitative Imaging to map intracortical myelin (proxied by longitudinal relaxation time T1) and iron concentration (proxied by transverse relaxation time T2*), microstructural processes deemed particularly germane to cortical macrostructure. Informed by meta-analytical evidence, we focused specifically on orbitofrontal and rostral anterior cingulate cortices among adult MDD patients (N = 48) and matched healthy controls (HC; N = 10). Analyses probed the association of MDD diagnosis and clinical profile (severity, medication use, comorbid anxiety disorders, childhood trauma) with aforementioned microstructural properties. MDD diagnosis (p’s < 0.05, Cohen’s D = 0.55–0.66) and symptom severity (p’s < 0.01, r = 0.271–0.267) both related to decreased intracortical myelination (higher T1 values) within the lateral orbitofrontal cortex, a region tightly coupled to processing negative affect and feelings of sadness in MDD. No relations were found with local iron concentrations. These findings allow uniquely fine-grained insights on frontocortical microstructure in MDD, and cautiously point to intracortical demyelination as a possible driver of macroscale cortical disintegrity in MDD.

Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. It stands as one of the most common mental disorders globally and ranks as the second leading cause of disability. The current diagnostic approach for MDD primarily relies on clinical observations and patient-reported symptoms, overlooking the diverse underlying causes and pathophysiological factors contributing to depression. Therefore, scientific researchers and clinicians must gain a deeper understanding of the pathophysiological mechanisms involved in MDD. There is growing evidence in neuroscience that depression is a brain network disorder, and the use of neuroimaging, such as magnetic resonance imaging (MRI), plays a significant role in identifying and treating MDD. Rest-state functional MRI (rs-fMRI) is among the most popular neuroimaging techniques used to study MDD. Deep learning techniques have been widely applied to neuroimaging data to help with early mental health disorder detection. Recent years have seen a rise in interest in graph neural networks (GNNs), which are deep neural architectures specifically designed to handle graph-structured data like rs-fMRI. This research aimed to develop an ensemble-based GNN model capable of detecting discriminative features from rs-fMRI images for the purpose of diagnosing MDD. Specifically, we constructed an ensemble model by combining functional connectivity features from multiple brain region segmentation atlases to capture brain complexity and detect distinct features more accurately than single atlas-based models. Further, the effectiveness of our model is demonstrated by assessing its performance on a large multi-site MDD dataset. We applied the synthetic minority over-sampling technique (SMOTE) to handle class imbalance across sites. Using stratified 10-fold cross-validation, the best performing model achieved an accuracy of 75.80%, a sensitivity of 88.89%, a specificity of 61.84%, a precision of 71.29%, and an F1-score of 79.12%. The results indicate that the proposed multi-atlas ensemble GNN model provides a reliable and generalizable solution for accurately detecting MDD.

BACKGROUND Major depressive disorder (MDD) is a severe and common mental illness. The first-episode drugs-naive MDD (FEDN-MDD) patients, who have not undergone medication intervention, contribute to understanding the biological basis of MDD. Multimodal Magnetic Resonance Imaging can provide a comprehensive understanding of brain functional and structural abnormalities in MDD. However, most MDD studies use single-modal, small-scale MRI data. And several multimodal studies of MDD are limited to simple linear combinations of functional and structural features. METHODS We screened a large sample of FEDN-MDD patients and healthy controlsmultimodal MRI data. Extracting the fractional amplitude of low-frequency fluctuations (fALFF) feature from functional magnetic resonance imaging and the gray matter volume (GMV) feature from structural magnetic resonance imaging. The mCCA-jICA method was used to integrate these two modal features to investigate the functional-structural co-variation abnormalities in MDD. To validate the stability of the extracted functional-structural covariant abnormalities features, we apply them to identify FEDN-MDD patients. RESULTS The results show that compared to healthy controls, FEDN-MDD patients exhibit joint group-discriminative independent component and modality-specific group-discriminative independent component, suggesting functional-structural covariant abnormalities in MDD patients. Using lightGBM classifier, we achieve a classification accuracy of 99.84 %. LIMITATION We use GMV and fALFF for multimodal fusion shows promise, but requires further validation with other datasets and exploration of additional multimodal features. CONCLUSIONS This may indicate that multimodal fusion features can effectively explore information between different modalities and can accurately identify FEDN-MDD patients, suggesting their potential as multimodal brain imaging biomarkers for MDD.

Major depressive disorder (MDD), anxiety disorders (ANX), and chronic pain (CP) are closely-related disorders with both high degrees of comorbidity among them and shared risk factors. Considering this multi-level overlap, but also the distinct phenotypes of the disorders, we hypothesized both common and disorder-specific changes of large-scale brain systems, which mediate neural mechanisms and impaired behavioral traits, in MDD, ANX, and CP. To identify such common and disorder-specific brain changes, we conducted a transdiagnostic, multimodal meta-analysis of structural and functional MRI-studies investigating changes of gray matter volume (GMV) and intrinsic functional connectivity (iFC) of large-scale intrinsic brain networks across MDD, ANX, and CP. The study was preregistered at PROSPERO (CRD42019119709). 320 studies comprising 10,931 patients and 11,135 healthy controls were included. Across disorders, common changes focused on GMV-decrease in insular and medial-prefrontal cortices, located mainly within the so-called default-mode and salience networks. Disorder-specific changes comprised hyperconnectivity between default-mode and frontoparietal networks and hypoconnectivity between limbic and salience networks in MDD; limbic network hyperconnectivity and GMV-decrease in insular and medial-temporal cortices in ANX; and hypoconnectivity between salience and default-mode networks and GMV-increase in medial temporal lobes in CP. Common changes suggested a neural correlate for comorbidity and possibly shared neuro-behavioral chronification mechanisms. Disorder-specific changes might underlie distinct phenotypes and possibly additional disorder-specific mechanisms.

OBJECTIVE Emerging studies have identified treatment-related connectome predictors in major depressive disorder (MDD). However, quantifying treatment-responsive patterns in structural connectivity (SC) and functional connectivity (FC) simultaneously remains underexplored. We aimed to evaluate whether spatial distributions of FC and SC associated treatment responses are shared or unique. METHODS Diffusion tensor imaging and resting-state functional magnetic resonance imaging were collected from 210 patients with MDD at baseline. We separately developed connectome-based prediction models (CPM) to predict reduction of depressive severity after 6-week monotherapy based on structural, functional, and combined connectomes, then validated them on the external dataset. We identified the predictive SC and FC from CPM with high occurrence frequencies during the cross-validation. RESULTS Structural connectomes (r = 0.2857, p < 0.0001), functional connectomes (r = 0.2057, p = 0.0025), and their combined CPM (r = 0.4, p < 0.0001) can significantly predict a reduction of depressive severity. We didn't find shared connectivity between predictive FC and SC. Specifically, the most predictive FC stemmed from the default mode network, while predictive SC was mainly characterized by within-network SC of fronto-limbic networks. CONCLUSIONS These distinct patterns suggest that SC and FC capture unique connectivity concerning the antidepressant response. SIGNIFICANCE Our findings provide comprehensive insights into the neurophysiology of antidepressants response.

Major depressive disorder (MDD) is a common mental disorder that typically affects a person's mood, cognition, behavior, and physical health. Resting-state functional magnetic resonance imaging (rs-fMRI) data are widely used for computer-aided diagnosis of MDD. While multi-site fMRI data can provide more data for training reliable diagnostic models, significant cross-site data heterogeneity would result in poor model generalizability. Many domain adaptation methods are designed to reduce the distributional differences between sites to some extent, but usually ignore overfitting problem of the model on the source domain. Intuitively, target data augmentation can alleviate the overfitting problem by forcing the model to learn more generalized features and reduce the dependence on source domain data. In this work, we propose a new augmentation-based unsupervised cross-domain fMRI adaptation (AUFA) framework for automatic diagnosis of MDD. The AUFA consists of 1) a graph representation learning module for extracting rs-fMRI features with spatial attention, 2) a domain adaptation module for feature alignment between source and target data, 3) an augmentation-based self-optimization module for alleviating model overfitting on the source domain, and 4) a classification module. Experimental results on 1,089 subjects suggest that AUFA outperforms several state-of-the-art methods in MDD identification. Our approach not only reduces data heterogeneity between different sites, but also localizes disease-related functional connectivity abnormalities and provides interpretability for the model.

Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used for automated diagnosis of brain disorders such as major depressive disorder (MDD) to assist in timely intervention. Multi-site fMRI data have been increasingly employed to augment sample size and improve statistical power for investigating MDD. However, previous studies usually suffer from significant inter-site heterogeneity caused for instance by differences in scanners and/or scanning protocols. To address this issue, we develop a novel discrepancy-based unsupervised cross-domain fMRI adaptation framework (called UFA-Net) for automated MDD identification. The proposed UFA-Net is designed to model spatio-temporal fMRI patterns of labeled source and unlabeled target samples via an attention-guided graph convolution module, and also leverage a maximum mean discrepancy constrained module for unsupervised cross-site feature alignment between two domains. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation for cross-site MDD identification. Extensive evaluation on 681 subjects from two imaging sites shows that the proposed method outperforms several state-of-the-art methods. Our method helps localize disease-associated functional connectivity abnormalities and is therefore well interpretable and can facilitate fMRI-based analysis of MDD in clinical practice.

Objective. Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD. Approach. In this study, we proposed a novel deep learning model that fused both structural MRI (sMRI) and resting-state MRI (rs-fMRI) data to enhance the diagnosis of MDD by capturing the interactions between deep features extracted from different modalities. Specifically, we first employed a brain function encoder (BFE) and a brain structure encoder (BSE) to extract the deep features from fMRI and sMRI, respectively. Then, we designed a function and structure co-attention fusion (FSCF) module that captured inter-modal interactions and adaptively fused multi-modal deep features for MDD diagnosis. Main results. This model was evaluated on a large cohort and achieved a high classification accuracy of 75.2% for MDD diagnosis. Moreover, the attention distribution of the FSCF module assigned higher attention weights to structural features than functional features for diagnosing MDD. Significance. The high classification accuracy highlights the effectiveness and potential clinical of the proposed model.

Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers.

Depression is one of the most common mental health disorders and has been a major focus of research, particularly through the lens of automated diagnostic methods. While many studies have explored magnetic resonance imaging techniques separately, the integration of multiple neuroimaging modalities has received less attention. To address this gap, we introduce a multimodal automatic classification method that leverages both resting-state functional magnetic resonance imaging and structural magnetic resonance imaging. Our approach employs a multi-stream 3D Convolutional Neural Network model to facilitate joint training on diverse features extracted from rs-fMRI and sMRI data. By classifying a combined group of 830 MDD patients and 771 normal controls from the REST-meta-MDD dataset, our model achieves an impressive accuracy of 69.38% using a feature combination of CSF, REHO, and fALFF. This result signifies a notable enhancement in classification performance, contributing valuable insights into the capabilities of multimodal imaging in MDD diagnosis.

Highlights • The major depressive disorder patients (MDD) showed decreased cortical volume in the superior temporal gyrus (STG) and middle temporal gyrus (MTG) than controls.• The MDD patients showed decreased surface area in the bilateral STG, temporal pole gyrus, entorhinal cortex, left inferior temporal gyrus, and fusiform gyrus than the controls.• The MDD patients exhibited lower functional connectivity (FC) between STG/MTG and regions of the visual network than controls.• The structural and functional findings involving the sensory processing areas showed a good classification between the MDD patients and controls.

Objective Inter-hemispheric network dysconnectivity has been well-documented in patients with recurrent major depressive disorder (MDD). However, it has remained unclear how structural networks between bilateral hemispheres relate to inter-hemispheric functional dysconnectivity and depression severity in MDD. Our study attempted to investigate the alterations in corpus callosum macrostructural and microstructural as well as inter-hemispheric homotopic functional connectivity (FC) in patients with recurrent MDD and to determine how these alterations are related with depressive severity. Materials and Methods Resting-state functional MRI (fMRI), T1WI anatomical images and diffusion tensor MRI of the whole brain were performed in 140 MDD patients and 44 normal controls matched for age, sex, years of education. We analyzed the macrostructural and microstructural integrity as well as voxel-mirrored homotopic functional connectivity (VMHC) of corpus callosum (CC) and its five subregion. Two-sample t-test was used to investigate the differences between the two groups. Significant subregional metrics were correlated with depression severity by spearman's correlation analysis, respectively. Results Compared with control subjects, MDD patients had significantly attenuated inter-hemispheric homotopic FC in the bilateral medial prefrontal cortex, and impaired anterior CC microstructural integrity (each comparison had a corrected P < 0.05), whereas CC macrostructural measurements remained stable. In addition, disruption of anterior CC microstructural integrity correlated with a reduction in FC in the bilateral medial prefrontal cortex, which correlated with depression severity in MDD patients. Furthermore, disruption of anterior CC integrity exerted an indirect influence on depression severity in MDD patients through an impairment of inter-hemispheric homotopic FC. Conclusion These findings may help to advance our understanding of the neurobiological basis of depression by identifying region-specific interhemispheric dysconnectivity.

Abstract Background Although postpartum depression (PPD) and non-peripartum major depressive disorder (MDD) occurring within and outside the postpartum period share many clinical characteristics, whether PPD and MDD are the same or not remains controversial. Methods The current study was devoted to identify the shared and different neural circuits between PPD and MDD by resting-state functional magnetic resonance imaging data from 77 participants (22 first-episodic drug-naïve MDD, 26 drug-naïve PPD, and 29 healthy controls (HC)). Results Both the PPD and MDD groups exhibited higher fractional amplitude of low-frequency fluctuation (fALFF) in left temporal pole relative to the HC group; the MDD group showed specifically increased degree centrality in the right cerebellum while PPD showed specifically decreased fALFF in the left supplementary motor area and posterior middle temporal gyrus (pMTG_L), and specifically decreased functional connectivities between pMTG and precuneus and between left subgeneual anterior cingulate cortex (sgACC_L) and right sgACC. Moreover, sgACC and left thalamus showed abnormal regional homogeneity of functional activities between any pair of HC, MDD, and PPD. Conclusions These results provide initial evidence that PPD and MDD have common and distinct neural circuits, which may facilitate understanding the neurophysiological basis and precision treatment for PPD.

BACKGROUND The present study aimed to explore the difference in the brain function and structure between patients with major depressive disorder (MDD) and healthy controls (HCs) using two-center and multi-modal MRI data, which would be helpful to investigate the pathogenesis of MDD. METHODS The subjects were collected from two hospitals. One including 140 patients with MDD and 138 HCs was used as primary cohort. Another one including 29 patients with MDD and 52 HCs was used as validation cohort. Functional and structural magnetic resonance images (MRI) were acquired to extract four types of features: functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV). Then classifiers using different combinations among the four types of selected features were respectively built to discriminate patients from HCs. Different templates were applied and the results under different templates were compared. RESULTS The classifier built with the combination of FC, ALFF, and GMV under the AAL template discriminated patients from HCs with the best performance (AUC=0.916, ACC=84.8%). The regions selected in all the different templates were mainly located in the default mode network, affective network, prefrontal cortex. LIMITATIONS First, the sample size of the validation cohort was limited. Second, diffusion tensor imaging data were not collected. CONCLUSION The performance of classifier was improved by using multi-modal MRI imaging. Different templates would be suitable for different types of analysis. The regions selected in all the different templates are possibly the core regions to investigate the pathophysiology of MDD.

No abstract available

Anxious major depressive disorder is a common subtype of major depressive disorder; however, its unique neural mechanism is not well-understood currently. Using multimodal MRI data, this study examined common and specific alterations of amygdala subregions between patients with and without anxiety. No alterations were observed in the gray matter volume or intra-region functional integration in either patient group. Compared with the controls, both patient groups showed decreased functional connectivity between the left superficial amygdala and the left putamen, and between the right superficial amygdala and the bilateral anterior cingulate cortex and medial orbitofrontal cortex, while only patients with anxiety exhibited decreased activity in the bilateral laterobasal and superficial amygdala. Moreover, the decreased activity correlated negatively with the Hamilton depression scale scores in the patients with anxiety. These findings provided insights into the pathophysiologic processes of anxious major depressive disorder and may help to develop new and effective treatment programs.

Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD-relevant alterations is still limited due to the scarcity of large-sample, multisite datasets. Here, we address this issue by including a large R-fMRI dataset with 1,434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long-range connections are computed using the amplitude of low-frequency fluctuations, regional homogeneity and distance-related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across-center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between-group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain-clinical variable relationship exhibits poor cross-center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P<.05 and a height threshold of P<.001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder.

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BACKGROUND Psilocybin is a serotonergic psychedelic drug under assessment as a potential therapy for treatment-resistant and major depression. Heterogeneous treatment responses raise interest in predicting the outcome from baseline data. METHODS A machine learning pipeline was implemented to investigate baseline resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI) as a predictor of symptom severity in psilocybin monotherapy for treatment-resistant depression (16 patients administered two 5 mg capsules followed by 25 mg, separated by one week). Generalizability was tested in a sample of 22 patients who participated in a psilocybin vs. escitalopram trial for moderate-to-severe major depression (two separate doses of 25 mg of psilocybin 3 weeks apart plus 6 weeks of daily placebo vs. two separate doses of 1 mg of psilocybin 3 weeks apart plus 6 weeks of daily oral escitalopram). The analysis was repeated using both samples combined. RESULTS Functional connectivity of visual, default mode and executive networks predicted early symptom improvement, while the salience network predicted responders up to 24 weeks after treatment (accuracy≈0.9). Generalization performance was borderline significant. Consistent results were obtained from the combined sample analysis. Fronto-occipital and fronto-temporal coupling predicted early and late symptom reduction, respectively. LIMITATIONS The number of participants and differences between the two datasets limit the generalizability of the findings, while the lack of a placebo arm limits their specificity. CONCLUSIONS Baseline neurophysiological measurements can predict the outcome of psilocybin treatment for depression. Future research based on larger datasets should strive to assess the generalizability of these predictions.

Background Anxious depression, which is a common subtype of major depressive disorder, has distinct clinical features from nonanxious depression. However, little is known about the neurobiological characteristics of anxious depression. In this study, we explored resting-state regional brain activity changes between anxious depression and nonanxious depression. Method Resting-state functional magnetic resonance (rs-fMRI) imaging data were collected from 60 patients with anxious depression, 38 patients with nonanxious depression, and 60 matched healthy controls (HCs). One-way analysis of variance was performed to compare the whole-brain fractional amplitude of low-frequency fluctuation (fALFF) in the three groups. The correlation between the fALFF values and the clinical measures was examined. Results Compared with those of HCs, the fALFF values in the left superior temporal gyrus (STG) in patients with anxious depression were significantly increased, while the fALFF values in the left middle temporal gyrus (MTG), left STG, and right STG in patients with nonanxious depression were significantly increased. Patients with anxious depression showed reduced fALFF values in the right STG compared with patients with nonanxious depression ( p  < 0.001, corrected). Within the anxious depression group, fALFF value in the right STG was positively correlated with the cognitive disturbance score ( r  = 0.36, p  = 0.005 corrected). Conclusion The bilateral STG and left MTG, which are related to the default mode network, appear to be key brain regions in nonanxious depression, while the right STG plays an essential role in the neuropathological mechanism of anxious depression.

Highlights • We recorded oxy-Hb changes during iTBS using fNIRS over the dlPFC.• This dlPFC region was used as seed in resting-state functional connectivity fMRI.• Symptom improvement was associated with reduced dlPFC-connectivity to the insula.• fNIRS oxy-Hb increase was associated with reduced dlPFC-connectivity to the PPC.• Combining fNIRS and fMRI may facilitate understanding the hemodynamic response to iTBS.

Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients ( N  = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD_17 change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment.

Depression alleviation following treatment with repetitive transcranial magnetic stimulation (rTMS) tends to be more effective when TMS is targeted to cortical areas with high resting state functional connectivity (rsFC) with the subgenual anterior cingulate cortex (sgACC). However, it has not yet been confirmed that rsFC-guided TMS coil placement leads to TMS modulation of the sgACC. For each participant (N=115, 34 depressed patients), a peak rsFC cortical ‘hotspot’ for the sgACC and control targets were prospectively identified. Single pulses of TMS interleaved with fMRI readouts were then administered to these targets and established significant downstream fMRI BOLD responses in the sgACC. We then marked an association between TMS-evoked BOLD responses in the sgACC and rsFC between the stimulation site and sgACC. This effect was qualified by a difference between healthy and patient participants: only in depressed patients, positively connected sites of stimulation led to the strongest evoked responses in the sgACC. Our results highlight rsFC-based targeting as a viable strategy to causally modulate sgACC subcortical targets and further suggest that cortical sites with high positive rsFC to the sgACC might represent an alternative target for the treatment of depression.

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

Background Somatic depression (SD) is different from non-somatic depression (NSD), and insular subregions have been associated with somatic symptoms. However, the pattern of damage in the insular subregions in SD remains unclear. The aim of this study was to use functional connectivity (FC) analyses to explore the bilateral ventral anterior insula (vAI), bilateral dorsal anterior insula (dAI), and bilateral posterior insula (PI) brain circuits in SD patients. Methods The study included 28 SD patients, 30 NSD patients, and 30 matched healthy control (HC) subjects. All participants underwent 3.0 T resting state functional magnetic resonance imaging. FC analyses were used to explore synchronization between insular subregions and the whole brain in the context of depression with somatic symptoms. Pearson correlation analyses were performed to assess relationships between FC values in brain regions showing significant differences and the total and factor scores on the 17-item Hamilton Rating Scale for Depression (HAMD 17 ). Results Compared with the NSD group, the SD group showed significantly decreased FC between the left vAI and the right rectus gyrus, right fusiform gyrus, and right angular gyrus; between the right vAI and the right middle cingulate cortex, right precuneus, and right superior frontal gyrus; between the left dAI and the left fusiform gyrus; and between the right dAI and the left postcentral gyrus. Relative to the NSD group, the SD group exhibited increased FC between the left dAI and the left fusiform gyrus. There were no differences in FC between bilateral PI and any brain regions among the SD, NSD, and HC groups. Within the SD group, FC values between the left vAI and right rectus gyrus were positively correlated with cognitive impairment scores on the HAMD 17 ; FC values between the right vAI and right superior frontal gyrus were positively related to the total scores and cognitive impairment scores on the HAMD 17 ( p <  0.05, uncorrected). Conclusions Aberrant FC between the anterior insula and the frontal and limbic cortices may be one possible mechanism underlying SD.

Background: The efficacy of repetitive transcranial magnetic stimulation (rTMS) for depression may vary depending on the subregion stimulated within the dorsolateral prefrontal cortex (DLPFC). Clinical TMS typically uses scalp-based landmarks for DLPFC targeting, rather than individualized MRI guidance. Objective: In rTMS patients, determine the brain systems targeted by multiple DLPFC stimulation rules by computing several surrogate measures: underlying brain targets labeled with connectivity-based atlases, subgenual cingulate anticorrelation strength, and functionally connected networks. Methods: Forty-nine patients in a randomized controlled trial of rTMS therapy for treatment resistant major depression underwent structural and functional MRI. DLPFC rules were applied virtually using MR-image guidance. Underlying cortical regions were labeled, and connectivity with the subgenual cingulate and whole-brain computed. Results: Scalp-targeting rules applied post hoc to these MRIs that adjusted for head size, including Beam F3, were comparably precise, successful in directly targeting classical DLPFC and frontal networks, and anticorrelated with the subgenual cingulate. In contrast, all rules involving fixed distances introduced variability in regions and networks targeted. The 5 cm rule targeted a transitional DLPFC region with a different connectivity profile from the adjusted rules. Seed-based connectivity analyses identified multiple regions, such as posterior cingulate and inferior parietal lobe, that warrant further study in order to understand their potential contribution to clinical response. Conclusion: EEG-based rules consistently targeted DLPFC brain regions with resting-state fMRI features known to be associated with depression response. These results provide a bridge from lab to clinic by enabling clinicians to relate scalp-targeting rules to functionally connected brain systems.

No abstract available

BACKGROUND Physical exercise has been proved to reduce the risk of major depression in Subthreshold depression (StD) individuals effectively, yet little is known about the spontaneous brain activity changes associated with physical exercise. METHODS A total of 70 adult subjects, including 38 StD and 32 healthy control (HC) subjects, underwent a resting-state functional magnetic resonance imaging (rs-fMRI) before and after eight-week aerobic exercise respectively. Then, the amplitude of low-frequency fluctuation (ALFF) alterations between the two groups were quantitatively analyzed. RESULTS Before exercise intervention, the rs-fMRI data showed increased ALFF of the right putamen in the StD group compared with HC group. After exercise intervention, there was no significant ALFF change observed between the StD and HC groups. The longitudinal ALFF differences from pre- to post- exercise intervention showed significantly decreased ALFF in the right middle and inferior occipital gyrus, right middle and inferior temporal gyrus, right fusiform gyrus (FG), while increased ALFF in the right middle cingulate, right superior parietal louble, right inferior parietal lobule (IPL) (inferior parietal gyrus and supramarginal gyrus), and bilateral precuneus in the StD group. As for HC group, the results showed that decreased ALFF in the right FG and right parahippocampus, while increased ALFF in the right precuneus, right middle cingulate, right supplementary motor area, right superior parietal lobule and right paracentral lobule in the HC group. No significant correlation between changes of ALFF and clinical scale scores in the StD group. LIMITATIONS The definitions of StD are varied in terms of different studies, the final sample size was relatively small, and the age range of the subjects in this study was narrow. Meanwhile, the exercise intervention trial was short-term. CONCLUSIONS These results further support the standpoint that physical exercise has the potential to reshape the abnormal patterns of spontaneous brain activity in adults with StD.

Major depressive disorder (MDD) is one of the most widespread mental disorders and can result in suicide. Suicidal ideation (SI) is strongly predictive of death by suicide, and electroconvulsive therapy (ECT) is effective for MDD, especially in patients with SI. In the present study, we aimed to determine differences in resting-state functional magnetic resonance imaging (rs-fMRI) in 14 adolescents aged 12–17 with MDD and SI at baseline and after ECT. All participants were administered the Hamilton Depression Scale (HAMD) and Beck Scale for Suicide Ideation (BSSI) and received rs-fMRI scans at baseline and after ECT. Following ECT, the amplitude of low frequency fluctuation (ALFF) and fractional ALFF (fALFF) significantly decreased in the right precentral gyrus, and the degree centrality (DC) decreased in the left triangular part of the inferior frontal gyrus and increased in the left hippocampus. There were significant negative correlations between the change of HAMD (ΔHAMD) and ALFF in the right precentral gyrus at baseline, and between the change of BSSI and the change of fALFF in the right precentral gyrus. The ΔHAMD was positively correlated with the DC value of the left hippocampus at baseline. We suggest that these brain regions may be indicators of response to ECT in adolescents with MDD and SI.

Network mechanisms of depression development and especially of improvement from nonpharmacological treatment remain understudied. The current study is aimed at examining brain networks functional connectivity in depressed patients and its dynamics in nonpharmacological treatment. Resting state fMRI data of 21 healthy adults and 51 patients with mild or moderate depression were analyzed with spatial independent component analysis; then, correlations between time series of the components were calculated and compared between-group (study 1). Baseline and repeated-measure data of 14 treated (psychotherapy or fMRI neurofeedback) and 15 untreated depressed participants were similarly analyzed and correlated with changes in depression scores (study 2). Aside from diverse findings, studies 1 and 2 both revealed changes in within-default mode network (DMN) and DMN to executive control network (ECN) connections. Connectivity in one pair, initially lower in depression, decreased in no treatment group and was inversely correlated with Montgomery-Asberg depression score change in treatment group. Weak baseline connectivity in this pair also predicted improvement on Montgomery-Asberg scale in both treatment and no treatment groups. Coupling of another pair, initially stronger in depression, increased in therapy though was unrelated to improvement. The results demonstrate possible role of within-DMN and DMN-ECN functional connectivity in depression treatment and suggest that neural mechanisms of nonpharmacological treatment action may be unrelated to normalization of initially disrupted connectivity.

No abstract available

Background Interleukin-18 (IL-18) may participate in the development of major depressive disorder, but the specific mechanism remains unclear. This study aimed to explore whether IL-18 correlates with areas of the brain associated with depression. Methods Using a case–control design, 68 subjects (34 patients and 34 healthy controls) underwent clinical assessment, blood sampling, and resting-state functional Magnetic Resonance Imaging (fMRI). The total Hamilton depression-17 (HAMD-17) score was used to assess depression severity. Enzyme-linked immunosorbent assay (ELISA) was used to detect IL-18 levels. Rest-state fMRI was conducted to explore spontaneous brain activity. Results The level of IL-18 was higher in patients with depression in comparison with healthy controls. IL-18 was negatively correlated with degree centrality of the left posterior cingulate gyrus in the depression patient group, but no correlation was found in the healthy control group. Conclusion This study suggests the involvement of IL-18 in the pathophysiological mechanism for depression and interference with brain activity.

Affective disorders are associated with maladaptive emotion regulation strategies. In particular, the left more than the right ventrolateral prefrontal cortex (vlPFC) may insufficiently regulate emotion processing, e.g., in the amygdala. A double-blind cross-over study investigated NF-supported cognitive reappraisal training in major depression (n = 42) and age- and gender-matched controls (n = 39). In a randomized order, participants trained to upregulate either the left or the right vlPFC during cognitive reappraisal of negative images on two separate days. We wanted to confirm regional specific NF effects with improved learning for left compared to right vlPFC (ClinicalTrials.gov NCT03183947). Brain responses and connectivity were studied with respect to training progress, gender, and clinical outcomes in a 4-week follow-up. Increase of vlPFC activity was stronger after NF training from the left- than the right-hemispheric ROI. This regional-specific NF effect during cognitive reappraisal was present across patients with depression and controls and supports a central role of the left vlPFC for cognitive reappraisal. Further, the activity in the left target region was associated with increased use of cognitive reappraisal strategies (r = 0.48). In the 4-week follow-up, 75% of patients with depression reported a successful application of learned strategies in everyday life and 55% a clinically meaningful symptom improvement suggesting clinical usability.

Objection This study was a primary study to evaluate the instant and sustained effect of electroacupuncture (EA) at GV20 (Baihui) in postgraduate students with mild depression by using a special flexible head coil. Methods A total of 20 postgraduate students with mild depression underwent EA stimulation at GV20 and 3 phases of resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Phase I: Preparation (before needle insertion); Phase II: during EA; Phase III: 15 minutes after needle removal. The Rs-fMRI data were processed using DPABI and SPSS 25. Results 1) ReHo values showed significantly differences in the right posterior cingulate cortex, right calcarine gyrus, right angular gyrus, right precuneus, right cuneus, and bilateral postcentral gyri among Phase I, Phase II and Phase III; 2) Relative to the Phase I, increased brain activity in the Phase II was observed in the bilateral postcentral gyri, right calcarine gyrus, right cuneus. Compared with the Phase II, decreased brain activity in the Phase III was observed in the right precuneus, right posterior cingulate cortex, right angular gyrus. Relative to the Phase I, Significantly increased brain activity in the Phase III was observed in the right calcarine gyrus, right cuneus, and bilateral postcentral gyri. While decreased ReHo values were found in the right posterior cingulate cortex, right angular gyrus, right precuneus; and 3) Correlation analysis showed that the ReHo values of multiple brain regions in Phase I and Phase III were significantly correlated with the VAS and HRSD-17 scores. Conclusion This study focuses on the instant and sustained effect in postgraduate students with depression. Our study showed that instant effect produced by EA stimulation at GV20 firstly induced changes in somatosensory and visual area, and then, sustained effect (Phase III) have a higher intensity and more extensive than instant effects. Meanwhile, we provide a visualization way to study the instant effects of head acupoints by using a flexible head coil.

Convergent evidence indicates that individuals with symptoms of depression exhibit altered functional connectivity (FC) of the amygdala, which is a key brain region in processing emotions. At present, the characteristics of amygdala functional circuits in patients with mild cognitive impairment (MCI) with and without depression are not clear. The current study examined the features of amygdala FC in patients with MCI with depression symptoms (D-MCI) using resting-state functional magnetic resonance imaging. We acquired resting-state functional magnetic resonance imaging data from 16 patients with D-MCI, 18 patients with MCI with no depression (nD-MCI), and 20 healthy controls (HCs) using a 3T scanner and compared the strength of amygdala FC between the three groups. Patients with D-MCI exhibited significant FC differences in the amygdala–medial prefrontal cortex and amygdala–sensorimotor networks. These results suggest that the dysfunction of the amygdala–medial prefrontal cortex network and the amygdala–sensorimotor network might be involved in the neural mechanism underlying depression in MCI.

OBJECTIVE To study the neuroimaging mechanisms of repetitive transcranial magnetic stimulation (rTMS) in treating major depressive disorder (MDD). METHODS Twenty-seven treatment-naive patients with major depressive disorder (MDD) and 27 controls were enrolled. All of them were scanned with resting-state functional magnetic resonance imaging (fMRI) at baseline, and 15 patients were rescanned after two-week rTMS. The amplitude of low frequency fluctuation (ALFF) and functional connection degree (FCD), based on voxels and 3 brain networks (default mode network [DMN], central executive network [CEN], salience network[SN]),were used as imaging indicators to analyze. The correlations of brain imaging changes after rTMS with clinical efficacy were calculated. RESULTS At baseline, patients groups showed increased ALFF in the right orbital frontal cortex (OFC) and decreased ALFF in the left striatal cortex and medial prefrontal cortex (PFC), while increased FCD in the right dorsal anterior cingulate cortex and OFC and decreased FCD in the right inferior parietal lobe and in the CEN. After rTMS, patients showed increased ALFF in the left dorsolateral prefrontal cortex (DLPFC)and superior frontal gyrus, FCD in the right dorsal anterior cingulate cortex, superior temporal gyrus and CEN, as well as decreased FCD in the bilateral lingual gyrus than pre-rTMS . These rTMS induced neuroimaging changes did not significantly correlated with clinical effecacy. CONCLUSIONS This study indicated that rTMS resulted in changes of ALFF and FCD in some brain regions and CEN. But we could not conclude this is the neuroimaging mechanism of rTMS according to the correlation analysis.

Psilocybin with psychological support is showing promise as a treatment model in psychiatry but its therapeutic mechanisms are poorly understood. Here, cerebral blood flow (CBF) and blood oxygen-level dependent (BOLD) resting-state functional connectivity (RSFC) were measured with functional magnetic resonance imaging (fMRI) before and after treatment with psilocybin (serotonin agonist) for treatment-resistant depression (TRD). Quality pre and post treatment fMRI data were collected from 16 of 19 patients. Decreased depressive symptoms were observed in all 19 patients at 1-week post-treatment and 47% met criteria for response at 5 weeks. Whole-brain analyses revealed post-treatment decreases in CBF in the temporal cortex, including the amygdala. Decreased amygdala CBF correlated with reduced depressive symptoms. Focusing on a priori selected circuitry for RSFC analyses, increased RSFC was observed within the default-mode network (DMN) post-treatment. Increased ventromedial prefrontal cortex-bilateral inferior lateral parietal cortex RSFC was predictive of treatment response at 5-weeks, as was decreased parahippocampal-prefrontal cortex RSFC. These data fill an important knowledge gap regarding the post-treatment brain effects of psilocybin, and are the first in depressed patients. The post-treatment brain changes are different to previously observed acute effects of psilocybin and other ‘psychedelics’ yet were related to clinical outcomes. A ‘reset’ therapeutic mechanism is proposed.

Background Abnormalities of functional and structural connectivity in the amygdala-prefrontal circuit which involved with emotion processing have been implicated in adults with major depressive disorder (MDD). Adolescent MDD may have severer dysfunction of emotion processing than adult MDD. In this study, we used resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) to examine the potential functional and structural connectivity abnormalities within amygdala-prefrontal circuit in first-episode medication-naïve adolescents with MDD. Methods Rs-fMRI and DTI data were acquired from 36 first-episode medication-naïve MDD adolescents and 37 healthy controls (HC). Functional connectivity between amygdala and the prefrontal cortex (PFC) and fractional anisotropy (FA) values of the uncinate fasciculus (UF) which connecting amygdala and PFC were compared between the MDD and HC groups. The correlation between the FA value of UF and the strength of the functional connectivity in the PFC showing significant differences between the two groups was identified. Results Compared with the HC group, decreased functional connectivity between left amygdala and left ventral PFC was detected in the adolescent MDD group. FA values were significant lower in the left UF within the adolescent MDD group compared to the HC group. There was no significant correlation between the UF and FA, and the strength of functional connectivity within the adolescent MDD group. Conclusions First-episode medication-naïve adolescent MDD showed decreased functional and structural connectivity in the amygdala-prefrontal circuit. These findings suggest that both functional and structural abnormalities of the amygdala-prefrontal circuit may present in the early onset of adolescent MDD and play an important role in the neuropathophysiology of adolescent MDD.

BACKGROUND Entropy analysis is a computational method used to quantify the complexity in a system, and loss of brain complexity is hypothesized to be related to mental disorders. Here, we applied entropy analysis to the resting-state functional magnetic resonance imaging (rs-fMRI) signal in subjects with late-life depression (LLD), an illness combined with emotion dysregulation and aging effect. METHODS A total of 35 unremitted depressed elderly and 22 control subjects were recruited. Multiscale entropy (MSE) analysis was performed in the entire brain, 90 automated anatomical labeling-parcellated ROIs, and five resting networks in each study participant. LIMITATIONS Due to ethical concerns, all the participants were under medication during the study. RESULTS Regionally, subjects with LLD showed decreased entropy only in the right posterior cingulate gyrus but had universally increased entropy in affective processing (putamen and thalamus), sensory, motor, and temporal nodes across different time scales. We also found higher entropy in the left frontoparietal network (FPN), which partially mediated the negative correlation between depression severity and mental components of the quality of life, reflecting the possible neural compensation during depression treatment. CONCLUSION MSE provides a novel and complementary approach in rs-fMRI analysis. The temporal-spatial complexity in the resting brain may provide the adaptive variability beneficial for the elderly with depression.

BACKGROUND Somatic symptoms are common among patients with major depressive disorder (MDD), and are known to negatively impact the course and severity of the disease. Although previous studies have attempted to explore the neuropathology of MDD, little is known regarding the neural basis of somatic symptoms in MDD. METHODS Resting-state functional magnetic resonance images of 28 MDD patients with somatic symptoms (somatic depression, SD), 30 patients without somatic symptoms (non-somatic depression, NSD) and 30 healthy controls (HC) were obtained. We investigated the neural basis of MDD with somatic symptoms based on the measure of regional homogeneity (ReHo). We also investigated whether the altered regional homogeneity may be correlated to any clinical features of depression. These comparison were also carried out in female and male subjects respectively. RESULTS The SD exhibited higher ReHo in the bilateral parahippocampus and left lingual gyrus than HC, as well as lower ReHo in the right frontal gyrus. Relative to NSD, the SD exhibited lower ReHo in the right middle frontal gyrus and left precentral gyrus. Furthermore, in the SD, ReHo in the left precentral gyrus was positively correlated with cognitive factor scores of the HAMD-17. In female subjects, SD exhibited increased ReHo in the right STG and decreased ReHo in the right MFG, relative to women of the NSD group. CONCLUSIONS Our preliminary findings indicated that abnormal ReHo in the frontal and temporal regions may play an important role in the neural basis of somatic depression.

Mismatch responses reflect neural mechanisms of early cognitive processing in the auditory domain. Disturbances of these mechanisms on multiple levels of neural processing may contribute to clinical symptoms in major depression (MD). A functional magnetic resonance imaging (fMRI) study was conducted to identify neurobiological foundations of altered mismatch processing in MD. Twenty‐five patients with major depression and 25 matched healthy individuals completed an auditory mismatch paradigm optimized for fMRI. Brain activity during mismatch processing was compared between groups. Moreover, seed‐based connectivity analyses investigated depression‐specific brain networks. In patients, mismatch processing was associated with reduced activation in the right auditory cortex as well as in a fronto‐parietal attention network. Moreover, functional coupling between the right auditory cortex and frontal areas was reduced in patients. Seed‐to voxel analysis on the whole‐brain level revealed reduced connectivity between the auditory cortex and the thalamus as well as posterior cingulate. The present study indicates deficits in sensory processing on the level of the auditory cortex in depression. Hyposensitivity in a fronto‐parietal network presumably reflects altered attention mechanisms in depression. The observed impairments may contribute to psychopathology by reducing the ability of the affected individuals to orient attention toward important environmental cues.

Resting-state fMRI has the potential to help doctors detect abnormal behavior in brain activity and to diagnose patients with depression. However, resting-state fMRI has a bias depending on the scanner site, which makes it difficult to diagnose depression at a new site. In this paper, we propose methods to improve the performance of the diagnosis of major depressive disorder (MDD) at an independent site by reducing the site bias effects using regression. For this, we used a subgroup of healthy subjects of the independent site to regress out site bias. We further improved the classification performance of patients with depression by focusing on melancholic depressive disorder. Our proposed methods would be useful to apply depression classifiers to subjects at completely new sites.

Background Attentional bias modification (ABM) may lead to more adaptive emotion perception and emotion regulation. Understanding the neural basis of these effects may lead to greater precision for the development of future treatments. Task-related functional MRI (fMRI) after ABM training has not been investigated in depression so far. The main aim of this randomized controlled trial was to explore differences in brain activity after ABM training, in response to emotional stimuli. Methods A total of 134 people with previous depression, who had been treated for depression and had various degrees of residual symptoms, were randomized to 14 days of active ABM or a closely matched placebo training, followed by an fMRI emotion regulation task. The training procedure was a classical dot–probe task with emotional face stimuli. In the active ABM condition, the probes replaced the more positively valenced face of a given pair. As participants implicitly learned to predict the probe location, this would be likely to induce a more positive attentional bias. The placebo condition was identical, except for the contingency of the probe, which appeared equally behind positive and negative stimuli. We compared depression symptoms and subjective ratings of perceived negativity during fMRI between the training groups. We explored brain activation in predefined regions of interest and across the whole brain. We explored activation in areas associated with changes in attentional bias and degree of depression. Results Compared with the placebo group, the ABM group showed reduced activation in the amygdala and the anterior cingulate cortex when passively viewing negative images. We found no group differences in predefined regions of interest associated with emotion regulation strategies. Response in the temporal cortices was associated with the degree of change in attentional bias and the degree of depressive symptoms in ABM versus placebo. Limitations These findings should be replicated in other samples of patients with depression, and in studies using fMRI designs that allow analyses of within-group variability from baseline to follow-up. Conclusion Attentional bias modification training has an effect on brain function in the circuitry associated with emotional appraisal and the generation of affective states. Clinicaltrials.gov identifier NCT02931487

Functional magnetic resonance imaging neurofeedback (fMRI-NF) training of areas involved in emotion processing can reduce depressive symptoms by over 40% on the Hamilton Depression Rating Scale (HDRS). However, it remains unclear if this efficacy is specific to feedback from emotion-regulating regions. We tested in a single-blind, randomized, controlled trial if upregulation of emotion areas (NFE) yields superior efficacy compared to upregulation of a control region activated by visual scenes (NFS). Forty-three moderately to severely depressed medicated patients were randomly assigned to five sessions augmentation treatment of either NFE or NFS training. At primary outcome (week 12) no significant group mean HDRS difference was found (B = −0.415 [95% CI −4.847 to 4.016], p = 0.848) for the 32 completers (16 per group). However, across groups depressive symptoms decreased by 43%, and 38% of patients remitted. These improvements lasted until follow-up (week 18). Both groups upregulated target regions to a similar extent. Further, clinical improvement was correlated with an increase in self-efficacy scores. However, the interpretation of clinical improvements remains limited due to lack of a sham-control group. We thus surveyed effects reported for accepted augmentation therapies in depression. Data indicated that our findings exceed expected regression to the mean and placebo effects that have been reported for drug trials and other sham-controlled high-technology interventions. Taken together, we suggest that the experience of successful self-regulation during fMRI-NF training may be therapeutic. We conclude that if fMRI-NF is effective for depression, self-regulation training of higher visual areas may provide an effective alternative.

Purpose: Although efforts have been made to identify neurobiological characteristic of major depressive disorder (MDD) in recent years, trait- and state-related biological characteristics of MDD still remains unclear. Using functional magnetic resonance imaging (fMRI), the aim of this study was to explore whether altered spontaneous neural activities in MDD are trait- or state- related. Materials and Methods: Resting-state fMRI data were analyzed for 72 current MDD (cMDD) patients (first-episode, medication-naïve), 49 remitted MDD (rMDD) patients, and 78 age- and sex- matched healthy control (HC) subjects. The values of amplitude of low-frequency fluctuation (ALFF) were compared between groups. Results: Compared with the cMDD group, the rMDD group had increased ALFF values in the left middle occipital gyrus, left middle temporal gyrus and right cerebellum anterior lobe. Besides, compared with the HC group, the cMDD group had decreased ALFF values in the left middle occipital gyrus. Further analysis explored that the mean ALFF values in the left middle occipital gyrus, left middle temporal gyrus and right cerebellum anterior lobe were correlated positively with BDI scores in rMDD patients. Conclusion: Abnormal activity in the left middle occipital gyrus, left middle temporal gyrus and right cerebellum anterior lobe may be state-specific in current (first-episode, medication-naïve) and remitted (medication-naïve) depression patients. Furthermore, the state-related compensatory effect was found in these brain areas.

BACKGROUND Major depressive disorder (MDD) is a severe mental illness, and the Hamilton Depression Rating Scale (HAMD) is commonly used to quantify its severity. Our aim is to develop a predictive model for MDD symptoms using machine learning techniques based on effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI). NEW METHOD We obtained large-scale rs-fMRI data and HAMD scores from the multi-site REST-meta-MDD dataset. Average time series were extracted using different atlases. Brain EC features were computed using Granger causality analysis based on symbolic path coefficients, and a machine learning model based on EC was constructed to predict HAMD scores. Finally, the most predictive features were identified and visualized. RESULTS Experimental results indicate that different brain atlases significantly impact predictive performance, with the Dosenbach atlas performing best. EC-based models outperformed functional connectivity, achieving the best predictive accuracy (r=0.81, p<0.001, Root Mean Squared Error=3.55). Among various machine learning methods, support vector regression demonstrated superior performance. COMPARISON WITH EXISTING METHODS Current phenotype score prediction primarily relies on FC, which cannot indicate the direction of information flow within brain networks. Our method is based on EC, which contains more comprehensive brain network information and has been validated on large-scale multi-site data. CONCLUSIONS Brain network connectivity features effectively predict HAMD scores in MDD patients. The identified EC feature network may serve as a biomarker for predicting symptom severity. Our work may provide clinically significant insights for the early diagnosis of MDD, thereby facilitating the development of personalized diagnostic tools and therapeutic interventions.

Abstract More and more evidence show that major depressive disorder (MDD) is closely related to inflammation caused by chronic stress, which seriously affects human physical and mental health. However, the inflammatory mechanism of depression and its effect on brain function have not been clarified. Based on resting‐state functional magnetic resonance imaging (rs‐fMRI), we investigated change of brain functional imaging and the inflammatory mechanism of damage‐related molecular patterns (DAMPs)—receptor of advanced glycation protein end product (RAGE) in MDD patients and depressive‐like cynomolgus monkeys and mice models induced by chronic stress. The regional homogeneity (ReHo) and functional connectivity (FC) were analyzed using MATLAB and SPM12 software. We detected the expression of DAMPs‐RAGE pathway‐related proteins and mRNA in MDD peripheral blood and in serum and brain tissue of cynomolgus monkeys and mice. Meanwhile, RAGE gene knockout mice, RAGE inhibitor, and overexpression of AVV9RAGE adeno‐associated virus were used to verify that RAGE is a reliable potential biomarker of depression. The results showed that the ReHo value of prefrontal cortex (PFC) in MDD patients and depressive‐like cynomolgus monkeys was decreased. Then, the PFC was used as a seed point, the FC of ipsilateral and contralateral PFC were weakened in depressive‐like mice. At the same time, qPCR showed that RAGE and HMGB1 mRNA were upregulated and S100β mRNA was downregulated. The expression of RAGE‐related inflammatory protein in PFC of depressive‐like monkeys and mice were consistent with that in peripheral blood of MDD patients. Moreover, the results were confirmed in RAGE –/– mice, injection of FPS‐ZM1, and overexpression of AAV9 RAGE in mice. To sum up, our findings enhance the evidence that chronic stress‐PFC‐RAGE are associated with depression. These results attempt to establish the links between brain functional imaging, and molecular targets among different species will help to reveal the pathophysiological mechanism of depression from multiple perspectives.

No abstract available

Major depressive disorder (MDD) is a globally prevalent psychiatric disorder that significantly impairs quality of life and increases suicide risk. Accurate identification of MDD is critical for clinically assisted diagnosis. Although substantial progress has been made in MDD identification, extracting region of interest (ROI) features from functional brain networks remains underexplored. Furthermore, most studies rely on small‐scale resting‐state functional magnetic resonance imaging (rs‐fMRI) datasets, which limits the generalizability of their findings to large‐scale brain networks. To address these issues, we propose a novel graph embedding‐based feature selection classification framework (GEF‐FSC) to identify MDD through multi‐site rs‐fMRI data. The framework employs the node2vec algorithm to learn local and global functional connectivity (FC) features of ROIs via flexible random walks, capturing structural information in functional brain networks. Random Forest is then applied for feature selection on the learned embedding features, followed by classification using an ensemble classifier. This approach captures complex, higher‐order structural information between ROIs and retains important features, enhancing classification accuracy by minimizing redundancy in high‐dimensional FC features. Evaluated on the REST‐meta‐MDD dataset, our framework achieved 81.65% accuracy under the Dosenbach template and 75.30% under the AAL atlas. Comparative experiments with eight benchmark methods and six state‐of‐the‐art classifiers demonstrated superior accuracy, sensitivity, specificity, and F1‐score. Interpretability analysis highlighted key brain regions and networks consistent with previous findings. The GEF‐FSC framework effectively classifies MDD and identifies key brain regions and networks associated with the disorder, emphasizing the importance of higher‐order structural information in improving diagnostic accuracy.

Abstract Background Accumulating evidence indicates a high prevalence of major depressive disorder (MDD) in patients with chronic inflammatory diseases (1-6), suggesting a causal association between MDD and inflammatory processes. Moreover, functional changes at the protein level are associated with systems-level alterations in the neural circuits underlying specific pathological behaviors (7,8). However, direct in vivo evidence demonstrating a link between neuroinflammation and neural systems-level alterations in MDD is lacking. Aims & Objectives We aimed to investigate changes in cerebral translocator protein (TSPO) availability and the functional connectivity (FC) affected by these changes in treatment-naïve young adult patients with MDD using positron emission tomography (PET) imaging with the TSPO ligand [11C]PK11195 and resting-state functional magnetic resonance imaging (rs-fMRI). Method Twenty treatment-naïve young adult MDD patients without comorbidity and twenty-six healthy controls (HCs) underwent [11C]PK11195 PET and 3-Tesla MRI. For TSPO availability, we quantified [11C]PK11195 binding potential (BPND) using a reference tissue model (9) based on the supervised cluster analysis (SVCA4) algorithm (10). Voxel-based between-group comparisons of [11C]PK11195 BPND were performed using a two-tailed two-sample t-test. The relationships between [11C]PK11195 BPND and clinical variables were investigated using voxel-based regression analysis. Between-group differences in the [11C]PK11195 BPND seed-based FC were investigated using a two-tailed two-sample t-test. The correlations between [11C]PK11195 BPND seed-based FC and clinical variables were examined using voxel-based regression analysis. Results The MDD group had significantly higher [11C]PK11195 BPND in the left anterior cingulate cortex, left postcentral gyrus, bilateral superior temporal gyrus, and bilateral middle and inferior frontal gyri (IFG) compared to the HC group (uncorrected p < 0.001). In the MDD group, the total Beck Depression Inventory (BDI) score was positively correlated with [11C]PK11195 BPND in the pons, and the total 17-item Hamilton Rating Scale for Depression (HAMD-17) score was positively correlated with [11C]PK11195 BPND in the right pulvinar and midbrain (uncorrected p < 0.01). FC analysis showed that the FC from right IFG seed to the right precuneus was significantly increased in the MDD group compared to the HC group (uncorrected p < 0.0001). In the MDD group, the higher the BDI score, the greater the FC from the pons seed to the right supramarginal gyrus (SMG), and the higher HAMD-17 score, the greater FC from the midbrain seed to the left SMG and left IFG (uncorrected p < 0.001). Discussion & Conclusions Using a multimodal imaging, this study provides important information on the integrated molecular and neural systems-level changes associated with neuroinflammation in MDD. Notably, our results suggest the possibility of alterations in TSPO binding and associated FC perturbations in the pons and midbrain, key brain regions involved in the serotonergic system, in relation to depressive symptom severity in MDD. Further multimodal imaging studies with double-tracer PET imaging using [11C]PK11195 and serotonergic ligands are needed to clarify the link between neuroinflammation, serotonergic changes, and their significance relating to systems-level perturbations in MDD.

This study introduces DepressionGraph, a graph-based deep learning framework designed for accurate diagnosis of Major Depressive Disorder (MDD) using resting-state functional Magnetic Resonance Imaging (rs-fMRI). While prior studies have explored spatio-temporal Graph Neural Networks (GNNs), DepressionGraph distinguishes itself by implementing a two-channel architecture—Graph Convolutional Network (GCN) for spatial connectivity and Temporal GCN (T-GCN) for dynamic temporal analysis—tailored specifically to MDD pathology. Unlike earlier approaches, this framework explicitly integrates both static and dynamic patterns from functional connectivity graphs to improve diagnostic granularity. The model is trained and evaluated on publicly available rs-fMRI datasets from the REST-meta-MDD Consortium, employing a stratified 10-fold cross-validation and a separate hold-out test set to ensure generalizability. Graphs are constructed using Pearson correlation-based connectivity across 116 anatomical regions, and classification is performed via fused embeddings from the dual channels. The results demonstrate that DepressionGraph achieves 88.4% accuracy, 86.9% sensitivity, 89.6% specificity, and an AUC of 0.93, outperforming traditional models (SVM, Random Forest) and 2D CNN baselines, thus ensuring a fair comparison by aligning data dimensionality. Additionally, attention maps from the spatial channel provide interpretability by highlighting clinically relevant brain regions. DepressionGraph represents a robust, interpretable, and clinically viable advancement for non-invasive MDD diagnosis, with potential extensions to multimodal imaging and broader neuropsychiatric disorders.

Major Depressive Disorder (MDD) is among the most prevalent and debilitating mental health conditions, demanding accurate and scalable predictive tools to support early intervention and prevention. In this study, we propose a novel Local-to-Global Graph Neural Network (LG-GNN) framework specifically designed to enhance the prediction of MDD from resting-state fMRI (rs-fMRI) data. The model integrates two complementary components: a Local-GNN, which extracts subject-specific features from regional brain connectivity, and a Global Subject-GNN, which captures inter-subject relationships by incorporating non-imaging data. This multi-scale approach enables both personalized and population-level insights into MDD. Comparative analyses identified GraphSAGE as the optimal architecture for the Global Subject-GNN and a custom GCN with Self-Attention-Based Pooling (SABP) as the best-performing Local-GNN. The final LG-GNN model significantly outperformed traditional machine learning and graph-based baselines, demonstrating its potential for MDD prediction. Beyond classification, the framework offers a scalable and interpretable approach for implementing personalized, digitalized, and data-driven prevention strategies in at-risk populations.

Major Depressive Disorder (MDD) is a widespread psychiatric condition that affects a significant portion of the global population. The classification and diagnosis of MDD is crucial for effective treatment. Traditional methods, based on clinical assessment, are subjective and rely on healthcare professionals' expertise. Recently, there's growing interest in using Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to objectively understand MDD's neurobiology, complementing traditional diagnostics. The posterior cingulate cortex (PCC) is a pivotal brain region implicated in MDD which could be used to identify MDD from healthy controls. Thus, this study presents an intelligent approach based on rs-fMRI data to enhance the classification of MDD. Original rs-fMRI data were collected from a cohort of 430 participants, comprising 197 patients and 233 healthy controls. Subsequently, the data underwent preprocessing using DPARSF, and the amplitudes of low-frequency fluctuation values were computed to reduce data dimensionality and feature count. Then data associated with the PCC were extracted. After eliminating redundant features, various types of Support Vector Machines (SVMs) were employed as classifiers for intelligent categorization. Ultimately, we compared the performance of each algorithm, along with its respective optimal classifier, based on classification accuracy, true positive rate, and the area under the receiver operating characteristic curve (AUC-ROC). Upon analyzing the comparison results, we determined that the Random Forest (RF) algorithm, in conjunction with a sophisticated Gaussian SVM classifier, demonstrated the highest performance. Remarkably, this combination achieved a classification accuracy of 81.9 % and a true positive rate of 92.9 %. In conclusion, our study improves the classification of MDD by supplementing traditional methods with rs-fMRI and machine learning techniques, offering deeper neurobiological insights and aiding accuracy, while emphasizing its role as an adjunct to clinical assessment.

BACKGROUND The current diagnosis of major depressive disorder (MDD) is mainly based on the patient's self-report and clinical symptoms. Machine learning methods are used to identify MDD using resting-state functional magnetic resonance imaging (rs-fMRI) data. However, due to large site differences in multisite rs-fMRI data and the difficulty of sample collection, most of the current machine learning studies use small sample sizes of rs-fMRI datasets to detect the alterations of functional connectivity (FC) or network attribute (NA), which may affect the reliability of the experimental results. METHODS Multisite rs-fMRI data were used to increase the size of the sample, and then we extracted the functional connectivity (FC) and network attribute (NA) features from 1611 rs-fMRI data (832 patients with MDD (MDDs) and 779 healthy controls (HCs)). ComBat algorithm was used to harmonize the data variances caused by the multisite effect, and multivariate linear regression was used to remove age and sex covariates. Two-sample t-test and wrapper-based feature selection methods (support vector machine recursive feature elimination with cross-validation (SVM-RFECV) and LightGBM's "feature_importances_" function) were used to select important features. The Shapley additive explanations (SHAP) method was used to assign the contribution of features to the best classification effect model. RESULTS The best result was obtained from the LinearSVM model trained with the 136 important features selected by SVMRFE-CV. In the nested five-fold cross-validation (consisting of an outer and an inner loop of five-fold cross-validation) of 1611 data, the model achieved the accuracy, sensitivity, and specificity of 68.90%, 71.75%, and 65.84%, respectively. The 136 important features were tested in a small dataset and obtained excellent classification results after balancing the ratio between patients with depression and HCs. CONCLUSIONS The combined use of FC and NA features is effective for classifying MDDs and HCs. The important FC and NA features extracted from the large sample dataset have some generalization performance and may be used as a reference for the altered brain functional connectivity networks in MDD.

Background Childhood maltreatment (CM) is increasingly recognized as a significant risk factor for major depressive disorder (MDD), yet the neural mechanisms underlying the connection between CM and depression are not fully understood. This study aims to deepen our understanding of this relationship through neuroimaging, exploring how CM correlates with depression. Methods The study included 56 MDD patients (33 with CM experiences and 23 without) and 23 healthy controls. Participants were assessed for depression severity, CM experiences, and underwent resting-state functional MRI scans. Independent Component Analysis was used to examine differences in functional connectivity (FC) within the Default Mode Network (DMN) among the groups. Results MDD patients with CM experiences exhibited significantly stronger functional connectivity in the left Superior Frontal Gyrus (SFG) and right Anterior Cingulate Cortex (ACC) within the DMN compared to both MDD patients without CM experiences and healthy controls. FC in these regions positively correlated with Childhood Trauma Questionnaire scores. Receiver Operating Characteristic (ROC) curve analysis underscored the diagnostic value of FC in the SFG and ACC for identifying MDD related to CM. Additionally, MDD patients with CM experiences showed markedly reduced FC in the left medial Prefrontal Cortex (mPFC) relative to MDD patients without CM experiences, correlating negatively with Childhood Trauma Questionnaire scores. Conclusion Our findings suggest that increased FC in the ACC and SFG within the DMN is associated with CM in MDD patients. This enhanced connectivity in these brain regions is key to understanding the predisposition to depression related to CM.

BACKGROUND Major depressive disorder (MDD) has a high rate of recurrence. Identifying patients with recurrent MDD is advantageous in adopting prevention strategies to reduce the disabling effects of depression. METHOD We propose a novel feature extraction method that includes dynamic temporal information, and inputs the extracted features into a graph convolutional network (GCN) to achieve classification of recurrent MDD. We extract the average time series using an atlas from resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation was calculated between brain region sequences at each time point, representing the functional connectivity at each time point. The connectivity is used as the adjacency matrix and the brain region sequences as node features for a GCN model to classify recurrent MDD. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to analyze the contribution of different brain regions to the model. Brain regions making greater contribution to classification were considered to be the regions with altered brain function in recurrent MDD. RESULT We achieved a classification accuracy of 75.8 % for recurrent MDD on the multi-site dataset, the Rest-meta-MDD. The brain regions closely related to recurrent MDD have been identified. LIMITATION The pre-processing stage may affect the final classification performance and harmonizing site differences may improve the classification performance. CONCLUSION The experimental results demonstrate that the proposed method can effectively classify recurrent MDD and extract dynamic changes of brain activity patterns in recurrent depression.

OBJECTIVE This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD). METHODS A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening. RESULTS The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models. CONCLUSION The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD.

Major depressive disorder (MDD) is a serious, complex psychiatric condition that affects millions of people worldwide. Early diagnosis and biomarker identification are critical for personalized treatment and effective disease monitoring. While resting-state functional magnetic resonance imaging (rs-fMRI) combined with deep learning has facilitated MDD prediction, existing methods often overlook the dynamic temporal characteristics of blood oxygen level-dependent (BOLD) signals and ignore the strength of inter-regional connections, resulting in brain region updates devoid of biological specificity. To this end, a functional-dynamic synaptic graph neural network (FDSyn-GNN) is proposed, which integrates a bidirectional gated recurrent unit (Bi-GRU) timestamp encoding (BGTE) module for modeling dynamic BOLD signals and a synaptic graph Transformer (SGT) module for connection-aware brain region updates. FDSyn-GNN is validated on two large-scale MDD datasets collected across multiple sites, where it outperforms 12 state-of-the-art (SOTA) baseline methods. In addition, extensive ablation and interpretability analyses highlight its potential for biomarker discovery, offering insights into the neural mechanisms underlying MDD. The code is publicly available at https://github.com/ZHChen-294/FDSyn-GNN.

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide with high recurrence rate. Identifying MDD patients, particularly those with recurrent episodes with resting‐state fMRI, may reveal the relationship between MDD and brain function. We proposed a Transformer‐Encoder model, which utilized functional connectivity extracted from large‐scale multisite rs‐fMRI datasets to classify MDD and HC. The model discarded the Transformer's Decoder part, reducing the model's complexity and decreasing the number of parameters to adapt to the limited sample size and it does not require a complex feature selection process and achieves end‐to‐end classification. Additionally, our model is suitable for classifying data combined from multiple brain atlases and has an optional unsupervised pre‐training module to acquire optimal initial parameters and speed up the training process. The model's performance was tested on a large‐scale multisite dataset and identified brain regions affected by MDD using the Grad‐CAM method. After conducting five‐fold cross‐validation, our model achieved an average classification accuracy of 68.61% on a dataset consisting of 1611 samples. For the selected recurrent MDD dataset, the model reached an average classification accuracy of 78.11%. Abnormalities were detected in the frontal gyri and cerebral cortex of MDD patients in both datasets. Furthermore, the identified brain regions in the recurrent MDD dataset generally exhibited a higher contribution to the model's performance.

When common software packages (CONN and SPM) are used to process fMRI, results such as functional connectivity measures can substantially differ depending on the versions of the packages used and the tools used to convert image formats such as DICOM to NIFTI. The significance of these differences are illustrated within the context of a realistic research application: finding moderators of antidepressant response from a large psychiatric study of 288 major depressive disorder (MDD) patients. Significant differences in functional connectivity measurements and discrepancies in derived moderators were found between nearly all software configurations. These results should encourage researchers to be vigilant of software versions during fMRI preprocessing, to maintain consistency throughout each project, and to carefully report versions to facilitate reproducibility.

Background Major depressive disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, diminished interest in pleasurable activities, and anhedonia. Some patients with depression experience high levels of anxiety, complicating clinical treatment. However, the underlying pathological mechanisms remain unclear. Methods The sample comprised 178 participants, including 73 MDD with high anxiety symptom subjects, 55 MDD with low anxiety symptom, and 50 healthy controls registered from multiple sites based on the REST-meta-MDD Project in China. Resting-state functional magnetic resonance imaging (rs-fMRI) data were recorded. Large-scale static and dynamic functional connectivity analyses were conducted to identify specific brain connectivity distinguishing MDD with low and high anxiety symptoms. Results While MDD patients with high and low anxiety symptoms exhibit overlapping alterations in dynamic functional connectivity between the auditory cortex and nodes of the salience network, their distinct clinical profiles may be associated with differential functional connectivity patterns between the components of the default mode network (DMN) and the visual network (VN), as well as between the components of the basal ganglia network (BGN) and VN. Conclusion The VN–DMN–BGN functional circuit may help elucidate the underlying pathological mechanisms associated with varying levels of anxiety in depressive disorders. Understanding this neural correlation could contribute to the development of targeted therapeutic strategies for MDD.

Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for psychiatric disorders. Unfortunately, no widely accepted biomarkers are available at present, partially because of the large variety of analysis pipelines for their development. In this study, we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD). We explored combinations of options in four sub-processes of the analysis pipelines: six types of brain parcellation, four types of functional connectivity (FC) estimations, three types of site-difference harmonization, and five types of machine-learning methods. A total of 360 different MDD classification biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four sites) as the discovery dataset, and datasets from other projects acquired with heterogeneous protocols (449 participants from four sites) were used for independent validation. We repeated the procedure after swapping the roles of the two datasets to identify superior pipelines, regardless of the discovery dataset. The classification results of the top 10 biomarkers showed high similarity, and weight similarity was observed between eight of the biomarkers, except for two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other psychiatric disorders (autism spectrum disorder and schizophrenia), and eight of the biomarkers exhibited sufficient classification performance for both disorders. Our results will be useful for establishing a standardized pipeline for classification biomarkers.

BACKGROUND Major depressive disorder (MDD) has been diagnosed through subjective and inconsistent clinical assessments. Resting-state functional magnetic resonance imaging (rs-fMRI) with connectivity analysis has been valuable for identifying neural correlates of patients with MDD, yet most studies rely on single-site and small sample sizes. METHODS This study utilized large-scale, multi-site rs-fMRI data from the Rest-meta-MDD consortium to assess effective connectivity in patients with MDD and its subtypes, i.e., drug-naïve first-episode (FEDN), recurrent (RMDD), and medicated MDD (MMDD) subtypes. To mitigate site-related variability, the ComBat algorithm was applied, and multivariate linear regression was used to control for age and gender effects. A random forest classification model was developed to identify the most predictive features. Nested five-fold cross-validation was used to assess model performance. RESULTS The model effectively distinguished FEDN subtype from healthy controls (HC) group, achieving 90.13% accuracy and 96.41% AUC. However, classification performance for RMDD vs. FEDN and MMDD vs. FEDN was lower, suggesting that differences between the subtypes were less pronounced than differences between the patients with MDD and the HC group. Patients with RMDD exhibited more extensive connectivity abnormalities in the frontal-limbic system and default mode network than the patients with FEDN, implying heightened rumination. Additionally, treatment with medication appeared to partially modulate the aberrant connectivity, steering it toward normalization. CONCLUSION This study showed altered brain connectivity in patients with MDD and its subtypes, which could be classified with machine learning models with robust performance. Abnormal connectivity could be the potential neural correlates for the presenting symptoms of patients with MDD. These findings provide novel insights into the neural pathogenesis of patients with MDD.

BACKGROUND Major Depressive Disorder (MDD) diagnosis mainly relies on subjective self-reporting and clinical assessments. Resting-state functional magnetic resonance imaging (rs-fMRI) and its analysis of Effective Connectivity (EC) offer a quantitative approach to understand the directional interactions between brain regions, presenting a potential objective method for MDD classification. METHODS Granger causality analysis was used to extract EC features from a large, multi-site rs-fMRI dataset of MDD patients. The ComBat algorithm was applied to adjust for site differences, while multivariate linear regression was employed to control for age and sex differences. Discriminative EC features for MDD were identified using two-sample t-tests and model-based feature selection, with the LightGBM algorithm being used for classification. The performance and generalizability of the model was evaluated using nested five-fold cross-validation and tested for generalizability on an independent dataset. RESULTS Ninety-seven EC features belonging to the cerebellum and front-temporal regions were identified as highly discriminative for MDD. The classification model using these features achieved an accuracy of 94.35 %, with a sensitivity of 93.52 % and specificity of 95.25 % in cross-validation. Generalization of the model to an independent dataset resulted in an accuracy of 94.74 %, sensitivity of 90.59 %, and specificity of 96.75 %. CONCLUSION The study demonstrates that EC features from rs-fMRI can effectively discriminate MDD from healthy controls, suggesting that EC analysis could be a valuable tool in assisting the clinical diagnosis of MDD. This method shows promise in enhancing the objectivity of MDD diagnosis through the use of neuroimaging biomarkers.

OBJECTIVE Recent functional connectivity (FC) studies have proved the potential value of resting-state functional magnetic resonance imaging (rs-fMRI) in the study of major depressive disorder (MDD). This study investigates alterations of amplitude of low-frequency fluctuations (ALFF) in first-episode major depressive disorder (FED) using rs-fMRI, and then explore the abnormalities of FC. METHODS A total of 66 FED patients and 66 healthy controls were recruited for this study. The amplitude of low frequency fluctuations (ALFF) method was employed to assess changes in brain activity across the entire brain in both groups. Subsequently, the identified brain regions were used as seeds to examine whole-brain functional connectivity (FC) changes. Additionally, we conducted partial correlation analyses to investigate the relationship between functional connectivity values in FED patients and clinical variables, such as the 17-item Hamilton Depression Rating Scale (HAMD-17) scores and illness duration. RESULTS FED patients exhibited increased ALFF values in the right fusiform and the right parahippocampal gyrus compared to controls (P < 0.05, GRF correction). Enhanced FC was observed between the right parahippocampal gyrus and the right precentral gyrus, with reduced connectivity to the brainstem (P < 0.05, GRF correction). The FC values of the right parahippocampal gyrus and right precentral gyrus positively correlated with the HAMD-17 scores (r = 0.314, P = 0.012). CONCLUSION Abnormal ALFF and FC in several brain regions of FED patients suggest these areas could serve as biological markers for identifying FED.

Why some individuals are resilient to trauma while others develop psychopathology remains a baffling question in mental health research. Trauma-related conditions like post-traumatic stress disorder (PTSD), major depressive disorder (MDD), and anxiety disorders affect millions worldwide, emphasizing the need to understand the neural mechanisms that underlie these divergent outcomes. Through the use of ultra-high field (UHF) 7 T imaging, this study sought to investigate how thalamic functional connectivity differentiates resilience from vulnerability in trauma-exposed individuals. To that end, UHF 7 T resting-state functional magnetic resonance imaging (rs-fMRI) was applied to a group of 46 refugees from the Levant region, including 23 symptomatic (PTSD, MDD, or anxiety disorders) and 23 asymptomatic participants. Using the CONN toolbox, we conducted seed-to-voxel analyses focused on the thalamic subregions defined by the Human Brainnetome Atlas. Our results revealed significant connectivity alterations in the right medial prefrontal thalamus (mPFtha), the lateral prefrontal thalamus (lPFtha), and the occipital thalamus (Otha). Symptomatic individuals exhibited hyperconnectivity between the thalamic subregions and the somatosensory, visual, and cerebellar networks, along with reduced inter-thalamic connectivity, suggesting emotional dysregulation and hypervigilance. In contrast, asymptomatic participants displayed increased inter-thalamic connectivity and hypoconnectivity with these networks, reflecting efficient sensory integration and emotion regulation. Reduced inter-thalamic connectivity was found to correlate with lower resilience, underscoring the importance of effective thalamic communication for emotional stability. Taken together, our findings suggest that thalamic dysregulation contributes to vulnerability, while increased inter-thalamic connectivity fosters resilience through better sensory and emotion regulation. Thus, this study affords valuable insights into potential neural targets for interventions, which may help enhance resilience in trauma-exposed populations.

Abstract Background Resting state functional connectivity analysis has a potential to unearth the putative neuronal underpinnings of various disorders of the brain. Major depressive disorder (MDD) has been regarded as a disorder arising out alterations in functional networks in the brain. Aims & Objectives There is paucity of literature on resting state functional MRI (Rs-fMRI) in major depressive disorder (MDD), especially from the Indian subcontinent. The purpose of our study was to elucidate the differences in Rs-fMRI connectivity between MDD patients and age and gender matched healthy controls (HC). Method In this prospective single-institute based study, the patients were recruited consecutively based on Hamilton depression rating scale (HAM-D). Age and gender matched healthy controls were also recruited. Rs-fMRI and anatomical MRI images were acquired for all the subjects (MDD and HC group) and subsequent analysis was done using the CONN toolbox. A total of 49 subjects were included in the final analysis (MDD = 28 patients, HC = 21 healthy controls). HAM-D score was noted to be 24.4±4.8 in the MDD group. There was no significant difference between MDD and HC groups as far as age, gender, employment status and level of education is concerned. Region-of-interest based analysis of Rs-fMRI data showed a significantly lower connectivity between the left insula and left nucleus accumbens and between left paracingulate gyrus and bilateral posterior middle temporal gyri in MDD group as compared to HC group. Results A total of 49 subjects were included in the final analysis (MDD = 28 patients, HC = 21 healthy controls). HAM-D score was noted to be 24.4±4.8 in the MDD group. There was no significant difference between MDD and HC groups as far as age, gender, employment status and level of education is concerned. Region-of-interest based analysis of Rs-fMRI data showed a significantly lower connectivity between the left insula and left nucleus accumbens and between left paracingulate gyrus and bilateral posterior middle temporal gyri in MDD group as compared to HC group. Discussion & Conclusions There is reduced connectivity between certain key regions of the brain in MDD patients, i.e., between the left insular cortex and left nucleus accumbens and between left paracingulate gyrus and bilateral posterior middle temporal gyrus. These findings could explain the basis of clinical features of MDD such as anhedonia, rumination of thoughts, reduced visuo-spatial comprehension, reduced language function and response to external stimuli.

Somatic symptoms are common in adolescent major depressive disorder (MDD) and related to severity of depression and clinical outcomes. However, the neurological mechanism of somatic symptoms in adolescent MDD remains unknown. In this study, we aimed to explore the functional alterations of intrinsic brain local connectivity in adolescent MDD with somatic symptoms based on static and dynamic reginal homogeneity (ReHo). This study included 50 first-episode, drug naïve adolescent MDD patients and 34 healthy controls (HCs) matched for age, gender and years of education. Patients were categorized into somatic depression (SD) group (n = 21) and non-somatic depression (NSD) group (n = 29) based on the presence of somatic symptoms or not. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI), and static and dynamic ReHo were calculated and compared among SD, NSD and HC groups. Correlation analysis was performed to evaluate the relationship between altered ReHo values and severity of clinical symptoms. Adolescent MDD patients with somatic symptoms showed higher total scores of the 17-items Hamilton Depression Scale (HAMD-17). Moreover, increased static ReHo in left inferior parietal gyrus (IPG), left superior parietal gyrus (SPG) and left triangular part of inferior frontal gyrus (IFGtriang) were observed in SD group compared with NSD group. The SD group also exhibited decreased dynamic ReHo in bilateral IPG, bilateral SPG, and left IFGtriang. Moreover, there were significant correlations between static and dynamic ReHo values in these abnormal brain regions and the weight factor scores of HAMD-17. Our findings suggested that there may be abnormal patterns of functional local connectivity in SPG, IPG and IFGtriang in adolescent MDD patients with somatic symptoms, enriching the knowledge of neurological mechanism concerning somatic symptoms in adolescent MDD.

Background/Objectives: Up to 75% of patients with major depressive disorder (MDD) exhibit persistent anhedonia symptoms related to abnormalities in the positive valence system. Cumulative evidence points to brain dysfunction in the reward system (RS), including in the ventral striatum, in patients with MDD with anhedonia. This study aims to evaluate the safety and efficacy of a novel neurofeedback (NF) device (termed Prism) which incorporates the EEG–FRI-Pattern biomarker of the reward system (RS-EFP) for use in self-neuromodulation training (RS-EFP-NF) for alleviating depression in patients with MDD with anhedonia. Methods: A total of 49 adults (age range: M = 39.9 ± 11.03) with a DSM-5 diagnosis of MDD with anhedonia (per a SHAPS-C score ≥ 25) were screened for the administration of ten sessions of RS-EFP-NF twice a week on nonconsecutive days. Depression and anhedonia severity was assessed, respectively, by HDRS-17 and SHAPS-C at baseline, midway, and treatment end. Results: A total of 34 patients (77%) completed the protocol and were included in the analyses. No device-related adverse events were serious or required treatment. Depression symptoms were reduced at end of treatment as indicated by the HDRS-17, with a reduction of eight points on average (95% CI: −10.5 to −5.41, p < 0.0001), a clinical improvement rate of 78.47%, and a remission rate of 32.25%. Anhedonia, as indicated by the SHAPS-C score, was diminished, showing an average reduction of 6.3 points (95% CI: −8.51 to −4.14, p < 0.0001). Conclusions: Self-neuromodulation using RS-EFP-NF is a promising and safe treatment for MDD with anhedonia. The intervention demonstrates substantial clinical effects on both depression and anhedonia symptoms, with high patient acceptability and retention. Prism may address a critical mechanism-driven treatment gap for anhedonia that often persists despite conventional therapies. Larger controlled implementation, efficacy, and dosing studies are warranted.

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) is effective in regulating mood and high-level cognition in patients with major depressive disorder (MDD). This study aimed to investigate the efficacy of taVNS treatment in patients with MDD and an altered brain topological organization of functional networks. Methods: Nineteen patients with MDD were enrolled in this study. Patients with MDD underwent 4 weeks of taVNS treatments; resting-state functional magnetic resonance imaging (rs-fMRI) data of the patients were collected before and after taVNS treatment. The graph theory method and network-based statistics (NBS) analysis were used to detect abnormal topological organizations of functional networks in patients with MDD before and after taVNS treatment. A correlation analysis was performed to characterize the relationship between altered network properties and neuropsychological scores. Results: After 4 weeks of taVNS treatment, patients with MDD had increased global efficiency and decreased characteristic path length (Lp). Additionally, patients with MDD exhibited increased nodal efficiency (NE) and degree centrality (DC) in the left angular gyrus. NBS results showed that patients with MDD exhibited reduced connectivity between default mode network (DMN)–frontoparietal network (FPN), DMN–cingulo-opercular network (CON), and FPN–CON. Furthermore, changes in Lp and DC were correlated with changes in Hamilton depression scores. Conclusions: These findings demonstrated that taVNS may be an effective method for reducing the severity of depressive symptoms in patients with MDD, mainly through modulating the brain’s topological organization. Our study may offer insights into the underlying neural mechanism of taVNS treatment in patients with MDD.

BACKGROUND Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression. PURPOSE This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance. METHODS Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method. RESULTS The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy = 92.42%, sensitivity = 92.86%, specificity = 92.11%, precision = 89.66%, F1 = 91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression. CONCLUSIONS This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

The diagnosis and analysis of major depressive disorder (MDD) faces some intractable challenges such as dataset limitations and clinical variability. Resting-state functional magnetic resonance imaging (Rs-fMRI) can reflect the fluctuation data of brain activity in a resting state, which can find the interrelationships, functional connections, and network characteristics among brain regions of the patients. In this paper, a brain functional connectivity matrix is constructed using Pearson correlation based on the characteristics of multi-site Rs-fMRI data and brain atlas, and an adaptive propagation operator graph convolutional network (APO-GCN) model is designed. The APO-GCN model can automatically adjust the propagation operator in each hidden layer according to the data features to control the expressive power of the model. By adaptively learning effective information in the graph, this model significantly improves its ability to capture complex graph structural patterns. The experimental results on Rs-fMRI data from 1601 participants (830 MDD and 771 HC) and 16 sites of REST-meta-MDD project show that the APO-GCN achieved a classification accuracy of 93.8%, outperforming those of the state-of-the-art classifier methods. The classification process is driven by multiple significant brain regions, and our method further reveals functional connectivity abnormalities between these brain regions, which are important biomarkers of classification. It is worth noting that the brain regions identified by the classifier and the networks involved are consistent with existing research results, which suggest that the pathogenesis of depression may be related to dysfunction of multiple brain networks.

Objective Major depressive disorder (MDD) is associated with suicidal attempts (SAs) among adolescents, with suicide being the most common cause of mortality in this age group. This study explored the predictive utility of support vector machine (SVM)-based analyses of amplitude of low-frequency fluctuation (ALFF) results as a neuroimaging biomarker for aiding the diagnosis of MDD with SA in adolescents. Methods Resting-state functional magnetic resonance imaging (rs-fMRI) analyses of 71 first-episode, drug-naive adolescent MDD patients with SA and 54 healthy control individuals were conducted. ALFF and SVM methods were used to analyze the imaging data. Results Relative to healthy control individuals, adolescent MDD patients with a history of SAs showed reduced ALFF values in the bilateral medial superior frontal gyrus (mSFG) and bilateral precuneus. These lower ALFF values were also negatively correlated with child depression inventory (CDI) scores while reduced bilateral precuneus ALFF values were negatively correlated with Suicidal Ideation Questionnaire Junior (SIQ-JR) scores. SVM analyses showed that reduced ALFF values in the bilateral mSFG and bilateral precuneus had diagnostic accuracy levels of 76.8% (96/125) and 82.4% (103/125), respectively. Conclusion Adolescent MDD patients with a history of SA exhibited abnormal ALFF. The identified abnormalities in specific brain regions may be involved in the pathogenesis of this condition and may help identify at-risk adolescents. Specifically, reductions in the ALFF in the bilateral mSFG and bilateral precuneus may be indicative of MDD and SA in adolescent patients.

Background The amplitude of low-frequency fluctuation (ALFF) is a measure of spontaneous brain activity derived from resting-state functional magnetic resonance imaging (rs-fMRI). Previous research has suggested that abnormal ALFF values may be associated with major depressive disorder (MDD) and suicide attempts in adolescents. In this study, our aim was to investigate the differences in ALFF values between adolescent MDD patients with and without a history of suicide attempts, and to explore the potential utility of ALFF as a neuroimaging biomarker for aiding in the diagnosis and prediction of suicide attempts in this population. Methods The study included 34 adolescent depression patients with suicide attempts (SU group), 43 depression patients without suicide attempts (NSU group), and 36 healthy controls (HC group). Depression was diagnosed using a threshold score greater than 17 on the Hamilton Depression Rating Scale (HDRS). The rs-fMRI was employed to calculate zALFF values and compare differences among the groups. Associations between zALFF values in specific brain regions and clinical variables such as emotion regulation difficulties were explored using Pearson partial correlation analysis. Receiver-Operating Characteristics (ROC) analysis assessed the ability of mean zALFF values to differentiate between SU and NSU groups. Results Significant differences in zALFF values were observed in the left and right inferior temporal gyrus (l-ITG, r-ITG) and right fusiform gyrus (r-FG) among the three groups (GRF corrected). Both SU and NSU groups exhibited increased zALFF values in the inferior temporal gyrus compared to the HC group. Furthermore, the SU group showed significantly higher zALFF values in the l-ITG and r-FG compared to both the NSU group and the HC group. Partial correlation analysis revealed a negative correlation between zALFF values in the left superior and middle frontal gyrus (l-SFG, l-MFG) and the degree of emotional dysregulation in the SU group (R = −0.496, p = 0.003; R = −0.484, p = 0.005). Combining zALFF values from the l-ITG and r-FG achieved successful discrimination between depressed adolescents with and without suicide attempts (AUC = 0.855) with high sensitivity (86%) and specificity (71%). Conclusion Depressed adolescents with suicidal behavior exhibit unique neural activity patterns in the inferior temporal gyrus and fusiform gyrus. These findings highlight the potential utility of these specific brain regions as biomarkers for identifying suicide risk in depressed adolescents. Furthermore, associations between emotion dysregulation and activity in their frontal gyrus regions were observed. These findings provide preliminary yet pertinent insights into the pathophysiology of suicide in depressed adolescents.

Transcutaneous electrical cranial-auricular acupoint stimulation (TECAS) is an innovative, non-invasive therapy for major depressive disorder (MDD). However, its effectiveness and underlying neural mechanisms remain not fully understood. This study aimed to investigate the treatment response and neurological effects of TECAS compared to escitalopram, a commonly used depression medication, using resting-state functional magnetic resonance imaging (rs-fMRI). Fifty-one patients with mild-to-moderate MDD (34 in the TECAS group and 17 in the Escitalopram group) and 51 healthy controls (HCs) participated in the study. We employed the low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) methods to explore brain abnormalities in MDD patients and HCs. Additionally, seed-based functional connectivity (FC) analysis was conducted to examine altered brain networks before and after treatment.Compared to the HCs group, the MDD group exhibited lower ReHo and ALFF values in the right medial superior frontal gyrus (mSFG_R), indicating altered neural activity in this region. Furthermore, mSFG-based FC analysis revealed abnormal FC values in the right inferior occipital gyrus (IOG_R) and middle temporal gyrus (MTG) between after and before treatment in MDD patients. Interestingly, TECAS treatment was found to normalize these abnormal FC brain regions, suggesting its potential role in restoring neural connectivity in MDD patients. Notably, both TECAS and escitalopram demonstrated equivalent antidepressant efficacy, with both treatments showing modulatory effects on connectivity within the default mode network (DMN). The observed normalization of abnormal FC regions, including mSFG_R, IOG_R, and MTG, all belong to the DMN. In conclusion, this study sheds light on the neurological effects and treatment response of TECAS in MDD, highlighting its potential as a non-invasive therapeutic option for depressed patients.

Background Although many studies have reported the biological basis of major depressive disorder (MDD), none have been put into practical use. Recently, we developed a generalizable brain network marker for MDD diagnoses (diagnostic marker) across multiple imaging sites using resting-state functional magnetic resonance imaging (rs-fMRI). We have planned this clinical trial to establish evidence for the practical applicability of this diagnostic marker as a medical device. In addition, we have developed generalizable brain network markers for MDD stratification (stratification markers), and the verification of these brain network markers is a secondary endpoint of this study. Methods This is a non-randomized, open-label study involving patients with MDD and healthy controls (HCs). We will prospectively acquire rs-fMRI data from 50 patients with MDD and 50 HCs and anterogradely verify whether our diagnostic marker can distinguish between patients with MDD and HCs. Furthermore, we will longitudinally obtain rs-fMRI and clinical data at baseline and 6 weeks later in 80 patients with MDD treated with escitalopram and verify whether it is possible to prospectively distinguish MDD subtypes that are expected to be effectively responsive to escitalopram using our stratification markers. Discussion In this study, we will confirm that sufficient accuracy of the diagnostic marker could be reproduced for data from a prospective clinical study. Using longitudinally obtained data, we will also examine whether the “brain network marker for MDD diagnosis” reflects treatment effects in patients with MDD and whether treatment effects can be predicted by “brain network markers for MDD stratification”. Data collected in this study will be extremely important for the clinical application of the brain network markers for MDD diagnosis and stratification. Trial registration Japan Registry of Clinical Trials ( jRCTs062220063 ). Registered 12/10/2022.

Major depressive disorder (MDD) is a severe mental disorder associated with high morbidity and mortality rates, which remains difficult to treat, as both resistance and recurrence rates are high. Repetitive transcranial magnetic stimulation (TMS) of the left dorsolateral prefrontal cortex (DLPFC) provides a safe and effective treatment for selected patients with treatment‐resistant MDD. Little is known about the mechanisms of action of TMS provided to the left DLPFC in MDD and we can currently not predict who will respond to this type of treatment, precluding effective patient selection. In order to shed some light on the mechanism of action, we applied single pulse TMS to the left DLPFC in 10 healthy participants using a unique TMS‐fMRI set‐up, in which we could record the direct effects of TMS. Stimulation of the DLPFC triggered activity in a number of connected brain regions, including the subgenual anterior cingulate cortex (sgACC) in four out of nine participants. The sgACC is of particular interest, because normalization of activity in this region has been associated with relief of depressive symptoms in MDD patients. This is the first direct evidence that TMS pulses delivered to the DLPFC can propagate to the sgACC. The propagation of TMS‐induced activity from the DLPFC to sgACC may be an accurate biomarker for rTMS efficacy. Further research is required to determine whether this method can contribute to the selection of patients with treatment resistant MDD who will respond to rTMS treatment.

No abstract available

Background Neurobiological mechanisms underlying the recurrence of major depressive disorder (MDD) at different ages are unclear, and this study used the regional homogeneity (ReHo) index to compare whether there are differences between early onset recurrent depression (EORD) and late onset recurrent depression (LORD). Methods Eighteen EORD patients, 18 LORD patients, 18 young healthy controls (HCs), and 18 older HCs were included in the rs-fMRI scans. ReHo observational metrics were used for image analysis and further correlation of differential brain regions with clinical symptoms was analyzed. Results ANOVA analysis revealed significant differences between the four groups in ReHo values in the prefrontal, parietal, temporal lobes, and insula. Compared with EORD, the LORD had higher ReHo in the right fusiform gyrus/right middle temporal gyrus, left middle temporal gyrus/left angular gyrus, and right middle temporal gyrus/right angular gyrus, and lower ReHo in the right inferior frontal gyrus/right insula and left superior temporal gyrus/left insula. Compared with young HCs, the EORD had higher ReHo in the right inferior frontal gyrus/right insula, left superior temporal gyrus/left insula, and left rolandic operculum gyrus/left superior temporal gyrus, and lower ReHo in the left inferior parietal lobule, right inferior parietal lobule, and left middle temporal gyrus/left angular gyrus. Compared with old HCs, the LORD had higher ReHo in the right fusiform gyrus/right middle temporal gyrus, right middle temporal gyrus/right angular gyrus, and left rolandic operculum gyrus/left superior temporal gyrus, and lower ReHo in the right inferior frontal gyrus/right insula. ReHo in the right inferior frontal gyrus/right insula of patients with LORD was negatively correlated with the severity of 17-item Hamilton Rating Scale for Depression (HAMD-17) scores (r = −0.5778, p = 0.0120). Conclusion Adult EORD and LORD patients of different ages have abnormal neuronal functional activity in some brain regions, with differences closely related to the default mode network (DMN) and the salience network (SN), and patients of each age group exhibit ReHo abnormalities relative to matched HCs. Clinical Trial Registration [http://www.chictr.org.cn/], [ChiCTR1800014277].

OBJECTIVE The resting-state functional magnetic resonance imaging (rs-fMRI) have been used to explore functional abnormality of the brain in MDD patients with suicidal ideation (SI). However, few studies reported the variability and concordance of alterations of rs-fMRI indices in MDD with SI. In this study, we aimed to explore the variability and concordance of alterations of rs-fMRI indices in MDD with SI. METHODS A sliding window analysis was performed among 36 MDD patients with SI, 66 MDD patients without SI (NSI), and 50 healthy controls (HCs). Furthermore, the correlation between voxel-wise concordance and cognitive function was examined in the SI group. RESULTS The SI group had a lower dynamics degree centrality (dDC) value than the NSI group in left inferior occipital gyrus, and a lower voxel mirrored homotopic connectivity (dVMHC) value than the NSI group in the right and left inferior occipital gyrus. The mean values of volume wise concordance of HCs group shown higher than SI group and NSI group. SI group revealed decreased voxel-wise concordance in right cerebellum, left fusiform gyrus, left lingual gyrus, right middle temporal gyrus, left postcentral gyrus, and right supplementary motor area compared to NSI group. Moreover, the voxel-wise concordance of left middle occipital gyrus was negatively correlated with verbal learning and memory and working memory in the SI group. LIMITATION This is a cross-sectional analysis, limiting causal inferences. CONCLUSIONS The abnormal voxel-wise concordance of left middle occipital gyrus could be useful in understanding the pathophysiology of MDD patients with SI.

Anxious depression is a common subtype of major depressive disorder (MDD) associated with adverse outcomes and severely impaired social function. It is important to clarify the underlying neurobiology of anxious depression to refine the diagnosis and stratify patients for therapy. Here we explored associations between anxiety and brain structure/function in MDD patients. A total of 260 MDD patients and 127 healthy controls underwent three-dimensional T1-weighted structural scanning and resting-state functional magnetic resonance imaging. Demographic data were collected from all participants. Differences in gray matter volume (GMV), (fractional) amplitude of low-frequency fluctuation ((f)ALFF), regional homogeneity (ReHo), and seed point-based functional connectivity were compared between anxious MDD patients, non-anxious MDD patients, and healthy controls. A random forest model was used to predict anxiety in MDD patients using neuroimaging features. Anxious MDD patients showed significant differences in GMV in the left middle temporal gyrus and ReHo in the right superior parietal gyrus and the left precuneus than HCs. Compared with non-anxious MDD patients, patients with anxious MDD showed significantly different GMV in the left inferior temporal gyrus, left superior temporal gyrus, left superior frontal gyrus (orbital part), and left dorsolateral superior frontal gyrus; fALFF in the left middle temporal gyrus; ReHo in the inferior temporal gyrus and the superior frontal gyrus (orbital part); and functional connectivity between the left superior temporal gyrus(temporal pole) and left medial superior frontal gyrus. A diagnostic predictive random forest model built using imaging features and validated by 10-fold cross-validation distinguished anxious from non-anxious MDD with an AUC of 0.802. Patients with anxious depression exhibit dysregulation of brain regions associated with emotion regulation, cognition, and decision-making, and our diagnostic model paves the way for more accurate, objective clinical diagnosis of anxious depression.

BACKGROUND Symptom-based diagnostic criteria of depression leads to notorious heterogeneity and subjectivity. METHODS The study was conducted in two stages at two sites: development of a neuroimaging-based subtyping and precise repetitive transcranial magnetic stimulation (rTMS) strategy for depression at Center 1 and its clinical application at Center 2. Center 1 identified depression subtypes and subtype-specific rTMS targets based on amplitude of low frequency fluctuation (ALFF) in a cohort of 238 major depressive disorder patients and 66 healthy controls (HC). Subtypes were identified using a Gaussian Mixture Model, and subtype-specific rTMS targets were selected based on dominant brain regions prominently differentiating depression subtypes from HC. Subsequently, one classifier was employed and 72 hospitalized, depressed youths at Center 2 received two-week precise rTMS. MRI and clinical assessments were obtained at baseline, midpoint, and treatment completion for evaluation. RESULTS Two neuroimaging subtypes of depression, archetypal and atypical depression, were identified based on distinct frontal-posterior functional imbalance patterns as measured by ALFF. The dorsomedial prefrontal cortex was identified as the rTMS target for archetypal depression, and the occipital cortex for atypical depression. Following precise rTMS, ALFF alterations were normalized in both archetypal and atypical depressed youths, corresponding with symptom response of 90.00% in archetypal depression and 70.73% in atypical depression. CONCLUSIONS A precision medicine framework for depression was developed based on objective neurobiomarkers and implemented with promising results, actualizing a subtyping-treatment-evaluation closed loop in depression. Future randomized controlled trials are warranted.

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No abstract available

Repetitive transcranial magnetic stimulation (rTMS) is a tool that can be used to administer treatment to neuropsychiatric disorders such as major depressive disorder (MDD). Though, the clinical efficacy is still rather modest. Overly general stimulation protocols that neither consider patient-specific depression symptomology nor individualized brain characteristics, such as anatomy, or structural and functional connections, may be the cause of the high inter- and intra-individual variability in rTMS clinical responses. Multimodal neuroimaging can provide the necessary insights into individual brain characteristics and can therefore be used to personalize rTMS parameters. Optimal coil positioning should include a three-step process: 1) identify the optimal (indirect) target area based on the exact symptom pattern of the patient; 2) derive the cortical (direct) target location based on functional and/or structural connectomes derived from functional and diffusion MRI data; 3) determine the ideal coil position by computational modeling, such that the electric field distribution overlaps with the cortical target. These TMS-induced electric field simulations, derived from anatomical and diffusion MRI data, can be further applied to compute optimal stimulation intensities. Besides MRI, EEG can provide complementary information regarding the ongoing brain oscillations. This information can be used to determine the optimal timing and frequency of the stimuli. The heightened benefits of these personalized stimulation approaches are logically reasoned, but speculative. Randomized clinical trials will be required to compare clinical responses from standard rTMS protocols to personalized protocols. Ultimately, an optimized clinical response may result from precision protocols derived from combinations of personalized stimulation parameters.

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls based on neuroimaging data. Drawing upon structural MRI data from a balanced sample of N = 1868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes (N = 4 to N = 150) from the population and showed a strong risk of misestimation. Specifically, for small sample sizes (N = 20), we observe accuracies of up to 95%. For medium sample sizes (N = 100) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance misestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets, which is readily available in most cases.

Highlights • Human connectome project examining adolescent anxiety and depression.• N = 225 planned, clinical and control adolescents, ages 14–17.• Description of clinical and functional imaging measures from first 140 participants.• Data publicly available via national institute of health data archive.

Highlights • Human Connectome Project examining adolescent anxiety and depression.• N = 225 planned, clinical and control adolescents, ages 14–17.• Description of acquisition protocols and quality assurance measurements.• Harmonized protocols with HCP projects related to anxiety and/or depression.• Data publicly available via National Institute of Mental Health Data Archive.

Background: The mechanism by which antidepressants normalizing aberrant resting-state functional connectivity (rsFC) in patients with major depressive disorder (MDD) is still a matter of debate. The current study aimed to investigate aberrant rsFC and whether antidepressants would restore the aberrant rsFC in patients with MDD. Methods: A total of 196 patients with MDD and 143 healthy controls (HCs) received the resting-state functional magnetic resonance imaging and clinical assessments at baseline. Patients with MDD received antidepressant treatment after baseline assessment and were re-scanned at the 6-month follow-up. Network-based statistics were employed to identify aberrant rsFC and rsFC changes in patients with MDD and to compare the rsFC differences between remitters and non-remitters. Results: We identified a significantly decreased sub-network and a significantly increased sub-network in MDD at baseline. Approximately half of the aberrant rsFC remained significantly different from HCs after 6-month treatment. Significant overlaps were found between baseline reduced sub-network and follow-up increased sub-network, and between baseline increased sub-network and follow-up decreased sub-network. Besides, rsFC at baseline and rsFC changes between baseline and follow-up in remitters were not different from non-remitters. Conclusions: Most aberrant rsFC in patients with MDD showed state-independence. Although antidepressants may modulate aberrant rsFC, they may not specifically target these aberrations to achieve therapeutic effects, with only a few having been directly linked to treatment efficacy.

No abstract available

Modern neuroimaging research has recognized that major depressive disorder (MDD) is a connectome disorder, characterized by altered functional connectivity across large-scale brain networks. However, the clinical heterogeneity, likely stemming from diverse neurobiological disturbances, complicates findings from standard group comparison methods. This variability has driven the search for MDD subtypes using objective neuroimaging markers. In this study, we sought to identify potential MDD subtypes from subject-level abnormalities in functional connectivity, leveraging a large multi-site dataset of resting-state MRI from 1276 MDD patients and 1104 matched healthy controls. Subject-level extreme functional connections, determined by comparing against normative ranges derived from healthy controls using tolerance intervals, were used to identify biological subtypes of MDD. We identified a set of extreme functional connections that were predominantly between the visual network and the frontoparietal network, the default mode network and the ventral attention network, with the key regions in the anterior cingulate cortex, bilateral orbitofrontal cortex, and supramarginal gyrus. In MDD patients, these extreme functional connections were linked to age of onset and reward-related processes. Using these features, we identified two subtypes with distinct patterns of functional connectivity abnormalities compared to healthy controls (p < 0.05, Bonferroni correction). When considering all patients together, no significant differences were found. These subtypes significantly enhanced case-control discriminability and showed strong internal discriminability between subtypes. Furthermore, the subtypes were reproducible across varying parameters, study sites, and in untreated patients. Our findings provide new insights into the taxonomy and have potential implications for both diagnosis and treatment of MDD.

No abstract available

Introduction The Attention Training Technique (ATT) is a psychotherapeutic intervention in Metacogntive Therapy (MCT) and aims at reducing maladaptive processes by strengthening attentional flexibility. ATT has demonstrated efficacy in treating depression on a clinical level. Here, we evaluated ATT at the neural level. We examined functional connectivity (FC) of the default mode network (DMN). Method 48 individuals diagnosed with Major Depressive Disorder (MDD) and 51 healthy controls (HC) participated in a resting-state (rs) functional magnetic resonance imaging (fMRI) experiment. The participants received either one week of ATT or a sham intervention. Rs-fMRI scans before and after treatment were compared using seed-to-voxel analysis. Results The 2x2x2 analysis did not reach significance. Nevertheless, a resting-state connectivity effect was found on the basis of a posttest at the second measurement time point in MDD. After one week, MDD patients who had received ATT intervention presented lower functional connectivity between the left posterior cingulate cortex (PCC) and the bilateral middle frontal gyrus (MFG) as well as between the right PCC and the left MFG compared to the MDD patients in the sham group. In HC we observed higher rsFC in spatially close but not the same brain regions under the same experimental condition. Conclusion We found a first hint of a change at the neural level on the basis of ATT. Whether the changes in rsFC found here indicate an improvement in the flexible shift of attentional focus due to ATT needs to be investigated in further research paradigms. Further experiments have to show whether this change in functional connectivity can be used as a specific outcome measure of ATT treatment.

BACKGROUND Major depressive disorder (MDD) is a leading cause of disability worldwide, with available treatments often showing limited efficacy. Recent research suggests targeting specific subtypes of depression and understanding the underlying brain mechanisms can improve treatment outcomes. This study investigates the potential of the potassium KCNQ (Kv7) channel opener ezogabine to modulate the resting-state functional connectivity (RSFC) of the brain's reward circuitry and alleviate depressive symptoms, including anhedonia, a core feature of MDD. METHODS A double-blind, randomized, placebo-controlled clinical trial in individuals aged 18 to 65 with MDD compared daily dosing with ezogabine (n=19) to placebo (n=21) for five weeks. Functional magnetic resonance imaging (fMRI) assessed RSFC of the brain's key reward regions (ventral caudate, nucleus accumbens) at baseline and post-treatment. Clinical symptoms were measured using the Snaith-Hamilton Pleasure Scale (SHAPS), Montgomery-Åsberg Depression Rating Scale (MADRS), and other clinical symptom scales. RESULTS Ezogabine significantly reduced RSFC between the reward seeds and the posterior cingulate cortex (PCC)/precuneus compared to placebo, which was associated with a reduction in depression severity. Improvements in anhedonia (SHAPS) and depressive symptoms (MADRS) with ezogabine compared to placebo were also associated with decreased connectivity between the reward seeds and mid/posterior cingulate regions (MCC, PCC, precuneus). CONCLUSIONS The findings suggest that ezogabine's antidepressant effects are mediated through modulation of striatal-mid/posterior cingulate connectivity, indicating a potential therapeutic mechanism for KCNQ-targeted drugs for MDD and anhedonia. Future studies should validate these results in larger trials. CLINICALTRIALS gov identifier: NCT03043560.

BACKGROUND Childhood trauma impacts brain development, increasing vulnerability to Major Depressive Disorder (MDD). The frontal-limbic network, essential for emotion regulation and stress response, is frequently implicated in MDD pathophysiology. This study examined whether childhood trauma is associated with alterations in resting-state functional connectivity (FC) within brain networks involved in emotion regulation in individuals with MDD and healthy controls (HCs). METHODS A cross-sectional study was conducted with 76 patients with MDD and 97 HCs (aged 19-64 years). Resting-state FC was assessed among 24 predefined regions of interest (ROIs) within brain networks associated within the fronto-limbic circuit. Childhood trauma was quantified using the Childhood Trauma Questionnaire (CTQ). ROI-to-ROI FC and global/regional brain network properties were evaluated. RESULTS The MDD group demonstrated significant reductions in frontal-limbic FC (e.g., left dorsolateral prefrontal cortex [dlPFC]-left hippocampus, left dlPFC-right pregenual anterior cingulate cortex [ACC], right pregenual ACC-right subgenual ACC). Brain network properties analysis revealed a negative correlation between global efficiency and emotional neglect severity, as well as positive correlations between local efficiency/clustering efficiency and total CTQ scores. Specifically, in the MDD group, the clustering coefficient in the ventromedial prefrontal cortex (vmPFC) was positively associated with emotional neglect, whereas in the HC group, total CTQ scores were positively correlated with the nodal degree of the right subgenual ACC. CONCLUSION Childhood trauma is associated with significant alterations in resting-state FC and brain network properties within the frontal-limbic network in individuals with MDD. These findings highlight a potential neural pathway through which early adversity contributes to MDD pathophysiology.

Background Fibromyalgia (FM) and major depressive disorder (MDD) frequently co-occur. This study investigated whether the differences in resting-state functional connectivity (rs-FC) of the emotion- and pain-related brain networks may differentiate FM patients with and without MDD and if these differences are associated with the severity of clinical symptoms, quality of life, recurrent depression, pain catastrophizing, and antidepressant use. Methods In this study, the authors recruited a sample of 37 females classified as FM with MDD (FM + MDD, n = 23) or FM without MDD (FM-only, n = 14) based on the International Neuropsychiatric Interview. The severity of depressive symptoms was measured using the Beck Depression Inventory-II (BDI-II). Results Age-adjusted rs-FC correlated significantly with BDI-II scores. FM + MDD patients showed increased rs-FC between the right ventral insula and left middle frontal gyrus (MFG) (χ²(1) = 5.54, P = 0.019, effect size [ES] = 0.87), and decreased rs-FC between the caudal hippocampus and middle cingulate cortex (χ²(1) = 6.65, P < 0.001, ES = 0.90). Increased rs-FC between the ventral insula and MFG was positively associated with recurrent MDD and pain catastrophizing, and negatively with FM-related quality of life. The connection between the left MFG and the right posterior parietal thalamus is associated with recurrent MDD and pain catastrophizing. Conclusions Distinct neurofunctional patterns in regions related to emotional regulation and cognitive control of pain—marked by increased inter-hemispheric frontal and decreased intra-hemispheric limbic-cingulate connectivity—may serve as potential biomarkers to distinguish FM patients with comorbid MDD from those without.

Abstract Major Depressive Disorder (MDD) is a highly prevalent mental health condition characterized by symptoms including anhedonia, which is defined as diminished pleasure, and impulsivity, which has been linked to increased substance abuse, self-harm and suicidal tendencies. Both anhedonia and impulsivity have been contributed to alterations in reward system function, with the striatum being a central hub involved in processing and regulating reward-related information. Selective serotonin reuptake inhibitors are currently the first-line treatment for patients with MDD. In recent years, there has been growing interest in the potential therapeutic benefits of psilocybin for the treatment of depression and there is mounting evidence suggesting that psychedelics may modulate the reward system.Here, we aim to employ seed-voxel analysis on resting-state functional magnetic resonance imaging (fMRI) data to investigate the effects of escitalopram and psychedelic therapy on the reward pathways within the associative, limbic and sensorimotor striatal subdivisions as well as the amygdala and hippocampus in patients with MDD. In this secondary analysis of a trial, 45 MDD patients were randomly assigned to either psilocybin therapy (n=24) or escitalopram (n=21) treatment groups. Participants underwent a 10 min long resting- state functional magnetic resonance imaging scan both before and after a 6-week intervention. Analyses examined the connectivity of three striatal networks and the amygdala and hippocampus using seed- based analysis methods. Changes in between-network connectivity, within-network connectivity and intra-striatal connectivity were examined. Changes in anhedonia and impulsivity from pre- to post- treatment were assessed. Both Escitalopram and psilocybin therapy groups demonstrated reductions in impulsivity and anhedonia scores. Significant interaction effects were found with the amygdala network, where there was a significant psilocybin >escitalopram change in connectivity with a region in the left anterior insula extending into the left putamen. There was a significant escitalopram >psilocybin change in connectivity with a region in the right cerebellum around Crus I extending up to the occipital cortex. There was a significant psilocybin >escitalopram change in connectivity in the limbic striatal network with the bilateral insula, the paracingulate and the temporoparietal junction. Post-hoc analysis revealed this interaction effect was driven by a reduction in connectivity in with the insula in the escitalopram group. A significant correlation was found between the escitalopram induced reduction in connectivity with the insula and the limbic striatum and a reduction in anhedonia. Reduced connectivity between the limbic striatal network and insula correlated with reductions in anhedonia in the escitalopram group. The insula is an area linked to salience and attentional control, which may reveal the reduction in influence of emotional processing from the limbic regions on the bottom-up detection of salient events. In psilocybin, an increase in connectivity between the amygdala and temporoparietal regions was observed, which may indicate enhanced sensory integration and with reward processing. These results show varying treatment effects on striatal connectivity and the implicit difference in treatment effect on underlying neural circuitry.

Introduction Early life adversity (ELA) such as physical and emotional abuse has been recognized as an important risk factor for depression in adults. Past research has shown that ELA was associated with alteration in the hippocampus, a key region involved in stress sensitivity, emotional learning and memory. However, relatively little is known about the role of the hippocampus in the relationship between ELA and depression in adolescents. Objectives This study aimed to investigate whether the hippocampal volume and hippocampus resting-state functional connectivity (RSFC) moderated the relationship between ELA and depressive symptom severity in adolescents with major depressive disorder (MDD). Methods This study included 73 adolescents with MDD (age M (SD) = 15.0 (1.5) years, 51 girls). The participants completed the Early Trauma Inventory and Children’s Depression Rating Scale to assess ELA and depressive symptom severity, respectively. Resting–state functional and structural T1 images were collected using a Siemens 3T MR scanner and preprocessed using AFNI and FreeSurfer routines. The average BOLD time-series was extracted from our regions-of-interest (ROIs), the bilateral hippocampus and dorsolateral prefrontal cortex (DLPFC). An ROI-to-ROI RSFC analysis was conducted to calculate Pearson correlation coefficients between the hippocampus and DLPFC ROIs. The correlation coefficients were transformed to Fisher’s z. We performed correlation and moderation analyses to test our moderation model (Figure 1) after controlling for age and sex. Results Emotional abuse, one form of ELA, was significantly correlated with depressive symptoms in adolescents with MDD (r = 0.25, p < .05). Bilateral hippocampus – left DLPFC RSFCs moderated the association between emotional abuse and depressive symptoms in adolescents with MDD (ps < .01). The association between emotional abuse and depressive symptoms was stronger when bilateral hippocampus – left DLPFC RSFCs were lower (left hippocampus – left DLPFC RSFC, -1D: b = 3.72, SE = 1.06, p < .001; right hippocampus – left DLPFC RSFC, -1D: b = 4.15, SE = 1.04, p < .001) than when they were greater (left hippocampus – left DLPFC RSFC, +1D: b = -0.09, SE = 1.05, p = .93; right hippocampus – left DLPFC RSFC, +1D: b = -0.10, SE = 0.98, p = .69) (Figure 2). Hippocampal volumes also moderated the relationship between emotional abuse and depressive symptoms, but the results did not remain significant after correcting for multiple comparisons. Image 1: Image 2: Conclusions Our findings suggest the important role of hippocampus RSFC with the DLPFC in the relationship between emotional abuse and depressive symptoms in adolescents with MDD. Disclosure of Interest None Declared

Major Depressive Disorder (MDD) is a common and costly mental health condition that is often associated with deficits in executive function. Smartphone applications (“apps”) have emerged as promising methods for assessing mental health-related outcomes in individuals’ daily lives that can detect changes that unfold over time. Although smartphone apps have been used to evaluate executive functioning in individuals with neurological conditions and in other mental health disorders, few studies have examined this in MDD. The present study tested whether smartphone-assessed executive function is (1) impaired in individuals with current MDD (cMDD; n=30) relative to healthy controls (HC; n=43) and (2) related to resting-state functional connectivity within cognitive control-related neural networks. For two weeks, participants completed a set-shifting (Trail Making Test, TMT-B) task on their smartphone. Participants with cMDD had significantly lower TMT accuracy (β=−.24, p=.044) and greater variability in TMT accuracy (β=.25, p=.049) compared to HC. Resting-state analyses revealed that the association between TMT accuracy and connectivity between nodes of the dorsal attention network (bilateral intraparietal sulcus) was greater in HC than cMDD. The results extend previous laboratory-based findings by demonstrating that individuals with cMDD exhibit poorer mean-level smartphone assessed set-shifting performance and greater variability, along with altered set-shifting-related functional connectivity within the dorsal attention network. Smartphone-based assessments may offer a scalable and accessible approach for identifying executive function deficits in individuals with depression, which could potentially be integrated in monitoring treatment effects over time.

Spontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In this study resting-state functional magnetic resonance imaging was used to measure the differences in connectivity and activation seen in major depressive disorder (MDD) patients with and without suicidal ideation and the control group. For our investigation, a brain atlas containing 116 regions of interest was used. We also used four voxel-based connectivity models, including degree centrality, the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity, and voxel-mirrored Homotopic Connectivity. Feature selection was conducted using a sequential backward floating selection approach along with a Random Forest Classifier and Elastic Net. While all four models yield significant results, fALFF demonstrated higher accuracy rates in classifying the three groups. Further analysis revealed three features that demonstrated statistically significant differences between these three, resulting in a 90.00% accuracy rate. Prominent features identified from our analysis, with suicide ideation as the key variable, included the Superior frontal gyrus (dorsolateral and orbital parts), the median cingulate, and the paracingulate gyri. These areas are associated with the Central Executive Control Network (ECN), the Default Mode Network, and the ECN, respectively. Comparing the results of MDD patients with suicidal ideation to those without suicidal ideations suggests dysfunctions in decision-making ability, in MDD females suffering from suicidal tendencies. This may be related to a lack of inhibition or emotion regulation capability, which contributes to suicidal ideations.

BACKGROUND Family history of Major Depressive Disorder (MDD) is a robust predictor of MDD onset, especially in early adolescence. We examined the relationships between familial risk for depression and alterations to resting state functional connectivity (rsFC) within the default mode network (wDMN) and between the DMN and the left/right hippocampus (DMN-LHIPP/DMN-RHIPP) to the risk for early adolescent MDD onset. METHODS We examined 9403 youth aged nine to eleven from the Adolescent Brain Cognitive Development study. Depressive symptoms were measured with the parent-reported Child Behavior Checklist. Both youth and their parents completed the Kiddie Schedule for Affective Disorders and Schizophrenia, which provided MDD diagnoses. A family history screen was administered to determine familial risk for depression. Youth underwent a resting state functional magnetic resonance imaging scan, providing us with rsFC data. RESULTS Negative wDMN rsFC was associated with child-reported current depression, both child- and parent-reported past depression, and parent-reported current depressive symptoms. No difference was found in wDMN, DMN-LHIPP or DMN-RHIPP rsFC in children with or without familial risk for depression. Familial risk for depression interacted with wDMN rsFC in association with child-reported past MDD diagnosis and parent-reported current depressive symptoms. LIMITATIONS Information such as length of depressive episodes and age of onset of depression was not collected. CONCLUSIONS Altered wDMN rsFC in youth at familial risk for depression may be associated with increased risk for MDD onset in adolescence, but longitudinal studies are needed to test this hypothesis.

Abstract Background The emotion regulation network (ERN) in the brain provides a framework for understanding the neuropathology of affective disorders. Although previous neuroimaging studies have investigated the neurobiological correlates of the ERN in major depressive disorder (MDD), whether patients with MDD exhibit abnormal functional connectivity (FC) patterns in the ERN and whether the abnormal FC in the ERN can serve as a therapeutic response signature remain unclear. Methods A large functional magnetic resonance imaging dataset comprising 709 patients with MDD and 725 healthy controls (HCs) recruited across five sites was analyzed. Using a seed-based FC approach, we first investigated the group differences in whole-brain resting-state FC of the 14 ERN seeds between participants with and without MDD. Furthermore, an independent sample (45 MDD patients) was used to evaluate the relationship between the aforementioned abnormal FC in the ERN and symptom improvement after 8 weeks of antidepressant monotherapy. Results Compared to the HCs, patients with MDD exhibited aberrant FC between 7 ERN seeds and several cortical and subcortical areas, including the bilateral middle temporal gyrus, bilateral occipital gyrus, right thalamus, calcarine cortex, middle frontal gyrus, and the bilateral superior temporal gyrus. In an independent sample, these aberrant FCs in the ERN were negatively correlated with the reduction rate of the HAMD17 score among MDD patients. Conclusions These results might extend our understanding of the neurobiological underpinnings underlying unadaptable or inflexible emotional processing in MDD patients and help to elucidate the mechanisms of therapeutic response.

Highlights • MDD patients exhibited decreased rsFC between the NAc subregions and the MCC.• Antidepressant treatment normalized the altered rsFC of NAc subregions in MDD.• Lower levels of rsFC between the NAc subregions and the MCC predicted greater treatment efficacy.• Dysfunction in the frontal-ventral striatum circuitry may represent a key therapeutic target for MDD.

This study investigated how resting-state functional connectivity (rsFC) of the subgenual anterior cingulate cortex (sgACC) predicts antidepressant response in patients with major depressive disorder (MDD). Eighty-seven medication-free MDD patients underwent baseline resting-state functional MRI scans. After 12 weeks of escitalopram treatment, patients were classified into remission depression (RD, n = 42) and nonremission depression (NRD, n = 45) groups. We conducted two analyses: a voxel-wise rsFC analysis using sgACC as a seed to identify group differences, and a prediction model based on the sgACC rsFC map to predict treatment efficacy. Haufe transformation was used to interpret the predictive rsFC features. The RD group showed significantly higher rsFC between the sgACC and regions in the fronto-parietal network (FPN), including the bilateral dorsolateral prefrontal cortex (DLPFC) and bilateral inferior parietal lobule (IPL), compared to the NRD group. These sgACC rsFC measures correlated positively with symptom improvement. Baseline sgACC rsFC also significantly predicted treatment response after 12 weeks, with a mean accuracy of 72.64% (p < 0.001), mean area under the curve of 0.74 (p < 0.001), mean specificity of 0.82, and mean sensitivity of 0.70 in 10-fold cross-validation. The predictive voxels were mainly within the FPN. The rsFC between the sgACC and FPN is a valuable predictor of antidepressant response in MDD patients. These findings enhance our understanding of the neurobiological mechanisms underlying treatment response and could help inform personalized treatment strategies for MDD.

BACKGROUND Recent studies have highlighted the significant role of inflammation in the development and progression of major depressive disorder (MDD). Elevated levels of proinflammatory cytokines have consistently been observed in MDD, and these markers are shown to be linked to disruptions in brain networks. Therefore, we aimed to explore the relationship between inflammatory markers and resting-state functional connectivity (RSFC) in patients with MDD. METHODS This study included 76 patients with MDD and 92 healthy controls (HCs). Seed-to-voxel RSFC analysis was performed using brain regions that have been identified in previous studies on the neural networks implicated in MDD. These regions served as key hubs in the default mode, salience, cognitive control, and frontostriatal networks and were used as seed regions. RESULTS Compared with HCs, patients with MDD exhibited elevated levels of interleukin (IL)-6 and IL-8. The MDD group showed significant alterations of the RSFC between the prefrontal cortex (PFC), anterior cingulate cortex, visual cortex, postcentral gyrus, and striatal regions compared to the HC group. Additionally, within the MDD group, a positive correlation was observed between tumor necrosis factor (TNF)-α levels and the RSFC of the right dorsolateral prefrontal cortex (dlPFC) and visual cortex. Conversely, in the HC group, TNF-α levels were negatively correlated with the RSFC between the right dlPFC and bilateral dorsomedial prefrontal cortex, while positive correlations were noted between the RSFC of the right dlPFC with occipital regions and the levels of both IL-8 and TNF-α. CONCLUSIONS The present study confirmed that cytokine levels are linked to alterations in the RSFC, particularly in the prefrontal regions. Our findings suggest that systemic inflammation may contribute to functional disruptions in the brain networks involved in emotion regulation and cognitive control in MDD.

The different symptoms of major depressive disorder (MDD) in adolescents compared to adults suggested there may be differences in the pathophysiology between adolescents and adults with MDD. However, despite the amygdala being considered critical in the pathophysiology, there was limited knowledge about the commonalities and differences in the resting-state functional connectivity (rsFC) of amygdala subregions in MDD patients of different age groups. In the current study, 65 adolescents (46 with MDD and 19 controls) and 91 adults (35 with MDD and 56 controls) were included. A seed-based functional connectivity analysis was performed for each of the amygdala subregions. A 2 × 2 ANOVA was used to analyze the main effect of age, diagnosis, and their interaction on the rsFC of each subregion. A significant main effect of age was revealed in the rsFC of bilateral centromedial (CM) subregions and right laterobasal (LB) subregion with several brain regions in the limbic system and frontoparietal network. The significant main effect of diagnosis showed MDD patients of different ages showed higher connectivity than controls between the right LB and left middle frontal gyrus (MFG). The rsFC of specific amygdala subregions with brain regions in the limbic system and frontoparietal network is affected by age, indicating a distinct amygdala connectivity profile in adolescents. The decreased rsFC between the right LB and the left MFG in adolescents and adults with MDD could serve as a diagnostic biomarker and a target of nonpharmacological treatment for MDD.

The brain's default mode network (DMN) and the executive control network (ECN) switch engagement are influenced by the ventral attention network (VAN). Alterations in resting‐state functional connectivity (RSFC) within this so‐called triple network have been demonstrated in patients with major depressive disorder (MDD) or anxiety disorders (ADs). This study investigated alterations in the RSFC in patients with comorbid MDD and ADs to better understand the pathophysiology of this prevalent group of patients. Sixty‐eight participants (52.9% male, mean age 35.3 years), consisting of 25 patients with comorbid MDD and ADs (MDD + AD), 20 patients with MDD only (MDD) and 23 healthy controls (HCs) were investigated clinically and with 3T resting‐state fMRI. RSFC utilizing a seed‐based approach within the three networks belonging to the triple network was compared between the groups. Compared with HC, MDD + AD showed significantly reduced RSFC between the ECN and the VAN, the DMN and the VAN and within the ECN. No differences could be found for the MDD group compared with both other groups. Furthermore, symptom severity and medication status did not affect RSFC values. The results of this study show a distinct set of alterations of RSFC for patients with comorbid MDD and AD compared with HCs. This set of dysfunctions might be related to less adequate switching between the DMN and the ECN as well as poorer functioning of the ECN. This might contribute to additional difficulties in engaging and utilizing consciously controlled emotional regulation strategies.

Background: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depressive disorder (MDD), but substantial heterogeneity in outcomes remains. We examined a potential mechanism of action of rTMS to normalize individual variability in resting-state functional connectivity (rs-fc) before and after a course of treatment. Methods: Variability in rs-fc was examined in healthy controls (baseline) and individuals with MDD (baseline and after 4–6 weeks of rTMS). Seed-based connectivity was calculated to 4 regions associated with MDD: left dorsolateral prefrontal cortex (DLPFC), right subgenual anterior cingulate cortex (sgACC), bilateral insula, and bilateral precuneus. Individual variability was quantified for each region by calculating the mean correlational distance of connectivity maps relative to the healthy controls; a higher variability score indicated a more atypical/idiosyncratic connectivity pattern. Results: We included data from 66 healthy controls and 252 individuals with MDD in our analyses. Patients with MDD did not show significant differences in baseline variability of rs-fc compared with controls. Treatment with rTMS increased rs-fc variability from the right sgACC and precuneus, but the increased variability was not associated with clinical outcomes. Interestingly, higher baseline variability of the right sgACC was significantly associated with less clinical improvement (p = 0.037, uncorrected; did not survive false discovery rate correction). Limitations: The linear model was constructed separately for each region of interest. Conclusion: This was, to our knowledge, the first study to examine individual variability of rs-fc related to rTMS in individuals with MDD. In contrast to our hypotheses, we found that rTMS increased the individual variability of rs-fc. Our results suggest that individual variability of the right sgACC and bilateral precuneus connectivity may be a potential mechanism of rTMS.

Air pollution, a reversible environmental factor, was significantly associated with the cognitive domains that are impaired in major depressive disorder (MDD), notably processing speed. Limited evidence explores the interactive effect of air pollution and the genetic risk of depression on cognition. This cross-sectional study aims to extend the research by specifically examining how this interaction influences depression-related cognitive impairment and resting-state brain function. Eligible participants were 497 healthy adult volunteers (48.7% males, mean age 24.5) living in Beijing for at least 1 year and exposed to relatively high air pollution from the local community controlling for socioeconomic and genomic. Six months’ ambient air pollution exposures were assessed based on residential addresses using monthly averages of fine particulate matter with a diameter of less than or equal to 2.5 μm (PM2.5). A cross-sectional analysis was conducted using functional magnetic resonance imaging (fMRI) and cognitive performance assessments. The polygenic risk score (PRS) of MDD was used to estimate genetic susceptibility. Using a general linear model and partial least square regression, we observed a negative association between resting-state local connectivity in precuneus and PRS-by-PM2.5 interactive effect (PFWE = 0.028), indicating that PM2.5 exposure reduced the spontaneous activity in precuneus in individuals at high genetic risk for MDD. DNA methylation and gene expression of the SLC30A3 gene, responsible for maintaining zinc-glutamate homeostasis, was suggestively associated with this local connectivity. For the global functional connectivity, the polygenic risk for MDD augmented the neural impact of PM2.5 exposure, especially in the frontal-parietal and frontal-limbic regions of the default mode network (PFDR < 0.05). In those genetically predisposed to MDD, increased PM2.5 exposure positively correlated with resting-state functional connectivity between the left angular gyrus and left cuneus gyrus. This connectivity was negatively associated with processing speed. Our cross-sectional study suggests that air pollution may be associated with an increased likelihood of cognitive impairment in individuals genetically predisposed to depression, potentially through alterations in the resting-state function of the occipitoparietal and default mode network.

Background: Resting-state functional connectivity (RSFC) is widely used to identify abnormal brain function associated with depression. Resting-state functional magnetic resonance imaging (fMRI) scans have many potential confounds, and task-based FC might provide complementary information leading to better insight on brain function. Methods: We used MATLAB’s (version 2024b) CONN toolbox (version 22a) to evaluate FC in 40 adults with and without major depressive disorder (MDD) (nMDD = 23, nHC = 17). fMRI acquisition was performed while participants were at rest and while performing the Selves Task, an individualized goal priming task. Seed-based analyses were performed using two seeds: medial prefrontal cortex (mPFC) and left hippocampus. Results: Both groups showed strong positive RSFC between the mPFC and other DMN regions, including the anterior cingulate cortex and precuneus, which had more focal positive FC to the mPFC during the task in both groups. Additionally, the MDD group had significantly lower RSFC between the mPFC and several regions, including the right inferior temporal gyrus. The left hippocampus seed-based analysis revealed a pattern of hypoconnectivity to multiple brain regions in MDD, including the cerebellum, which was present at rest and during the task. Conclusions: Our results indicated multiple FC differences between adults with and without MDD, as well as distinct FC patterns and contrast results in resting state and task-based analyses, including differential FC between mPFC–cerebellum and hippocampus–cerebellum. These results emphasize that resting-state and task-based fMRI capture distinct patterns of brain connectivity. Further investigation into combining resting-state and task-based FC could inform future neuroimaging research.

INTRODUCTION We aimed to investigate the relationship between changes in resting-state functional connectivity in a subregion of the hippocampus and the antidepressant effects of esketamine as well as to identify potential neuroimaging markers of the treatment outcomes. METHODS Twenty-nine patients with major depressive disorder (MDD) received six intravenous infusions of esketamine. All patients completed the Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Baker Suicide Ideation Scale (BSI), and Montreal Cognitive Assessment Scale (MoCA) to assess emotional and cognitive recovery after treatment. At the same time, all participants underwent magnetic resonance imaging using seed point functional connectivity analysis to divide the hippocampus into two subregions (rostral hippocampus (rHipp) and caudal hippocampus (cHipp)). RESULTS We found that 29 patients with MDD responded favorably to esketamine, with significant reductions in HAMA, HAMD, and BSI scores and significant increases in MoCA scores. After two weeks of treatment with esketamine, we found that the FC between the right cHipp and the left cerebellum_6, precuneus, and middle temporal gyrus (MTG) was significantly increased in patients with MDD. Correlation analysis showed that the FC values between the right cHipp subregion and the left MTG were negatively correlated with MoCA scores. DISCUSSION The altered functional connectivity pattern in the hippocampal subregions in patients with MDD may be related to the regulatory mechanism of esketamine in improving depressive symptoms, mainly involving the default network and cortico-cerebellar loop. This study provides new insights into the antidepressant effects of esketamine and potential targets for the treatment of MDD.

BACKGROUND Adolescent depression is a growing public health concern, and neuroimaging offers a promising approach to its pathology. We focused on the functional connectivity of the amygdala and subgenual anterior cingulate cortex (sgACC), which is theoretically important in major depressive disorder (MDD), but empirical evidence has remained inconsistent. This discrepancy is likely due to the limited statistical power of small sample sizes. METHODS We rigorously examined sgACC-amygdala connectivity in depressed adolescents and adults using data from the Healthy Brain Network (n=321; 170 females), the Adolescent Brain Cognitive Development study (n=141; 56 females), the Boston Adolescent Neuroimaging of Depression and Anxiety study (n=108; 75 females), and the REST-meta-MDD project (n=1436; 880 females). Linear mixed models, Bayesian factor analyses, and meta-analysis were employed to assess connectivity. RESULTS Our analyses revealed that sgACC-amygdala connectivity in adolescents with MDD was comparable to that in healthy controls, whereas adults with recurrent MDD exhibited reduced connectivity. Resampling analysis demonstrated that small sample sizes (i.e., n<30 MDDs) tend to inflate effects, potentially leading to misinterpretations. CONCLUSIONS These findings clarify the state of sgACC-amygdala connectivity in MDD and underscore the importance of refining neurocognitive models separately for adolescents and adults. The study also highlights the necessity for large-scale replication studies to ensure robust and reliable findings.

BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern. The neural mechanisms underlying suicidal behavior in adolescents with major depressive disorder (MDD) remain poorly understood. Aberrant resting-state functional connectivity (rsFC) in the amygdala, a key region implicated in emotional regulation and threat detection, is strongly implicated in depression and suicidal behavior. AIM To investigate rsFC alterations between amygdala subregions and whole-brain networks in adolescent patients with depression and suicide attempts. METHODS Resting-state functional magnetic resonance imaging data were acquired from 32 adolescents with MDD and suicide attempts (sMDD) group, 33 adolescents with MDD but without suicide attempts (nsMDD) group, and 34 demographically matched healthy control (HC) group, with the lateral and medial amygdala (MeA) defined as regions of interest. The rsFC patterns of amygdala subregions were compared across the three groups, and associations between aberrant rsFC values and clinical symptom severity scores were examined. RESULTS Compared with the nsMDD group, the sMDD group exhibited reduced rsFC between the right lateral amygdala (LA) and the right inferior occipital gyrus as well as the left middle occipital gyrus. Compared with the HC group, the abnormal brain regions of rsFC in the sMDD group and nsMDD group involve the parahippocampal gyrus (PHG) and fusiform gyrus. In the sMDD group, right MeA and right temporal pole: Superior temporal gyrus rsFC value negatively correlated with the Rosenberg Self-Esteem Scale scores (r = -0.409, P = 0.025), while left LA and right PHG rsFC value positively correlated with the Adolescent Self-Rating Life Events Checklist interpersonal relationship scores (r = 0.372, P = 0.043). CONCLUSION Aberrant rsFC changes between amygdala subregions and these brain regions provide novel insights into the underlying neural mechanisms of suicide attempts in adolescents with MDD.

No abstract available

In this study, the effects of antidepressants on large‐scale brain networks and the neural basis of individual differences in response were explored. A total of 41 patients with major depressive disorder (MDD) and 42 matched healthy controls (HCs) were scanned by resting‐state functional magnetic resonance imaging separately at baseline and after a 12‐week follow‐up. The patients with MDD received escitalopram for 12 weeks. After treatment, patients were classified into those with MDD in remission [MDDr, endpoint 17‐item Hamilton Depression Rating Scale (HAMD) total score ≤7] and those in nonremission (MDDnr). The human Brainnetome Atlas was used to define large‐scale networks and compute within‐ and between‐network resting‐state functional connectivity (rsFC). Results showed the decreased subcortical network (SCN)–ventral attention network (VAN) connectivity at baseline increased in patients with MDD after 12‐week treatment, and it was comparable with that of HCs. This change was only observed in patients with MDDr. However, the decreased within‐network rsFC in SCN and default mode network (DMN) persisted in all patients with MDD, including those with MDDr and MDDnr, after treatment. The strength of SCN–VAN connectivity at baseline was significantly negatively correlated with the reduction rate of HAMD score in all patients with MDD. Thus, SCN–VAN connectivity may be an antidepressant target associated with depressive state changes and a predictor of treatment response to serotonin reuptake inhibitors. The within‐network rsFC in SCN and DMN may reflect a trait‐like abnormality in MDD. These findings provide further insights into the mechanism of antidepressants and their individual differences in response. The trial name is “Appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement” (URL: http://www.chictr.org.cn/showproj.aspx?proj=21377; registration number: ChiCTR‐OOC‐17012566).

BACKGROUND Studies comparing the brain functions of major depressive disorder (MDD) and social anxiety disorder (SAD) at the regional and network levels remain scarce. This study aimed to elucidate their pathogenesis using neuroimaging techniques and explore biomarkers that can differentiate these disorders. METHODS Resting-state fMRI data were collected from 48 patients with MDD, 41 patients with SAD, and 82 healthy controls. Differences in the amplitude of low-frequency fluctuations (ALFF) among the three groups were examined to identify regions showing abnormal regional spontaneous activity. A seed-based functional connectivity (FC) analysis was conducted using ALFF results as seeds and different connections were identified between regions showing abnormal local spontaneous activity and other regions. The correlation between abnormal brain function and clinical symptoms was analyzed. RESULTS Patients with MDD and SAD exhibited similar abnormal ALFF and FC in several brain regions; notably, FC between the right superior frontal gyrus (SFG) and the right posterior supramarginal gyrus (pSMG) in patients with SAD was negatively correlated with depressive symptoms. Furthermore, patients with MDD showed higher ALFF in the right SFG than HCs and those with SAD. LIMITATION Potential effects of medications, comorbidities, and data type could not be ignored. CONCLUSION MDD and SAD showed common and distinct aberrant brain function patterns at the regional and network levels. At the regional level, we found that the ALFF in the right SFG was different between patients with MDD and those with SAD. At the network level, we did not find any differences between these disorders.

Major depressive disorder (MDD) is a devastating mental disorder that affects up to 17% of the population worldwide. Although brain-wide network-level abnormalities in MDD patients via resting-state functional magnetic resonance imaging (rsfMRI) exist, the mechanisms underlying these network changes are unknown, despite their immense potential for depression diagnosis and management. Here, we show that the astrocytic calcium-deficient mice, inositol 1,4,5-trisphosphate-type-2 receptor knockout mice (Itpr2−/− mice), display abnormal rsfMRI functional connectivity (rsFC) in depression-related networks, especially decreased rsFC in medial prefrontal cortex (mPFC)–related pathways. We further uncover rsFC decreases in MDD patients highly consistent with those of Itpr2−/− mice, especially in mPFC-related pathways. Optogenetic activation of mPFC astrocytes partially enhances rsFC in depression-related networks in both Itpr2−/− and wild-type mice. Optogenetic activation of the mPFC neurons or mPFC-striatum pathway rescues disrupted rsFC and depressive-like behaviors in Itpr2−/− mice. Our results identify the previously unknown role of astrocyte dysfunction in driving rsFC abnormalities in depression.

BACKGROUND Chronic stress impacts brain function and emotion regulation, increasing depression risk. How chronic stress shapes neural dynamics in response to acute stress remains unclear. This study investigates how chronic stress influences neural responses after acute stress, focusing on ventromedial prefrontal cortex (vmPFC)-amygdala and vmPFC-hippocampus functional connectivity (FC) and their relationship to depression symptoms. METHODS Eighty-seven adults underwent resting-state fMRI at baseline, during acute stress, and during recovery. Participants were divided into High and Low chronic stress groups based on perceived stress over the past 4 weeks. Depression symptoms were measured with the Symptom Checklist-90. Linear mixed-effect model and repeated-measures ANOVA were used to analyse neural dynamics and interaction effects. Recovery-related changes in FC were calculated as differences between acute stress and recovery. RESULTS Distinct neural dynamics patterns across stress phases emerged between groups. The Low group showed significant decreases in vmPFC-amygdala and vmPFC-hippocampus connectivity from acute stress to recovery, while the High group exhibited no changes. Chronic stress moderated the association between the recovery-related changes in vmPFC-amygdala connectivity and depression symptoms. In the High chronic stress group, greater decreases in FC from stress to recovery were associated with higher depression symptoms. CONCLUSIONS Chronic stress modulates neural dynamics during acute stress response and recovery, and their association with depression symptoms. Individuals with higher chronic stress exhibit blunted cortical-limbic circuit dynamics, potentially increasing depression vulnerability. Rapid disengagement of emotion regulation circuits may represent a maladaptive response supporting the allostatic load model. These findings clarify stress, brain, and depression relationships.

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The NMDA receptor (NMDAR) antagonist ketamine elicits a long-lasting antidepressant response in patients with treatment-resistant depression. Understanding how antagonism of NMDARs alters synapse and circuit function is pivotal to developing circuit-based therapies for depression. Using virally induced gene deletion, ex vivo optogenetic-assisted circuit analysis, and in vivo chemogenetics and fMRI, we assessed the role of NMDARs in the medial prefrontal cortex (mPFC) in controlling depression-related behavior in mice. We demonstrate that post-developmental genetic deletion of the NMDAR subunit GluN2B from pyramidal neurons in the mPFC enhances connectivity between the mPFC and limbic thalamus, but not the ventral hippocampus, and reduces depression-like behavior. Using intersectional chemogenetics, we show that activation of this thalamocortical circuit is sufficient to elicit a decrease in despair-like behavior. Our findings reveal that GluN2B exerts input-specific control of pyramidal neuron innervation and identify a medial dorsal thalamus (MDT)→mPFC circuit that controls depression-like behavior.

The widely acknowledged cognitive theory of depression, developed by Aaron Beck, focused on biased information processing that emphasizes the negative aspects of affective and conceptual information. Current attempts to discover the neurological mechanism underlying such cognitive and affective bias have successfully identified various brain regions associated with severally biased functions such as emotion, attention, rumination, and inhibition control. However, the neurobiological mechanisms of how individuals in depression develop this selective processing toward negative is still under question. This paper introduces a neurological framework centered around the frontal-limbic circuit, specifically analyzing and synthesizing the activity and functional connectivity within the amygdala, hippocampus, and medial prefrontal cortex. Firstly, a possible explanation of how the positive feedback loop contributes to the persistent hyperactivity of the amygdala in depression at an automatic level is established. Building upon this, two hypotheses are presented: hypothesis 1 revolves around the bidirectional amygdalohippocampal projection facilitating the amplification of negative emotions and memories while concurrently contributing to the impediment of the retrieval of opposing information in the hippocampus attractor network. Hypothesis 2 highlights the involvement of the ventromedial prefrontal cortex in the establishment of a negative cognitive framework through the generalization of conceptual and emotional information in conjunction with the amygdala and hippocampus. The primary objective of this study is to improve and complement existing pathological models of depression, pushing the frontiers of current understanding in neuroscience of affective disorders, and eventually contributing to successful recovery from the debilitating affective disorders.

Major Depressive Disorder (MDD) is being increasingly viewed as a network-level pathology of the brain involving dysfunctional neural connections, neuroplasticity impairments, as well as a lack of executive control of emotional processes. In December 2024, the U.S. FDA approved the at-home transcranial direct current stimulation device (tDCS) 'Flow Neuroscience’s FL-100' for the treatment of moderate to severe depressive disorders in adults. The advent of this non-pharmacological approach represents a paradigmatic shift in circuit-level therapies for depression. In this paper, we investigate the neuropsychological basis of the FDA’s approval of the FL-100 device within a modern conceptualization of depression involving disruptions in the regulatory circuitry of the prefrontal and limbic systems of the brain. In addition, this study advances a theoretically well-grounded multi-modal approach to integrate transcranial stimulation using music-driven auditory stimulation as a primary state-dependent amplifier of the electromagnetic impacts of FL-100 stimulation of underlying emotional, reward, as well as executive neural networks of the brain, as supported by empirical evidence in cognitive neuroscience, affective neuropsychology, as well as research in non-invasive brain stimulation techniques.

Experiencing family material hardship has been shown to be associated with disruptions in physical and psychological development. However, the association between material hardship and functional connectivity in the fronto-limbic circuit during fear learning is unclear. A total of 161 healthy young adults aged 17-28 were recruited in our brain imaging study, using the Fear Conditioning Task to test the associations between material hardship and connectivity in fronto-limbic circuit and psychopathology. The results showed that family material hardship was linked to higher positive connectivity between the left amygdala and bilateral dorsal anterior cingulate cortex, as well as higher negative connectivity between the left hippocampus and right ventromedial prefrontal cortex. A mediation analysis showed that material hardship was associated with depression via amygdala functional connectivity (indirect effect = 0.228, P = 0.016), and also indirectly associated with aggression and anger-hostility symptoms through hippocampal connections (aggression: indirect effect = 0.057, P = 0.001; anger-hostility: indirect effect = 0.169, P = 0.048). That is, family material hardship appears to affect fronto-limbic circuits through changes in specific connectivity, and these specific changes, in turn, could lead to specific psychological symptoms. The findings have implications for designing developmentally sensitive interventions to mitigate the emergence of psychopathological symptoms.

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Depression is associated with negative emotional biases which are often studied using images of fearful facial expressions. Brain imaging studies of depression with fearful face stimuli have extensively focused on the amygdala and the prefrontal cortex, but the results have been inconsistent, potentially due to small studied sample sizes (typically N<50). It remains unclear whether there are altered activations in these regions in response to fearful faces, and if any alterations are a characteristic of depressive state or of past experience of depression. Moreover, it is not clear if there are changes in effective connectivity between these regions. To address these questions, we investigated activations of the amygdala and the dorsolateral prefrontal cortex (DLPFC), as well as effective connectivity between these regions, in response to fearful face stimuli in a comparatively large, deeply-phenotyped, population-based sample of participants who had a clinically-verified diagnosis of major depressive disorder during their lifetime (lifetime depression). Brain imaging was conducted in a subsample of the Generation Scotland cohort. While in the scanner, control participants with no history of depression (N=664) and participants with lifetime depression (N=290) completed an implicit facial emotion processing task with neutral and fearful face stimuli. Case-control differences in activations of the amygdala and the DLPFC were assessed using a region-of-interest approach. Changes in effective connectivity between these regions were assessed with dynamic causal modelling. Compared to controls, lifetime depression was associated with increased activation in the left amygdala (small volume corrected PFWE=0.031, cluster volume 4 voxels; mean activation change {beta}=0.0715, P=0.0314) and the left DLPFC (small volume corrected PFWE=0.002, cluster volume 33 voxels) in response to fearful faces compared to baseline. Effective connectivity analysis indicated significantly increased inhibition from the left amygdala to the left DLPFC (effect -0.15, probability 0.82), again related to the contrast of fearful faces to baseline. These results did not appear to be attributed to acute depressive illness severity or antidepressant medication status. Our findings indicate that lifetime (past or present) experience of depression is related to small increases in activity of both the amygdala and the DLPFC in response to fearful faces, with complementary evidence of stronger inhibitory input from the amygdala to the DLPFC. These results substantially contribute to addressing uncertainties in the past literature and suggest disruption of 'bottom-up' limbic-prefrontal connectivity in depression.

Clinical studies have reported altered reward processing in major depressive disorder. Using dynamic causal modelling, Rupprecher et al. show that blunted reward-related effective connectivity from the medial prefrontal cortex to the striatum is associated with increased severity of depressive symptoms.

BACKGROUND Major depressive disorder (MDD) is characterized by strong emotional dysregulation. Mechanisms driving the negative affect in depression may be fast processes existing on an unconscious level. METHODS A priming task was conducted using simultaneous EEG-fMRI measurement involving presentation of facial expressions (happy, sad, neutral) to examine the neurophysiological pathway of biased unconscious emotion processing in MDD. Priming prior to a target emotion created unconscious (16.7 ms primer) and conscious (150 ms primer) trials. A large sample of N = 126 was recruited, containing healthy controls (HC; n = 66; 37 women) and MDD (n = 60; 31 women). RESULTS HC showed a shorter reaction time in happy, but not in sad or neutral trials compared to MDD. N170 amplitudes were lower in trials with unconscious compared to conscious primer presentation. N170 amplitudes correlated with cortical (right fusiform gyrus (FFG), right middle temporal gyrus, right inferior temporal gyrus, left supplementary motor area, right middle frontal gyrus) and subcortical brain regions (right amygdala). The strength of N170 and brain activity correlation increased when the stimulus was consciously presented. Presented emotions did not affect the correlation of N170 values and brain activity. CONCLUSIONS Our findings show that MDD may exhibit biased emotion regulation abilities at a behavioral and neurophysiological level. Face-sensitive event-related potentials demonstrate a correlation with heightened brain activity in regions associated with both face recognition (FFG) and emotion processing (amygdala). These findings are evident in both MDD and HC, with lower effect sizes in MDD indicating reduced emotion recognition and processing abilities.

OBJECTIVE Patients with major depressive disorder (MDD) may experience a series of emotional and mental problems accompanied by characteristic clinical symptoms. Depressive patients often have emotional recognition disorders, but the reasons remain unclear. Though a great many functional abnormalities have been observed in the brains of depressed patients, such abnormalities are not often related to clinical symptoms. Currently in Traditional Chinese Medicine (TCM), syndrome differentiation for the MDD mainly consists of excess pattern (EP), and deficiency pattern (DP). EP and DP emphasize balance-regulation thought processes, and are widely used in diagnosis of diseases including depression, anxiety, insomnia, and other emotional disorders. We hope that syndrome differentiation in TCM can combine clinical symptoms and brain function more effectively. The present study investigated altered patterns and different association of brain activation in MDD patients with EP and DP during a facial emotion discrimination task with fMRI. METHODS A total of 45 patients (20 with EP and 25 with DP) and 18 normal controls participated in this study. Whole-brain functional scans were collected for each subject. Different patterns of brain activation and association during the facial emotion discrimination task were analyzed statistically. RESULTS Comparing all the MDD patients with the normal controls, there were no significant differences for sad vs. neutral condition or for happy vs. neutral condition (corrected p >  0.05). One-way ANCOVA showed significant differences in the left inferior frontal gyrus, the left insula, and the left caudate for sad vs. neutral condition across the DP, EP and NC groups (corrected p <  0.05). The whole brain activation comparison for sad vs. neutral condition between the EP MDD subtype and the DP MDD subtype further verified these differences in the left insula and left inferior frontal gyrus, discovering that these regions showed increased activation in EP MDD subtype compared with the DP MDD subtype (corrected p <  0.05). There were no significant differences in brain activation between each MDD subtype and the normal controls. CONCLUSION Disparities in sad face processing exist between MDD patients with different TCM syndrome types, suggesting that TCM syndrome differentiation may provide a biological basis for negativity bias in depression, and may determine both symptom formation and social dysfunction.

Importance and objectives Childhood adversity is a strong risk factor for the development of various psychopathologies including major depressive disorder (MDD). However, not all depressed patients experience early life trauma. Functional magnetic resonance imaging (fMRI) studies using facial emotion processing tasks have documented altered blood-oxygen-level-dependent (BOLD) responses in specific cortico-limbic networks both in MDD patients and in individuals with a history of childhood maltreatment (CM). Therefore, a history of maltreatment may represent a key modulating factor responsible for the altered processing of socio-affective stimuli. To test this hypothesis, we recruited MDD patients with and without of maltreatment history to study the long-term consequences of childhood trauma and examined the impact of CM on brain activity using a facial emotion recognition fMRI task. Methods MDD patients with childhood maltreatment (MDD + CM, n = 21), MDD patients without maltreatment (MDD, n = 19), and healthy controls (n = 21) matched for age, sex and intelligence quotient underwent fMRI while performing a block design facial emotion matching task with images portraying negative emotions (fear, anger and sadness). The history of maltreatment was assessed with the 28-item Childhood Trauma Questionnaire. Results Both MDD and MDD + CM patients displayed impaired accuracy to recognize sad faces. Analysis of brain activity revealed that MDD + CM patients had significantly reduced negative BOLD signals in their right accumbens, subcallosal cortex, and anterior paracingulate gyrus compared to controls. Furthermore, MDD + CM patients had a significantly increased negative BOLD response in their right precentral and postcentral gyri compared to controls. We found little difference between MDD and MDD + CM patients, except that MDD + CM patients had reduced negative BOLD response in their anterior paracingulate gyrus relative to the MDD group. Conclusions Our present data provide evidence that depressed patients with a history of maltreatment are impaired in facial emotion recognition and that they display altered functioning of key reward-related fronto-striatal circuits during a facial emotion matching task.

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Introduction Major depressive disorder (MDD) is a severe psychiatric condition with a high risk of suicide. Research on MDD and suicidality has identified structural and functional abnormalities in the cortico-limbic network as candidate biomarkers, but little is known about the temporal dynamics of these brain regions. Recently, abnormal amygdala habituation to emotional stimuli has been highlighted as a reliable fMRI phenotype linked to emotional dysregulation and increased suicide risk. Objectives Our study aimed to assess amygdala habituation to emotional stimuli in MDD and explore differences between suicide attempters (SA) and non-attempters (nSA). Additionally, we examined the relationship between amygdala habituation and depressive symptoms. Methods 414 MDD patients (239 SA, 175 nSA) selected from the UK Biobank underwent fMRI during a block-designed emotion processing task, including faces and shapes conditions. We obtained bilateral amygdala activation for each block using FSL. Habituation was quantified using two methods: the regression approach (REG) and First minus Last block (FmL). One sample T-tests were used to investigate whether habituation rates significantly differed from zero. Group differences were analysed using Mann-Whitney U-tests. Generalized linear models (GLM) were applied to examine relationships between habituation and depression severity, controlling for age, sex, group (SA vs. nSA), and handedness. Results In both MDD and SA groups, no significant habituation was observed for either emotional or non-emotional stimuli (p FDR >.05). However, the nSA group showed significantly positive habituation rates for left amygdala in both conditions and for right amygdala in faces condition using REG (p FDR <.05), suggesting a possible sensitization process. Moreover, nSA showed significantly higher habituation rates than SA in all conditions with REG (p FDR <.01). GLM analyses revealed no significant associations with depression severity. Conclusions Our results suggest that MDD is characterized by a lack of amygdala habituation to emotional stimuli, potentially offering new insights into its pathophysiology. This biomarker may help in developing novel therapeutic strategies targeting the amygdala and its regulation within the cortico-limbic system. Fundings The current study was supported by the Italian Ministry of Health, GR-2019-12370616. Disclosure of Interest None Declared

INTRODUCTION MRI compatible EEG systems enable simultaneous EEG-fMRI data assessment, which provides high spatial and high temporal resolution of neural signaling data. Functional connectivity analyses suggest altered fronto-limbic emotion regulation in patients with major depressive disorder (MDD). METHODS Sixty patients with MDD and 66 healthy controls (HC) performed a priming task using unconsciously and consciously presented emotional facial expressions (happy, sad, neutral) performed a priming task using unconsciously and consciously presented emotional facial expressions. Effective connectivity of simultaneously recorded EEG-fMRI data between cortical (bilateral dorsolateral prefrontal cortex and fusiform gyrus) and subcortical regions (bilateral amygdala) was captured using dynamic causal modeling (DCM). Delineate stimulus-related changes in bottom-up and top-down neurophysiological networks across both EEG and fMRI data were estimated in models of unconscious and conscious processing, defined for both groups. RESULTS Bayesian model selection favored a bottom-up processing model for both groups and input conditions (conscious and unconscious) in EEG-DCMs. Mixed top-down and bottom-up processing models best represented conscious and unconscious stimulus processing in HC fMRI-DCM, while bottom-up models were most representative for MDD fMRI data. Amygdala activity leads to higher DLPFC activity in conscious, and lower DLPFC activity in unconscious conditions in both groups. CONCLUSION This study demonstrates the distinct capabilities of EEG and fMRI data through showing that EEG captures early and fast processing (bottom-up) while fMRI reflects both, bottom-up and top-down regulation. Activity reduction of DLPFC through FFA bottom-up connectivity in early processing (EEG-DCM) might inhibit later top-down emotion regulation through the DLPFC in MDD (fMRI-DCM).

Biased emotion processing has been suggested to underlie the etiology and maintenance of depression. Neuroimaging studies have shown mood-congruent alterations in amygdala activity in patients with acute depression, even during early, automatic stages of emotion processing. However, due to a lack of prospective studies over periods longer than 8 weeks, it is unclear whether these neurofunctional abnormalities represent a persistent correlate of depression even in remission. In this prospective case-control study, we aimed to examine brain functional correlates of automatic emotion processing in the long-term course of depression. In a naturalistic design, n = 57 patients with acute major depressive disorder (MDD) and n = 37 healthy controls (HC) were assessed with functional magnetic resonance imaging (fMRI) at baseline and after 2 years. Patients were divided into two subgroups according to their course of illness during the study period (n = 37 relapse, n = 20 no-relapse). During fMRI, participants underwent an affective priming task that assessed emotion processing of subliminally presented sad and happy compared to neutral face stimuli. A group × time × condition (3 × 2 × 2) ANOVA was performed for the amygdala as region-of-interest (ROI). At baseline, there was a significant group × condition interaction, resulting from amygdala hyperactivity to sad primes in patients with MDD compared to HC, whereas no difference between groups emerged for happy primes. In both patient subgroups, amygdala hyperactivity to sad primes persisted after 2 years, regardless of relapse or remission at follow-up. The results suggest that amygdala hyperactivity during automatic processing of negative stimuli persists during remission and represents a trait rather than a state marker of depression. Enduring neurofunctional abnormalities may reflect a consequence of or a vulnerability to depression.

Considering the complexity of serotonergic influence on emotions, we conducted a comprehensive investigation of the interplay between emotion processing and the serotonergic system using simultaneous functional and molecular neuroimaging during pharmacological challenge while disentangling the effects of serotonin transporter (SERT) binding, genotype, and diagnosis of major depressive disorder (MDD). Herein, 153 subjects (44 with MDD) performed a facial emotion processing task during functional magnetic resonance imaging (fMRI) before and after an acute intravenous application of 8 mg citalopram or placebo. Patients with MDD were assessed again after at least three months of antidepressant treatment. Citalopram administration resulted in a reduced fMRI activation in regions involved in fear processing, including the anterior cingulate cortex (ACC), when viewing fearful faces contrasted against happy or neutral faces. ACC activation correlated negatively with striatal/thalamic SERT availability across drug conditions as measured by [11 C]DASB positron emission tomography. Across groups, citalopram-induced changes in ACC activation correlated with emotional attribution, indicating stronger reductions for subjects with higher self- versus other- attribution. Moreover, striatal SERT availability mediated the influence of the number of 5-HTTLPR/rs25531 LA alleles on ACC activation under placebo. Patients with MDD exhibited increased activations in the intraparietal and superior frontal sulcus in response to fearful versus happy faces at baseline, and along the parieto-occipital/calcarine fissure after treatment. We interpret our findings on multiple levels of the serotonergic-emotional interaction within the context of enhanced passive coping and acute anxiolytic effects of citalopram following potential changes in serotonin or SERT availability.

Introduction Approximately one in six people will experience an episode of major depressive disorder (MDD) in their lifetime. Effective treatment is hindered by subjective clinical decision-making and a lack of objective prognostic biomarkers. Functional MRI (fMRI) could provide such an objective measure but the majority of MDD studies has focused on static approaches, disregarding the rapidly changing nature of the brain. In this study, we aim to predict depression severity changes at 3 and 6 months using dynamic fMRI features. Methods For our research, we acquired a longitudinal dataset of 32 MDD patients with fMRI scans acquired at baseline and clinical follow-ups 3 and 6 months later. Several measures were derived from an emotion face-matching fMRI dataset: activity in brain regions, static and dynamic functional connectivity between functional brain networks (FBNs) and two measures from a wavelet coherence analysis approach. All fMRI features were evaluated independently, with and without demographic and clinical parameters. Patients were divided into two classes based on changes in depression severity at both follow-ups. Results The number of coherence clusters (nCC) between FBNs, reflecting the total number of interactions (either synchronous, anti-synchronous or causal), resulted in the highest predictive performance. The nCC-based classifier achieved 87.5% and 77.4% accuracy for the 3- and 6-months change in severity, respectively. Furthermore, regression analyses supported the potential of nCC for predicting depression severity on a continuous scale. The posterior default mode network (DMN), dorsal attention network (DAN) and two visual networks were the most important networks in the optimal nCC models. Reduced nCC was associated with a poorer depression course, suggesting deficits in sustained attention to and coping with emotion-related faces. An ensemble of classifiers with demographic, clinical and lead coherence features, a measure of dynamic causality, resulted in a 3-months clinical outcome prediction accuracy of 81.2%. Discussion The dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts.

Major depressive disorder (MDD) is a complex mental disorder featured by an increased focus on the self and emotion dysregulation whose interaction remains unclear, though. At the same time, various studies observed abnormal representation of global fMRI brain activity in specifically those regions, e.g., cortical midline structure (CMS) in MDD that are associated with the self. Are the self and its impact on emotion regulation related to global brain activity unevenly represented in CMS relative to non-CMS? Addressing this yet open question is the main goal of our study. We here investigate post-acute treatment responder MDD and healthy controls in fMRI during an emotion task involving both attention and reappraisal of negative and neutral stimuli. We first demonstrate abnormal emotion regulation with increased negative emotion severity on the behavioral level. Next, focusing on a recently established three-layer topography of self, we show increased representation of global fMRI brain activity in specifically those regions mediating the mental (CMS) and exteroceptive (Right temporo-parietal junction and mPFC) self in post-acute MDD during the emotion task. Applying a complex statistical model, namely multinomial regression analyses, we show that increased global infra-slow neural activity in the regions of the mental and exteroceptive self modulates the behavioral measures of specifically negative emotion regulation (emotion attention and reappraisal/suppression). Together, we demonstrate increased representation of global brain activity in regions of the mental and exteroceptive self, including their modulation of negative emotion dysregulation in specifically the infra-slow frequency range (0.01 to 0.1 Hz) of post-acute MDD. These findings support the assumption that the global infra-slow neural basis of the increased self-focus in MDD may take on the role as basic disturbance in that it generates the abnormal regulation of negative emotions.

Major Depressive Disorder (MDD) often is a recurrent and chronic disorder. We investigated the neurocognitive underpinnings of the incremental risk for poor disease course by exploring relations between enduring depression and brain functioning during regulation of negative and positive emotions using cognitive reappraisal. We used fMRI-data from the longitudinal Netherlands Study of Depression and Anxiety acquired during an emotion regulation task in 77 individuals with MDD. Task-related brain activity was related to disease load, calculated from presence and severity of depression in the preceding nine years. Additionally, we explored task related brain-connectivity. Brain functioning in individuals with MDD was further compared to 35 controls to explore overlap between load-effects and general effects related to MDD history/presence. Disease load was not associated with changes in affect or with brain activity, but with connectivity between areas essential for processing, integrating and regulating emotional information during downregulation of negative emotions. Results did not overlap with general MDD-effects. Instead, MDD was generally associated with lower parietal activity during downregulation of negative emotions. During upregulation of positive emotions, disease load was related to connectivity between limbic regions (although driven by symptomatic state), and connectivity between frontal, insular and thalamic regions was lower in MDD (vs controls). Results suggest that previous depressive load relates to brain connectivity in relevant networks during downregulation of negative emotions. These abnormalities do not overlap with disease-general abnormalities and could foster an incremental vulnerability to recurrence or chronicity of MDD. Therefore, optimizing emotion regulation is a promising therapeutic target for improving long-term MDD course.

BACKGROUND Disturbed emotion processing underlies depression. We examined the neuronal underpinnings of emotional processing in patients (PAT) with major depressive disorder (MDD) compared to healthy volunteers (HV) using functional magnetic resonance (fMRI) scan. METHODS Thirty-six MDD patients and 30 HV underwent T2-weighted fMRI assessments during the presentation of an implicit affective processing task in three conditions. They differed regarding their affective quality (=valence, high negative, low negative and neutral stimuli) and regarding the arousal based on stimuli from the International Affective Picture System. RESULTS Group contrasts showed lower left-sided activation in dorsolateral prefrontal cortex (DLPFC), anterior PFC, precentral and premotor cortex in PAT compared with HV (Cluster-level threshold, 5000 iterations, p<0.01). We found a significant interaction effect of valence and group, a significant effect of emotional valence and a significant effect of group. All effects were shown in brain regions within the emotional network (Cluster-level threshold, 5000 iterations, p<0.01). Higher arousal (rho=-0.33, p<0.01) and higher valence (rho=-0.33, p<0.01) during high negative stimuli presentation as well as more severe depression (Beck Depression Inventory II [BDI II]; r=0.39, p=0.01) were significantly negatively associated with left DLFPC activity in patients. LIMITATIONS Potential influence of psychopharmacological drugs on functional activation is one of the most discussed source of bias in studies with medicated psychiatric patients. CONCLUSIONS The results highlight the importance of left DLPFC during the processing of negative emotional stimuli in MDD. The integration of a neurophysiological model of emotional processing in MDD may help to clarify and improve therapeutic options.

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Several previous functional magnetic resonance imaging (fMRI) studies have demonstrated the predictive value of brain activity during emotion processing for antidepressant response, with a focus on clinical outcome after 6–8 weeks. However, longitudinal studies emphasize the paramount importance of early symptom improvement for the course of disease in major depressive disorder (MDD). We therefore aimed to assess whether neural activity during the emotion discrimination task (EDT) predicts early antidepressant effects, and how these predictive measures relate to more sustained response. Twenty-three MDD patients were investigated once with ultrahigh-field 7T fMRI and the EDT. Following fMRI, patients received Escitalopram in a flexible dose schema and were assessed with the Hamilton Depression Rating Scale (HAMD) before, and after 2 and 4 weeks of treatment. Deactivation of the precuneus and posterior cingulate cortex (PCC) during the EDT predicted change in HAMD scores after 2 weeks of treatment. Baseline EDT activity was not predictive of HAMD change after 4 weeks of treatment. The precuneus and PCC are integral components of the default mode network (DMN). We show that patients who exhibit stronger DMN suppression during emotion processing are more likely to show antidepressant response after 2 weeks. This is, to our knowledge, the first study to show that DMN activity predicts early antidepressant effects. However, DMN deactivation did not predict response at 4 weeks, suggesting that our finding is representative of early, likely treatment-related, yet unspecific symptom improvement. Regardless, early effects may be harnessed for optimization of treatment regimens and patient care.

BACKGROUND Altered global signal (GS) topography features in the resting-state fMRI of major depressive disorder (MDD), showing abnormally strong global signal representation in the default-mode network (DMN). Whether the abnormal local to global change also shapes activity during task states, and how it relates to psychopathological symptoms, e.g., abnormally slow time speed of motor, cognitive, and affective symptoms, remains unknown. METHODS We investigated fMRI-based GS with its topographical representation during task states in unmedicated 51 MDD subjects and 28 healthy subjects. Task-related global signal correlation (GSCORR) was probed by a novel paradigm testing the processing of negative/neutral emotions during different time speeds, i.e., slow and fast. RESULTS We observed a significant interaction between time speed and emotion of GSCORR in various DMN regions in healthy subjects. Next, we showed that MDD exhibits reduced task-related GSCORR in various DMN regions during specifically the fast processing of negative emotions. Finally, we demonstrated that GSCORR in DMN and other brain regions (motor-related regions, inferior frontal cortex) correlated with the degree of psychomotor retardation especially during the fast emotional stimuli. LIMITATIONS The measurement of interoceptive variables like respiration rate or heart rate were not included in our fMRI acquisition. CONCLUSION Together, we demonstrated the functional relevance of GS topography by showing reduced GSCORR in DMN during specifically the fast processing of negative emotions in MDD, suggesting the abnormal slowness, i.e., reduced time speed, to be a key feature of both brain and symptoms in MDD.

BACKGROUND Neurobiological predictors of antidepressant response may help guide treatment selection and improve response rates to available treatments for major depressive disorder (MDD). Behavioral activation therapy for depression (BATD) is an evidence-based intervention designed to ameliorate core symptoms of MDD by promoting sustained engagement with value-guided, positively-reinforcing activities. The present study examined pre-treatment task-based functional brain connectivity as a predictor of antidepressant response to BATD. METHODS Thirty-three outpatients with MDD and 20 nondepressed controls completed a positive emotion regulation task during fMRI after which participants with MDD received up to 15 sessions of BATD. We used generalized psychophysiological interaction analyses to examine group differences in pre-treatment functional brain connectivity during intentional upregulation of positive emotion to positive images. Hierarchical linear models were used to examine whether group differences in functional connectivity predicted changes in depression and anhedonia over the course of BATD. RESULTS Compared to controls, participants with MDD exhibited decreased connectivity between the left middle frontal gyrus and right temporoparietal regions during upregulation of positive emotion. Within the MDD group, decreased connectivity of these regions predicted greater declines in anhedonia symptoms over treatment. LIMITATIONS Future studies should include comparison treatments and longitudinal follow-up to clarify the unique effects of BATD on neural function and antidepressant response. CONCLUSIONS Results are consistent with previous work showing BATD may be particularly effective for individuals with greater disturbances in brain reward network function, but extend these findings to highlight the importance of frontotemporoparietal connectivity in targeting symptoms of low motivation and engagement.

Major depressive disorder (MDD) patients demonstrate abnormal neural activation even after complete remission. Many task-related functional magnetic resonance imaging (fMRI) studies have focused on changes in brain function in individuals with remitted MDD (rMDD). We conducted a meta-analysis of these studies to explore differences in brain activation between patients with rMDD and healthy controls (HCs). Our meta-analysis included 13 studies, encompassing 18 experiments, 304 rMDD patients and 321 HCs. Patients with rMDD showed increased neural activation in the left inferior parietal gyrus and right fusiform gyrus and decreased neural activation in the left superior frontal gyrus, right middle temporal gyrus and right Heschl gyrus. Meta-regression analysis revealed that patient age and the number of depressive episodes were negatively associated with brain activity in the left superior frontal gyrus. Our findings suggest abnormal brain function, especially in areas involved in cognitive function, emotion regulation and perception, in rMDD patients; alterations of these regions may be the primary or secondary neurophysiological mechanisms underlying MDD and provide potential neuroimaging targets for early screening.

Pre‐treatment blood oxygenation level‐dependent (BOLD) functional magnetic resonance imaging (fMRI) has been used for the early identification of patients with major depressive disorder (MDD) who later respond or fail to respond to medication. However, BOLD responses early after treatment initiation may offer insight into early neural changes associated with later clinical response. The present study evaluated both pre‐treatment and early post‐treatment fMRI responses to an emotion processing task, to further our understanding of neural changes associated with a successful response to pharmacological intervention.

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Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging approach for studies and novel treatments of major depressive disorder (MDD). EEG performed simultaneously with an rtfMRI-nf procedure allows an independent evaluation of rtfMRI-nf brain modulation effects. Frontal EEG asymmetry in the alpha band is a widely used measure of emotion and motivation that shows profound changes in depression. However, it has never been directly related to simultaneously acquired fMRI data. We report the first study investigating electrophysiological correlates of the rtfMRI-nf procedure, by combining the rtfMRI-nf with simultaneous and passive EEG recordings. In this pilot study, MDD patients in the experimental group (n = 13) learned to upregulate BOLD activity of the left amygdala using an rtfMRI-nf during a happy emotion induction task. MDD patients in the control group (n = 11) were provided with a sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper alpha band and BOLD activity across the brain were examined. Average individual changes in frontal EEG asymmetry during the rtfMRI-nf task for the experimental group showed a significant positive correlation with the MDD patients' depression severity ratings, consistent with an inverse correlation between the depression severity and frontal EEG asymmetry at rest. The average asymmetry changes also significantly correlated with the amygdala BOLD laterality. Temporal correlations between frontal EEG asymmetry and BOLD activity were significantly enhanced, during the rtfMRI-nf task, for the amygdala and many regions associated with emotion regulation. Our findings demonstrate an important link between amygdala BOLD activity and frontal EEG asymmetry during emotion regulation. Our EEG asymmetry results indicate that the rtfMRI-nf training targeting the amygdala is beneficial to MDD patients. They further suggest that EEG-nf based on frontal EEG asymmetry in the alpha band would be compatible with the amygdala-based rtfMRI-nf. Combination of the two could enhance emotion regulation training and benefit MDD patients.

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Major Depressive Disorder (MDD) is characterized by poor emotion regulation. Rumination, a maladaptive strategy for dealing with negative emotions, is common in MDD, and is associated with impaired inhibition and cognitive inflexibility that may contribute to impaired emotion regulation abilities. However, it is unclear whether rumination is differently associated with emotion regulation in individuals with MDD history (MDD-ever) and healthy individuals. In this study, children (8–15 years old) performed a cognitive reappraisal task in which they attempted to decrease their emotional response to sad images during fMRI scanning. Functional connectivity (FC) between both the amygdala and subgenual anterior cingulate (sACC) increased with cortical control regions during reappraisal as rumination increased in MDD-ever, while connectivity between those regions decreased during reappraisal as rumination increased in healthy controls. As the role of cortical control regions is to down-regulate activity of emotion processing regions during reappraisal, this suggests that rumination in MDD-ever, but not controls, is associated with inefficient regulation. This finding suggests that rumination may be particularly associated with poor emotion regulation in MDD-ever, and may also indicate qualitative group differences in whether rumination is maladaptive. These differences in rumination may provide important insight into depressive risk and potential avenues for treatment.

Objective: Alexithymia relates to difficulties recognizing and describing emotions. It has been linked to subjectively increased interoceptive awareness (IA) and to psychiatric illnesses such as major depressive disorder (MDD) and somatization. MDD in turn is characterized by aberrant emotion processing and IA on the subjective as well as on the neural level. However, a link between neural activity in response to IA and alexithymic traits in health and depression remains unclear. Methods: A well-established fMRI task was used to investigate neural activity during IA (heartbeat counting) and exteroceptive awareness (tone counting) in non-psychiatric controls (NC) and MDD. Firstly, comparing MDD and NC, a linear relationship between IA-related activity and scores of the Toronto Alexithymia Scale (TAS) was investigated through whole-brain regression. Secondly, NC were divided by median-split of TAS scores into groups showing low (NC-low) or high (NC-high) alexithymia. MDD and NC-high showed equally high TAS scores. Subsequently, IA-related neural activity was compared on a whole-brain level between the three independent samples (MDD, NC-low, NC-high). Results: Whole-brain regressions between MDD and NC revealed neural differences during IA as a function of TAS-DD (subscale difficulty describing feelings) in the supragenual anterior cingulate cortex (sACC; BA 24/32), which were due to negative associations between TAS-DD and IA-related activity in NC. Contrasting NC subgroups after median-split on a whole-brain level, high TAS scores were associated with decreased neural activity during IA in the sACC and increased insula activity. Though having equally high alexithymia scores, NC-high showed increased insula activity during IA compared to MDD, whilst both groups showed decreased activity in the sACC. Conclusions: Within the context of decreased sACC activity during IA in alexithymia (NC-high and MDD), increased insula activity might mirror a compensatory mechanism in NC-high, which is disrupted in MDD.

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BackgroundThe fundamental mechanism underlying emotional processing in major depressive disorder (MDD) remains unclear. To better understand the neural correlates of emotional processing in MDD, we investigated the role of multiple functional networks (FNs) during emotional stimuli processing.MethodsThirty-two medication-naïve subjects with MDD and 36 healthy controls (HCs) underwent an emotional faces fMRI task that included neutral, happy and fearful expressions. Spatial independent component analysis (sICA) and general linear model (GLM) were conducted to examine the main effect of task condition and group, and two-way interactions of group and task conditions.ResultsIn sICA analysis, MDD patients and HCs together showed significant differences in task-related modulations in five FNs across task conditions. One FN mainly involving the ventral medial prefrontal cortex showed lower activation during fearful relative to happy condition. Two FNs mainly involving the bilateral inferior frontal gyrus and temporal cortex, showed opposing modulation relative to the ventral medial prefrontal cortex FN, i.e., greater activation during fearful relative to happy condition. Two remaining FNs involving the fronto-parietal and occipital cortices, showed reduced activation during both fearful and happy conditions relative to the neutral condition. However, MDD and HCs did not show significant differences in expression-related modulations in any FNs in this sample.ConclusionsSICA revealed differing functional activation patterns than typical GLM-based analyses. The sICA findings demonstrated unique FNs involved in processing happy and fearful facial expressions. Potential differences between MDD and HCs in expression-related FN modulation should be investigated further.

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Major depressive disorder (MDD) is characterized by affective symptoms and cognitive impairments, which have been associated with changes in limbic and prefrontal activity as well as with monoaminergic neurotransmission. A genome-wide association study implicated the polymorphism rs2522833 in the piccolo (PCLO) gene—involved in monoaminergic neurotransmission—as a risk factor for MDD. However, the role of the PCLO risk allele in emotion processing and executive function or its effect on their neural substrate has never been studied. We used functional magnetic resonance imaging (fMRI) to investigate PCLO risk allele carriers vs noncarriers during an emotional face processing task and a visuospatial planning task in 159 current MDD patients and healthy controls. In PCLO risk allele carriers, we found increased activity in the left amygdala during processing of angry and sad faces compared with noncarriers, independent of psychopathological status. During processing of fearful faces, the PCLO risk allele was associated with increased amygdala activation in MDD patients only. During the visuospatial planning task, we found no genotype effect on performance or on BOLD signal in our predefined areas as a function of increasing task load. The PCLO risk allele was found to be specifically associated with altered emotion processing, but not with executive dysfunction. Moreover, the PCLO risk allele appears to modulate amygdala function during fearful facial processing in MDD and may constitute a possible link between genotype and susceptibility for depression via altered processing of fearful stimuli. The current results may therefore aid in better understanding underlying neurobiological mechanisms in MDD.

Abstract Background Cognitive behavioral therapy (CBT) is an effective treatment for many patients suffering from major depressive disorder (MDD), but predictors of treatment outcome are lacking, and little is known about its neural mechanisms. We recently identified longitudinal changes in neural correlates of conscious emotion regulation that scaled with clinical responses to CBT for MDD, using a negative autobiographical memory-based task. Methods We now examine the neural correlates of emotional reactivity and emotion regulation during viewing of emotionally salient images as predictors of treatment outcome with CBT for MDD, and the relationship between longitudinal change in functional magnetic resonance imaging (fMRI) responses and clinical outcomes. Thirty-two participants with current MDD underwent baseline MRI scanning followed by 14 sessions of CBT. The fMRI task measured emotional reactivity and emotion regulation on separate trials using standardized images from the International Affective Pictures System. Twenty-one participants completed post-treatment scanning. Last observation carried forward was used to estimate clinical outcome for non-completers. Results Pre-treatment emotional reactivity Blood Oxygen Level-Dependent (BOLD) signal within hippocampus including CA1 predicted worse treatment outcome. In contrast, better treatment outcome was associated with increased down-regulation of BOLD activity during emotion regulation from time 1 to time 2 in precuneus, occipital cortex, and middle frontal gyrus. Conclusions CBT may modulate the neural circuitry of emotion regulation. The neural correlates of emotional reactivity may be more strongly predictive of CBT outcome. The finding that treatment outcome was predicted by BOLD signal in CA1 may suggest overgeneralized memory as a negative prognostic factor in CBT outcome.

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While the extant literature has focused on major depressive disorder (MDD) as being characterized by abnormalities in processing affective stimuli (e.g., facial expressions), little is known regarding which specific aspects of cognition influence the evaluation of affective stimuli, and what are the underlying neural correlates. To investigate these issues, we assessed 26 adolescents diagnosed with MDD and 37 well-matched healthy controls (HCL) who completed an emotion identification task of dynamically morphing faces during functional magnetic resonance imaging (fMRI). We analyzed the behavioral data using a sequential sampling model of response time (RT) commonly used to elucidate aspects of cognition in binary perceptual decision making tasks: the Linear Ballistic Accumulator (LBA) model. Using a hierarchical Bayesian estimation method, we obtained group-level and individual-level estimates of LBA parameters on the facial emotion identification task. While the MDD and HCL groups did not differ in mean RT, accuracy, or group-level estimates of perceptual processing efficiency (i.e., drift rate parameter of the LBA), the MDD group showed significantly reduced responses in left fusiform gyrus compared to the HCL group during the facial emotion identification task. Furthermore, within the MDD group, fMRI signal in the left fusiform gyrus during affective face processing was significantly associated with greater individual-level estimates of perceptual processing efficiency. Our results therefore suggest that affective processing biases in adolescents with MDD are characterized by greater perceptual processing efficiency of affective visual information in sensory brain regions responsible for the early processing of visual information. The theoretical, methodological, and clinical implications of our results are discussed.

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In our attempt to understand the brain basis of both normal and pathological brain function, the study of brain networks has been foundational (Bassett and Sporns, 2017). The brain is a complex network that can be studied at the level of the gene, protein, synapse, cell, circuit or system. In the search for the brain basis of the vast array of emotion, cognition, perception and action, much recent work has focused on network structures and processes that integrate brain function (Park and Friston, 2013). Clinical work on neural networks has been facilitated by the widespread availability of highresolution magnetic resonance imaging (MRI) and rapid advances in computational tools. The application of graph theory analysis to covariance in structural (sMRI) and functional (fMRI) MRI data has resulted in better understanding of both the local(segregated) and large-scale (global) organization of the brain. This organization provides the brain with its efficiency, robustness, adaptability and resilience. Functional brain networks were initially studied in response to experimental tasks and stimuli (Biswal et al., 1995; Friston, 2002). It was later observed that even when the brain is “at rest,” with the individual lying in the scanner with their eyes closed, certain brain regions are active in organized synchrony (Greicius et al., 2003). Subsequent work showed some brain regions that were active at rest to be synchronously deactivated when attention-demanding tasks were presented. This was labeled as the default mode network (DMN) (Raichle et al., 2001). Anatomically, the DMN has been shown to comprise three main subdivisions: the ventral medial prefrontal cortex (VMPFC), the dorsal medial prefrontal cortex (DMPFC) and the posterior cingulate cortex (PCC) along with the adjacent precuneus and the lateral parietal cortex. The entorhinal cortex is sometimes associated with the DMN (Raichle et al., 2001). These have also been referred to as the anterior and posterior elements of the DMN. The anterior elements have been shown to be involved in emotional processing (VMPFC) and self-referential mental activity (DMPFC), and the posterior elements in memory processes. Notably, these regions are anatomically distant from the sensory and motor systems of the brain (Smallwood et al., 2021). The DMN has a central role in the large-scale functional organization of the brain. It interacts closely with other networks that exhibit patterns of connectivity at rest or during task performance (e.g. executive control, salience, dorsal attention, sensorimotor, visual and auditory networks). Given the central role of the DMN in emotional and cognitive processes, it has been studied in relation to disorders such as depression (Borserio et al., 2021; Drevets et al., 1997) and Alzheimer’s disease (AD) (Buckner et al., 2008) using resting-state fMRI (rs-fMRI). Individuals with depression demonstrate significant differences in patterns of activity in the DMN, both at rest and during task activation (Broyd et al., 2009). Several studies have shown failure of suppression of DMN activity in depressed individuals in response to a variety of stimuli, especially negative pictures (Sheline et al., 2009), and this lack of inhibition was related to depressive self-focus in a meta-analysis (Lemogne et al., 2012). Factors of observable activity in the DMN have been related to the core features of depression, and in a recent study using rs-fMRI, four neurophysiological biotypes of depression were identified which corresponded to clinical-symptom profiles with predominant anhedonia, psychomotor retardation, anxiety and insomnia (Drysdale et al., 2017). Interestingly, a resting-state hypothesis of depression has been proposed (Northoff et al., 2011) in which DMN is placed at the top of the organizational hierarchy in the neurodynamics of the attentional, emotional and sensory networks, and disturbance in the activity in components of the DMN and its ongoing functional connectivity forms the basis for the depressed state. Genetics, developmental factors, trauma, stress, neuroendocrine and other factors influence the connectivity patterns of the DMN and its vulnerability to a depressed state. The PCC, a core posterior region of the DMN, is known to be affected early in AD as seen in functional imaging studies (Minoshima et al., 1997). It has been shown that early in the AD process, there is decreased activity in the PCC and hippocampus, suggesting disrupted connectivity (Greicius et al., International Psychogeriatrics (2022), 34:8, 675–678 © International Psychogeriatric Association 2022

Abstract Social comparisons are a core feature of human life. Theories posit that social comparisons play a critical role in depression and social anxiety triggering negative evaluations about the self, as well as negative emotions. We investigated the neural basis of social comparisons in participants with major depression and/or social anxiety (MD-SA, n = 56) and healthy controls (n = 47) using functional magnetic resonance imaging. While being scanned participants performed a social comparison task, during which they received feedback about their performance and the performance of a coplayer. Upward social comparisons (being worse than the coplayer) elicited high levels of negative emotions (shame, guilt, and nervousness) across participants, with this effect being enhanced in the MD-SA group. Notably, during upward comparison the MD-SA group showed greater activation than the control group in regions of the default mode network (DMN). Specifically, for upward comparison MD-SA participants demonstrated increased activation in the dorsomedial prefrontal cortex and reduced deactivation in the posteromedial cortex, regions linked to self-referential processing, inferences about other people’s thoughts, and rumination. Findings suggest that people with depression and social anxiety react to upward comparisons with a more negative emotional response, which may be linked to introspective processes related to the DMN.

Subclinical depression (ScD), serving as a significant precursor to depression, is a prevalent condition in college students and imposes a substantial health service burden. However, the brain network topology of ScD remains poorly understood, impeding our comprehension of the neuropathology underlying ScD. Functional networks of individuals with ScD (n = 26) and healthy controls (HCs) (n = 33) were constructed based on functional magnetic resonance imaging data. These networks were then optimized using a small-worldness and modular similarity-based network thresholding method to ensure the robustness of functional networks. Subsequently, graph-theoretic methods were employed to investigated both global and nodal topological metrics of these functional networks. Compared to HCs, individuals with ScD exhibited significantly higher characteristic path length, clustering coefficient, and local efficiency, as well as a significantly lower global efficiency. Additionally, significantly lower nodal centrality metrics were found in the default mode network (DMN) regions (anterior cingulate cortex, superior frontal gyrus, precuneus) and occipital lobe in ScD, and the nodal efficiency of the left precuneus was negatively correlated with the severity of depression. Altered global metrics indicate a disrupted small-world architecture and a typical shift toward regular configuration of functional networks in ScD, which may result in lower efficiency of information transmission in the brain of ScD. Moreover, lower nodal centrality in DMN regions suggest that DMN dysfunction is a neuroimaging characteristic shared by both ScD and major depressive disorder, and might serve as a vital factor promoting the development of depression.

Although Postpartum depression (PPD) and PPD with anxiety (PPD‐A) have been well characterized as functional disruptions within or between multiple brain systems, however, how to quantitatively delineate brain functional system irregularity and the molecular basis of functional abnormalities in PPD and PPD‐A remains unclear. Here, brain sample entropy (SampEn), resting‐state functional connectivity (RSFC), transcriptomic and neurotransmitter density data were used to investigate brain functional system irregularity, functional connectivity abnormalities and associated molecular basis for PPD and PPD‐A. PPD‐A exhibited higher SampEn in medial prefrontal cortex (MPFC) and posterior cingulate cortex (PPC) than healthy postnatal women (HPW) and PPD while PPD showed lower SampEn in PPC compared to HPW and PPD‐A. The functional connectivity analysis with MPFC and PPC as seed areas revealed decreased functional couplings between PCC and paracentral lobule and between MPFC and angular gyrus in PPD compared to both PPD‐A and HPW. Moreover, abnormal SampEn and functional connectivity were associated with estrogenic level and clinical symptoms load. Importantly, spatial association analyses between functional changes and transcriptome and neurotransmitter density maps revealed that these functional changes were primarily associated with synaptic signaling, neuron projection, neurotransmitter level regulation, amino acid metabolism, cyclic adenosine monophosphate (cAMP) signaling pathways, and neurotransmitters of 5‐hydroxytryptamine (5‐HT), norepinephrine, glutamate, dopamine and so on. These results reveal abnormal brain entropy and functional connectivities primarily in default mode network (DMN) and link these changes to transcriptome and neurotransmitters to establish the molecular basis for PPD and PPD‐A for the first time. Our findings highlight the important role of DMN in neuropathology of PPD and PPD‐A.

Adolescent major depressive disorder (MDD) is associated with altered resting-state connectivity between the default mode network (DMN) and the salience network (SN), which are involved in self-referential processing and detecting and filtering salient stimuli, respectively. Using spectral dynamical causal modelling, we investigated the effective connectivity and input sensitivity between key nodes of these networks in 30 adolescents with MDD and 32 healthy controls while undergoing resting-state fMRI. We found that the DMN received weaker inhibition from the SN and that the medial prefrontal cortex and the anterior cingulate cortex showed reduced self-inhibition in MDD, making them more prone to external influences. Moreover, we found that selective serotonin reuptake inhibitor (SSRI) intake was associated with decreased and increased self-inhibition of the SN and DMN, respectively, in patients. Our findings suggest that adolescent MDD is characterized by a hierarchical imbalance between the DMN and the SN, which could affect the integration of emotional and self-related information. We propose that SSRIs may help restore network function by modulating excitatory/inhibitory balance in the DMN and the SN. Our study highlights the potential of prefrontal-amygdala interactions as a biomarker and a therapeutic target for adolescent depression.

ImportanceUp to 50% of individuals fail to respond to current depression treatments. Repetitive negative thought and default mode network hyperconnectivity are central in depression and can be targeted using novel neuromodulation techniques. ObjectiveThis study assessed whether non-invasive transcranial focused ultrasound to the default mode network can decrease depression symptoms and repetitive negative thought, and improve quality of life. DesignThis open-label case series began in August 2023, with a six-month follow-up period (current). SettingA community-based study at the University of Arizona. ParticipantsTwenty individuals aged 18 - 45 were enrolled from among 247 screened. Exclusion criteria included history of psychosis/mania, acute suicidality, MRI contraindications, pregnancy, and medical and neurological factors that may complicate diagnosis or brain function. InterventionUp to three weeks of transcranial ultrasound (11 sessions) targeting the anterior medial prefrontal cortex; ten minutes per session. Main Outcomes and MeasuresDepression severity (Beck Depression Inventory - II and the Hamilton Depression Rating Scale), repetitive negative thought (Perseverative Thinking Questionnaire), and quality of life (World Health Organization Quality of Life survey) were outcomes. ResultsThis sample was young (mean 30.4 years {+/-} 10.0), predominantly female (75%), with moderate to severe depression and high comorbidity. Fifty percent of participants endorsed current psychiatric medication use. Ten percent of subjects dropped out of the study. Significant decreases in depression occurred on self-report, 11.3 (p < 0.001, CI = -14.68, -8.15) and interview ratings, 4.3 (p < 0.001, CI = -6.21, -2.43). Repetitive negative thought decreased by 8.53 (p <0.001, CI = -11.01, -5.79). Physical and psychological well-being improved by 7.6 (p < 0.001, CI = 3.62, 11.63) and 11.9 points (p < 0.001, CI = 7.51, 16.21), respectively. Environment satisfaction increased by 5.0 (p = 0.001, CI = 2.24, 7.56). Conclusions and RelevanceTranscranial ultrasound holds promise as a treatment for depression. Trial RegistrationAltering Default Mode Network Activity with Transcranial Focused Ultrasound to Reduce Depressive Symptoms (DMNtFUS). Registration number: 019782-00001 Clinical trials ID: NCT06320028 URL: https://clinicaltrials.gov/study/NCT06320028?intr=Ultrasound&cond=depression&locStr=Arizona&country=United%20States&state=Arizona&rank=1

Abstract The default mode network (DMN) is a network of brain regions active during rest and self-referential thinking. Individuals with major depressive disorder (MDD) show increased or decreased DMN activity relative to controls. DMN activity has been linked to a tendency to ruminate in MDD. It is unclear if individuals who are at risk for, but who have no current or past history of depression, also show differential DMN activity associated with rumination. We investigated whether females with high levels of neuroticism with no current or lifetime mood or anxiety disorders (n = 25) show increased DMN activation, specifically when processing negative self-referential information, compared with females with average levels of neuroticism (n = 28). Participants heard criticism and praise during functional magnetic resonance imaging (MRI) scans in a 3T Siemens Prisma scanner. The at-risk group showed greater activation in two DMN regions, the medial prefrontal cortex and the inferior parietal lobule (IPL), after hearing criticism, but not praise (relative to females with average levels of neuroticism). Criticism-specific activation in the IPL was significantly correlated with rumination. Individuals at risk for depression may, therefore, have an underlying neurocognitive vulnerability to use a brain network typically involved in thinking about oneself to preferentially ruminate about negative, rather than positive, information.

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Highlights • ECT modifies coupling between the hippocampus and the default mode network.• ECT induces microstructural alterations in the parahippocampal cingulum.• ECT reverses reductions in posterior cingulate cortex functional connectivity (FC)• ECT decreases FC between the hippocampus and the posterior cingulate cortex.• ECT increases FC between the hippocampus and the supplementary motor area.

Functional abnormalities of default mode network (DMN) have been well documented in major depressive disorder (MDD). However, the association of DMN functional reorganization with antidepressant treatment and gene expression is unclear. Moreover, whether the functional interactions of DMN could predict treatment efficacy is also unknown. Here, we investigated the link of treatment response with functional alterations of DMN and gene expression with a comparably large sample including 46 individuals with MDD before and after electroconvulsive therapy (ECT) and 46 age- and sex-matched healthy controls. Static and dynamic functional connectivity (dFC) analyses showed increased intrinsic/static but decreased dynamic functional couplings of inter- and intra-subsystems and between nodes of DMN. The changes of static functional connections of DMN were spatially correlated with brain gene expression profiles. Moreover, static and dFC of the DMN before treatment as features could predict depressive symptom improvement following ECT. Taken together, these results shed light on the underlying neural and genetic basis of antidepressant effect of ECT and the intrinsic functional connectivity of DMN have the potential to serve as prognostic biomarkers to guide accurate personalized treatment.

Background/Objective Neuroimaging studies have reported abnormalities in the examination of functional connectivity in late-life depression (LLD) in the default mode network (DMN). The present study aims to study resting-state functional connectivity within the DMN in people diagnosed with late-life major depressive disorder (MDD) compared to healthy controls (HCs). Moreover, we would like to differentiate these same connectivity patterns between participants with high vs. low anxiety levels. Method The sample comprised 56 participants between the ages of 60 and 75; 27 of them were patients with a diagnosis of MDD. Patients were further divided into two samples according to anxiety level: the four people with the highest anxiety level and the five with the lowest anxiety level. Clinical aspects were measured using psychological questionnaires. Each participant underwent functional magnetic resonance imaging (fMRI) acquisition in different regions of interest (ROIs) of the DMN. Results There was a greater correlation between pairs of ROIs in the control group than in patients with LLD, being this effect preferentially observed in patients with higher anxiety levels. Conclusions There are differences in functional connectivity within the DMN depending on the level of psychopathology. This can be reflected in these correlations and in the number of clusters and how the brain lateralizes (clustering).

Recent studies have begun to examine the extent to which signals in the brain correspond to the underlying white matter structure by using tools from the field of graph signal processing to quantify brain function alignment to brain network topology. Here, we apply this framework for the first time towards a transdiagnostic cohort of internalizing psychopathologies, including mood and anxiety disorders, to uncover how such alignment within the default mode network (DMN) is related to depression and rumination symptoms. We found that signal alignment within the posterior DMN is greater in IP patients than healthy controls and is anticorrelated with baseline depression and rumination scales. Signal alignment within the posterior DMN was also found to correlate with the ratio of total within-DMN to extra-DMN functional connectivity for these regions. These findings are consistent with previous literature regarding pathologic promiscuity of posterior DMN connectivity and provide the first GSP-based analyses in a transdiagnostic IP cohort.

Highlights • We ran a meta-analysis of differences of DMN subsystems connectivity in depression.• Connectivity of the DMN Core is slightly reduced in depression.• This difference is small, variable and heterogeneous across sites.• Rumination does not predict DMN subsystem functional connectivity.

Default mode network (DMN) connectivity is altered in depression. We evaluated the relationship between changes in within‐network DMN connectivity and improvement in depression in a subsample of our parent clinical trial comparing escitalopram/memantine (ESC/MEM) to escitalopram/placebo (ESC/PBO) in older depressed adults (NCT01902004).

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Two different but interacting neural systems exist in the human brain: the task positive networks and task negative networks. One of the most important task positive networks is the central executive network (CEN), while the task negative network generally refers to the default mode network (DMN), which usually demonstrates task-induced deactivation. Although previous studies have clearly shown the association of both the CEN and DMN with major depressive disorder (MDD), how the causal interactions between these two networks change in depressed patients remains unclear. In the current study, 99 subjects (43 patients with MDD and 56 healthy controls) were recruited with their resting-state fMRI data collected. After data preprocessing, spectral dynamic causal modeling (spDCM) was used to investigate the causal interactions within and between the DMN and CEN. Group commonalities and differences in causal interaction patterns within and between the CEN and DMN in patients and controls were assessed by a parametric empirical Bayes (PEB) model. Both subject groups demonstrated significant effective connectivity between regions of the CEN and DMN. In particular, we detected inhibitory influences from the CEN to the DMN with node-level PEB analyses, which may help to explain the anticorrelations between these two networks consistently reported in previous studies. Compared with healthy controls, patients with MDD showed increased effective connectivity within the CEN and decreased connectivity from regions of the CEN to DMN, suggesting impaired control of the DMN by the CEN in these patients. These findings might provide new insights into the neural substrates of MDD.

Patients with depression who ruminate repeatedly focus on depressive thoughts; however, there are two cognitive subtypes of rumination, reflection and brooding, each associated with different prognoses. Reflection involves problem-solving and is associated with positive outcomes, whereas brooding involves passive, negative, comparison with other people and is associated with poor outcomes. Rumination has also been related to atypical functional hyperconnectivity between the default mode network and subgenual prefrontal cortex. Repetitive pulse transcranial magnetic stimulation of the prefrontal cortex has been shown to alter functional connectivity, suggesting that the abnormal connectivity associated with rumination could potentially be altered. This study examined potential repetitive pulse transcranial magnetic stimulation prefrontal cortical targets that could modulate one or both of these rumination subtypes. Forty-three patients who took part in a trial of repetitive pulse transcranial magnetic stimulation completed the Rumination Response Scale questionnaire and resting-state functional magnetic resonance imaging. Seed to voxel functional connectivity analyses identified an anticorrelation between the left lateral orbitofrontal cortex (−44, 26, −8; k = 172) with the default mode network-subgenual region in relation to higher levels of reflection. Parallel analyses were not significant for brooding or the RRS total score. These findings extend previous studies of rumination and identify a potential mechanistic model for symptom-based neuromodulation of rumination.

ABSTRACT Objectives: Patients with geriatric depression exhibit a spectrum of symptoms ranging from mild to severe cognitive impairment which could potentially lead to the development of Alzheimer’s disease (AD). The aim of the study is to assess the alterations of the default mode network (DMN) in remitted geriatric depression (RGD) patients and whether it could serve as an underlying neuropathological mechanism associated with the risk of progression of AD. Design: Cross-sectional study. Participants: A total of 154 participants, comprising 66 RGD subjects (which included 27 patients with comorbid amnestic mild cognitive impairment [aMCI] and 39 without aMCI [RGD]), 45 aMCI subjects without a history of depression (aMCI), and 43 matched healthy comparisons (HC), were recruited. Measurements: All participants completed neuropsychological tests and underwent resting-state functional magnetic resonance imaging (fMRI). Posterior cingulate cortex (PCC)-seeded DMN functional connectivity (FC) along with cognitive function were compared among the four groups, and correlation analyses were conducted. Results: In contrast to HC, RGD, aMCI, and RGD-aMCI subjects showed significant impairment across all domains of cognitive functions except for attention. Furthermore, compared with HC, there was a similar and significant decrease in PCC-seed FC in the bilateral medial superior frontal gyrus (M-SFG) in the RGD, aMCI, and RGD-aMCI groups. Conclusions: The aberrations in rsFC of the DMN were associated with cognitive deficits in RGD patients and might potentially reflect an underlying neuropathological mechanism for the increased risk of developing AD. Therefore, altered connectivity in the DMN could serve as a potential neural marker for the conversion of geriatric depression to AD.

Background Exposure to maternal major depressive disorder (MDD) bears long-term negative consequences for children's well-being; to date, no research has examined how exposure at different stages of development differentially affects brain functioning. Aims Utilising a unique cohort followed from birth to preadolescence, we examined the effects of early versus later maternal MDD on default mode network (DMN) connectivity. Method Maternal depression was assessed at birth and ages 6 months, 9 months, 6 years and 10 years, to form three groups: children of mothers with consistent depression from birth to 6 years of age, which resolved by 10 years of age; children of mothers without depression; and children of mothers who were diagnosed with MDD in late childhood. In preadolescence, we used magnetoencephalography and focused on theta rhythms, which characterise the developing brain. Results Maternal MDD was associated with disrupted DMN connectivity in an exposure-specific manner. Early maternal MDD decreased child connectivity, presenting a profile typical of early trauma or chronic adversity. In contrast, later maternal MDD was linked with tighter connectivity, a pattern characteristic of adult depression. Aberrant DMN connectivity was predicted by intrusive mothering in infancy and lower mother–child reciprocity and child empathy in late childhood, highlighting the role of deficient caregiving and compromised socio-emotional competencies in DMN dysfunction. Conclusions The findings pinpoint the distinct effects of early versus later maternal MDD on the DMN, a core network sustaining self-related processes. Results emphasise that research on the influence of early adversity on the developing brain should consider the developmental stage in which the adversity occured.

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Major depression is associated with altered static functional connectivity in various brain networks, particularly the default mode network (DMN). Dynamic functional connectivity is a novel tool with little application in affective disorders to date, and holds the potential to unravel fluctuations in connectivity strength over time in major depression. We assessed stability of connectivity in major depression between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), key nodes in the DMN that are implicated in ruminative cognitions. Functional connectivity stability between the mPFC and PCC over the course of a resting-state functional magnetic resonance imaging (fMRI) scan was compared between medication-free patients with major depression and healthy controls matched for age, sex and handedness. We tested replicability of the results in an independent sample using multi-echo resting-state fMRI. The primary sample included 20 patients and 19 controls, while the validation sample included 19 patients and 19 controls. Greater connectivity variability was detected in major depression between mPFC and PCC. This was demonstrated in both samples indicating that the results were reliable and were not influenced by the fMRI acquisition approach used. Our results demonstrate that alterations within the DMN in major depression go beyond changes in connectivity strength and extend to reduced connectivity stability within key DMN regions. Findings were robustly replicated across two independent samples. Further research is necessary to better understand the nature of these fluctuations in connectivity and their relationship to the aetiology of major depression.

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Major depressive disorder (MDD) has been a prominent topic in recent years due to its unknown etiology and pathology, high prevalence rate, and the high cost of treatment. Due to its high resolution for soft tissue, magnetic resonance imaging (MRI) has become an essential noninvasive tool for the evaluation of the brain substrates underlying mental disorders. MRI enables characterization of brain morphology and function in MDD patients. Compared to healthy controls, MDD patients have structural changes in certain brain regions such as the prefrontal cortex, cingulate cortex, precuneus, thalamus, and hippocampus. Abnormal brain functions as indicated by various MRI measurements including region homogeneity, the amplitude of low-frequency fluctuation, functional connectivity, and mean kurtosis may also contribute to the pathogenesis of MDD. This mini-review summarizes recent MRI findings on the neural manifestations of MDD. We discuss the potential of MRI biomarkers that may prove clinically useful for early diagnosis and evaluation of treatment outcomes for depression.

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

Magnetic resonance imaging (MRI) has been recognized as a valuable tool for achieving 'reification of clinical diagnosis' of major depressive disorder (MDD). However, the reliability and validity of MRI results are often compromised by genetic, environmental, and clinical heterogeneity within test samples. Here, we combined MRI with other clinical findings using multimodal MRI fusion algorithm to construct a data-driven, bottom-up diagnostic approach. The covariation patterns between the multimodal MRI features and differential expression of exosomal microRNA (miRNA) were identified on a subset of 70 MDD patients and 71 healthy controls (HCs) (served as a training set) as classification features, whereas data from the other 45 MDD patients and 43 HCs served as a test set. Furthermore, longitudinal data from 28 MDD patients undergoing antidepressant treatment for six months were utilized to validate the identified biomarkers, and related signaling pathways were initially explored in depression-like mice. Plasma exosome-derived miR-151a-3p levels were found to be significantly lower in MDD patients compared to HCs and correlated with abnormal changes in functional MRI (fMRI) metrics in the anterior cingulate cortex (ACC), visual cortex, and default mode network, etc. Then, these multimodal MRI features associated with miR-151a-3p expression distinguished MDD patients from HCs with high classification accuracy of 92.05% in support vector machine (SVM) model, outperforming the diagnostic rate when only multimodal MRI features with intergroup differences were entered (70.45%). Furthermore, 10 out of 28 MDD patients exhibited a clinically significant response to the treatment (a reduction of over 50% in Hamilton Rating Scale for Depression (HAMD) score). The significant upregulation of plasma exosomal miR-151a-3p levels and changes of fMRI indicators were also observed in these 10 patients after treatment of six months. Animal experiments have shown that reducing the expression of miR-151-3p in ACC induces depression-like behaviors in mice, while elevating hsa-miR-151a-3p expression in ACC alleviates the depression-like behaviors of mice exposed to chronic unpredictable mild stress. Our study proposed an innovative diagnostic model of MDD by combining the plasma exosome-derived miR-151a-3p expression with its associated multimodal MRI patterns, potentially serving as a novel diagnostic tool.

Magnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA). Seventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus. The primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r The results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

Anhedonia is a core symptom of major depressive disorder (MDD). Two subtypes of anhedonia: anticipatory anhedonia and consummatory anhedonia has been recognized in MDD patients. However, our knowledge regarding the distinction of anticipatory anhedonia and consummatory anhedonia in MDD remains limited. This study aimed to characterize the anticipatory anhedonia and consummatory anhedonia in first-episode, drug-naïve MDD patients. Resting-state functional MRI and T1-structural MRI were acquired for 38 MDD patients and 65 matched healthy controls (HCs). The ALFF and cortical surface indexes were compared between MDD and HCs. Then the correlations between the ALFF and cortical surface indexes alternations and the scores of anticipatory and consummatory pleasure measured by Temporal Experience of Pleasure Scale were evaluated. The elevated ALFF of left dorsal anterior cingulate cortex (dACC) and the reduced cortical thickness (CT) of left rostral ACC and lateral orbitofrontal cortex (lOFC) were respectively correlated with anticipatory anhedonia and consummatory anhedonia in MDD patients. These findings suggested the dissociated pathophysiological basis and imaging characteristics of anticipatory anhedonia and consummatory anhedonia. The ALFF and CT values of ACC and lOFC might serve as the imaging biomarker of the subtypes of anhedonia in early onset of MDD.

Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional connectivity (FC) patterns within the brain. However, whether FC could be used as a potential biomarker for diagnosis of adolescent MDD is still unclear. The aim of our study was to investigate the potential diagnostic value of whole-brain FC in adolescent MDD. Resting-state functional magnetic resonance imaging data were obtained from 94 adolescents with MDD and 78 healthy adolescents. The whole brain was segmented into 90 regions of interest (ROIs) using the automated anatomical labeling atlas. FC was assessed by calculating the Pearson correlation coefficient of the average time series between each pair of ROIs. A multivariate pattern analysis was employed to classify patients from controls using the whole-brain FC as input features. The linear support vector machine classifier achieved an accuracy of 69.18% using the optimal functional connection features. The consensus functional connections were mainly located within and between large-scale brain networks. The top 10 nodes with the highest weight in the classification model were mainly located in the default mode, salience, auditory, and sensorimotor networks. Our findings highlighted the importance of functional network connectivity in the neurobiology of adolescent MDD, and suggested the possibility of altered FC and high-weight regions as complementary diagnostic markers in adolescents with depression.

The hypothalamus is a limbic structure involved in the emergence and persistence of major depressive disorder symptoms. Previous studies have indicated that major depressive disorder patients exhibited dysregulation between the hypothalamus and cerebral regions. However, it is still unclear about the exact hypothalamic functional connectivity patterns with other brain regions based on resting-state functional MRI in major depressive disorder. Here, we investigated the whole-brain voxel-based hypothalamic resting-state functional connectivity in 55 patients with major depressive disorder and 40 age sex-matched healthy controls. The results showed that major depressive disorder patients had a significant decrease in resting-state functional connectivity of the bilateral hypothalamus with the right insula, superior temporal gyrus, inferior frontal gyrus, and Rolandic operculum compared with healthy controls. This study suggests that the pathophysiology of major depressive disorder might be associated with the abnormal hypothalamic resting-state functional connectivity.

Rumination is closely linked to the onset and maintenance of major depressive disorder (MDD). Prior neuroimaging studies have identified the association between self-reported rumination trait and the functional coupling among a network of brain regions using resting-state functional magnetic resonance imaging (MRI). However, little is known about the underlying neural circuitry mechanism during active rumination in MDD. Degree centrality (DC) is a simple metric to denote network integration, which is critical for higher-order psychological processes such as rumination. During an MRI scan, individuals with MDD (N = 45) and healthy controls (HC, N = 46) completed a rumination state task. We examined the interaction effect between the group (MDD vs. HC) and condition (rumination vs. distraction) on vertex-wise DC. We further characterized the identified brain region's functional involvement with Neurosynth and BrainMap. Network-wise seed-based functional connectivity (FC) analysis was also conducted for the identified region of interest. Finally, exploratory correlation analysis was conducted between the identified region of interest's network FCs and self-reported in-scanner affect levels. We found that a left superior frontal gyrus (SFG) region, generally overlapped with the frontal eye field, showed a significant interaction effect. Further analysis revealed its involvement with executive functions. FCs between this region, the frontoparietal, and the dorsal attention network (DAN) also showed significant interaction effects. Furthermore, its FC to DAN during distraction showed a marginally significant negative association with in-scanner affect level at the baseline. Our results implicated an essential role of the left SFG in the rumination's underlying neural circuitry mechanism in MDD and provided novel evidence for the conceptualization of rumination in terms of impaired executive control.

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Major depressive disorder (MDD) in children and adolescents is a growing global public health concern. Metabolic alterations in the microbiota-gut-brain (MGB) axis have been implicated in MDD pathophysiology, but their specific role in pediatric populations remains unclear. We conducted a multi-omics study on 256 MDD patients and 307 healthy controls in children and adolescents, integrating plasma metabolomics, fecal metagenomics, and resting-state functional magnetic resonance imaging (rs-fMRI) of the brain. KEGG enrichment analysis of 360 differential expressed metabolites (DEMs) indicated significant plasma amino acid (AA) metabolism deficiencies (p-value < 0.0001). We identified 58 MDD-enriched and 46 MDD-depleted strains, as well as 6 altered modules in amino acid metabolism in fecal metagenomics. Procrustes analysis revealed the association between the altered gut microbiome and circulating AA metabolism (p-value = 0.001, M Our findings highlight that gut microbiota alterations contribute to AAs deficiency, particularly lysine, which plays a crucial role in MDD pathogenesis in children and adolescents. Targeting AA metabolism may offer novel therapeutic strategies for pediatric depression. Video Abstract.

Major depressive disorder (MDD) is characterized by a substantial burden on health, including changes in appetite and body weight. Heterogeneity of depressive symptoms has hampered the identification of biomarkers that robustly generalize to most patients, thus calling for symptom-based mapping. To define the functional architecture of the reward circuit subserving increases vs decreases in appetite and body weight in patients with MDD by specifying their contributions and influence on disease biomarkers using resting-state functional connectivity (FC). In this case-control study, functional magnetic resonance imaging (fMRI) data were taken from the Marburg-Münster FOR 2107 Affective Disorder Cohort Study (MACS), collected between September 2014 and November 2016. Cross-sectional data of patients with MDD (n = 407) and healthy control participants (n = 400) were analyzed from March 2018 to June 2022. Changes in appetite during the depressive episode and their association with FC were examined using fMRI. By taking the nucleus accumbens (NAcc) as seed of the reward circuit, associations with opposing changes in appetite were mapped, and a sparse symptom-specific elastic-net model was built with 10-fold cross-validation. Among 407 patients with MDD, 249 (61.2%) were women, and the mean (SD) age was 36.79 (13.4) years. Reduced NAcc-based FC to the ventromedial prefrontal cortex (vmPFC) and the hippocampus was associated with reduced appetite (vmPFC: bootstrap r = 0.13; 95% CI, 0.02-0.23; hippocampus: bootstrap r = 0.15; 95% CI, 0.05-0.26). In contrast, reduced NAcc-based FC to the insular ingestive cortex was associated with increased appetite (bootstrap r = -0.14; 95% CI, -0.24 to -0.04). Critically, the cross-validated elastic-net model reflected changes in appetite based on NAcc FC and explained variance increased with increasing symptom severity (all patients: bootstrap r = 0.24; 95% CI, 0.16-0.31; patients with Beck Depression Inventory score of 28 or greater: bootstrap r = 0.42; 95% CI, 0.25-0.58). In contrast, NAcc FC did not classify diagnosis (MDD vs healthy control). In this study, NAcc-based FC reflected important individual differences in appetite and body weight in patients with depression that can be leveraged for personalized prediction. However, classification of diagnosis using NAcc-based FC did not exceed chance levels. Such symptom-specific associations emphasize the need to map biomarkers onto more confined facets of psychopathology to improve the classification and treatment of MDD.

Patients with major depressive disorder (MDD) exhibit concurrent deficits in both sensory and higher-order cognitive processing. Connectome studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this gradient structure is disrupted in patients with MDD and how this disruption associates with gene expression profiles and treatment outcome remain unknown. Using a large cohort of resting-state fMRI data from 2227 participants (1148 MDD patients and 1079 healthy controls) recruited at nine sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth, postmortem gene expression, and an 8-week antidepressant treatment (20 MDD patients) data to assess the meta-analytic cognitive functions, transcriptional profiles, and treatment outcomes related to MDD gradient alterations, respectively. Relative to the controls, MDD patients exhibited global topographic alterations in the principal primary-to-transmodal gradient, including reduced explanation ratio, gradient range, and gradient variation (Cohen's d = 0.16-0.21), and focal alterations mainly in the primary and transmodal systems (d = 0.18-0.25). These gradient alterations were significantly correlated with meta-analytic terms involving sensory processing and higher-order cognition. The transcriptional profiles explained 53.9% variance of the altered gradient pattern, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. The baseline gradient maps of patients significantly predicted symptomatic improvement after treatment. These results highlight the connectome gradient dysfunction in MDD and its linkage with gene expression profiles and clinical management, providing insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

To achieve greater clinical efficacy, a revolution in treatment for major depressive disorder (MDD) is highly anticipated. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive and safe neuromodulation technique that immediately changes brain activity. Despite its wide application in the treatment for MDD, the treatment response remains different among individuals, which may be attributable to the inaccurate positioning of the stimulation target. Our study aims to examine whether the functional magnetic resonance imaging (fMRI)-assisted positioning improves the efficacy of rTMS in treating depression. We intend to identify and stimulate the subregion of dorsolateral prefrontal cortex (DLPFC) in MDD with strongest anti-correlation with the subgenual anterior cingulate cortex (sgACC), and to conduct a comparative investigation of this novel method and the traditional 5-cm rule. To achieve more precise stimulation, both methods were applied under the guidance of neuronavigation system. We expected that the TMS treatment with individualized positioning based on resting state functional connectivity may show better clinical efficacy than the 5-cm method.

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Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression. To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables. This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022. Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status. A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables. Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.

Overgeneralised self-blame and worthlessness are key symptoms of major depressive disorder (MDD) and have previously been associated with self-blame-selective changes in connectivity between right superior anterior temporal lobe (rSATL) and subgenual frontal cortices. Another study showed that remitted MDD patients were able to modulate this neural signature using functional magnetic resonance imaging (fMRI) neurofeedback training, thereby increasing their self-esteem. The feasibility and potential of using this approach in symptomatic MDD were unknown. This single-blind pre-registered randomised controlled pilot trial probed a novel self-guided psychological intervention with and without additional rSATL-posterior subgenual cortex (BA25) fMRI neurofeedback, targeting self-blaming emotions in people with insufficiently recovered MDD and early treatment-resistance ( As predicted, neurofeedback led to a training-induced reduction in rSATL-BA25 connectivity for self-blame These findings suggest that self-blame-rebalance neurofeedback may be superior over a solely psychological intervention in non-anxious MDD, although further confirmatory studies are needed. Simple self-guided strategies tackling self-blame were beneficial, but need to be compared against treatment-as-usual in further trials. https://doi.org/10.1186/ISRCTN10526888.

Repetitive negative thinking (RNT) is a cognitive process focusing on self-relevant and negative experiences, leading to a poor prognosis of major depressive disorder (MDD). We previously identified that connectivity between the precuneus/posterior cingulate cortex (PCC) and right temporoparietal junction (rTPJ) was positively correlated with levels of RNT. In this double-blind, randomized, sham-controlled, proof-of-concept trial, we employed real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) to delineate the neural processes that may be causally linked to RNT and could potentially become treatment targets for MDD. MDD-affected individuals were assigned to either active (n = 20) or sham feedback group (n = 19). RNT was measured by the Ruminative Response Scale-brooding subscale (RRS-B) before and 1 week after the intervention. Individuals in the active but not in the sham group showed a significant reduction in the RRS-B; however, a greater reduction in the PCC-rTPJ connectivity was unrelated to a greater reduction in the RRS-B. Exploratory analyses revealed that a greater reduction in the retrosplenial cortex (RSC)-rTPJ connectivity yielded a more pronounced reduction in the RRS-B in the active but not in the sham group. RtfMRI-nf was effective in reducing RNT. Considering the underlying mechanism of rtfMIR-nf, the RSC and rTPJ could be part of a network (i.e., default mode network) that might collectively affect the intensity of RNT. Understanding the relationship between the functional organization of targeted neural changes and clinical metrics, such as RNT, has the potential to guide the development of mechanism-based treatment of MDD.

Anterior cingulate cortex (ACC) plays an essential role in the pathophysiology of major depressive disorder (MDD) and its treatment. However, it's still unclear whether the effects of disease and antidepressant treatment on ACC perform diversely in neural mechanisms. Fifty-nine MDD patients completed resting-state fMRI scanning twice at baseline and after 12-week selective serotonin reuptake inhibitor (SSRI) treatment, respectively in acute state and remission state. Fifty-nine demographically matched healthy controls were enrolled. Using fractional amplitude of low-frequency fluctuation (fALFF) in ACC as features, we performed multi-voxel pattern analysis over pretreatment MDD patients vs health control (HC), and over pretreatment MDD patients vs posttreatment MDD patients. Discriminative regions in ACC for MDD impairment and changes after antidepressants were obtained. The intersection set and difference set were calculated to form ACC subregions of recovered, unrecovered and compensative, respectively. The recovered ACC subregion mainly distributed in rostral ACC (80 %) and the other two subregions had nearly equal distribution over dorsal ACC and rostral ACC. Furthermore, only the compensative subregion had significant changed functional connectivity with cingulo-opercular control network (CON) after antidepressant treatment. The number of subjects was relatively small. The results need to be validated with larger sample sizes and multisite data. This finding suggested that the local function of ACC was partly recovered on regulating emotion after antidepressant by detecting the common subregional targets of depression impairment and antidepressive effect. Besides, changed fALFF in the compensative ACC subregion and its connectivity with CON may partly compensate for the cognition deficits.

Major depressive disorder is strongly associated with impairments and difficulties in social interactions. Deficits in empathy, a vital skill for social interactions, have been identified as a risk factor for relapse. However, research on empathy in remitted states of depression is scarce. We chose a social neuroscience approach to investigate potentially altered neural processes involved in sub-components of empathy in remitted states of depression. We expected aberrations in cognitive components of empathy, based on previous reports regarding their role as risk factors for relapse. Employing functional magnetic resonance imaging and a pain empathy task (video clips of painful medical treatments), we compared behavioral and neural empathic responses of unmedicated remitted depressive patients (N = 32) to those of untreated acutely depressed patients (N = 29) and healthy controls (N = 35). Self-report ratings of pain evaluation and affect-sharing were obtained. Compared to controls and acutely depressed patients, remitted depressive patients reported higher pain evaluation and showed increased activity in the right temporo-parietal junction. This region, which is central to self-other distinction and which has been linked to adopting a detached perspective, also exhibited reduced connectivity to the anterior insula. Furthermore, we observed reduced activity in regions involved in emotion processing (amygdala) and perception of affective facial expressions (fusiform face area, posterior superior temporal sulcus). Remitted states of depression are associated with a detached empathic style in response to others' pain, characterized by increased self-other distinction, lowered affective processing, and reduced connectivity between empathy-related brain regions. Although this may prevent emotional harm in specific situations, it may reduce opportunities for positive experiences in social interactions in the long run.

The underlying mechanism of pain-depression comorbidity is not well understood. This study aims to analyze the abnormal brain activity in adolescents with pain-depression comorbidity, and to provide imaging evidence for the understanding of brain neural mechanisms of pain-depression comorbidity. Depression in adolescents with ( Compared with the depression group, the comorbidity group showed increased ALFF values in the right amygdala and right middle frontal gyrus, decreased ALFF values in the left middle occipital gyrus, right inferior temporal gyrus and right superior temporal gyrus, increased ReHo values in the right insula, and elevated functional connectivity between the right inferior temporal gyrus and right angular gyrus (Gaussian random field [GRF] corrected, Compared with adolescents with depression without pain, adolescents with pain-depression comorbidity have differences in neuronal activity and functional connectivity in the middle frontal gyrus, amygdala, insular lobe, temporoparietal and occipital lobes, suggesting that abnormal neuronal activity in these brain regions may be the neural basis of pain-depression comorbidity.

The nucleus accumbens (NAc) is considered a hub of reward processing and a growing body of evidence has suggested its crucial role in the pathophysiology of major depressive disorder (MDD). However, inconsistent results have been reported by studies on reward network-focused resting-state functional MRI (rs-fMRI). In this study, we examined functional alterations of the NAc-based reward circuits in patients with MDD via meta- and mega-analysis. First, we performed a coordinated-based meta-analysis with a new SDM-PSI method for all up-to-date rs-fMRI studies that focused on the reward circuits of patients with MDD. Then, we tested the meta-analysis results in the REST-meta-MDD database which provided anonymous rs-fMRI data from 186 recurrent MDDs and 465 healthy controls. Decreased functional connectivity (FC) within the reward system in patients with recurrent MDD was the most robust finding in this study. We also found disrupted NAc FCs in the DMN in patients with recurrent MDD compared with healthy controls. Specifically, the combination of disrupted NAc FCs within the reward network could discriminate patients with recurrent MDD from healthy controls with an optimal accuracy of 74.7%. This study confirmed the critical role of decreased FC in the reward network in the neuropathology of MDD. Disrupted inter-network connectivity between the reward network and DMN may also have contributed to the neural mechanisms of MDD. These abnormalities have potential to serve as brain-based biomarkers for individual diagnosis to differentiate patients with recurrent MDD from healthy controls.

An enduring mystery in neuroscience is the intricate interplay between brain anatomical structure and functional dynamics, particularly in the context of mental disorders such as major depressive disorder (MDD). A pivotal scientific question arises: How does the cortical morphology-function coupling (MFC) manifest in MDD, and what insights can this coupling provide into the clinical manifestations of the disorder? To tackle this question, we conducted a comprehensive analysis using high-resolution T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional MRI (rs-fMRI) data from a cohort of 830 MDD patients and 853 healthy control (HC). By constructing morphological and functional networks based on cortical gray matter (GM) morphology and regional rs-fMRI time series correlations, respectively, we aimed to quantify MFC by assessing the spatial correspondence between these networks. Results revealed that MDD patients exhibited a spatial hierarchical pattern of MFC similar to HC, with variations in specific networks. Specifically, lower coupling was observed in the visual network (VIS) and sensorimotor network (SMN), while higher coupling was noted in the default mode network (DMN) and frontoparietal network (FPN). Notably, MDD patients demonstrated significantly increased MFC within the VIS, SMN, and dorsal attention network (DAN) compared to HC. Furthermore, altered MFC in the VIS correlated positively with depressive symptom severity. These findings contribute to our understanding of the potential clinical significance of MFC alterations in MDD.

Increasing evidence has shown that the microbiota-gut-brain axis (MGB) is involved in the mechanism of major depressive disorder (MDD). However, the relationship between the gut microbiome and brain function in MDD patients has not been determined. Here, we intend to identify specific changes in the gut microbiome and brain function in first-episode, drug-naïve MDD patients and then explore the associations between the two omics to elucidate how the MGB axis plays a role in MDD development. We recruited 38 first-episode, drug-naïve MDD patients and 37 healthy controls (HC). The composition of the fecal microbiome and neural spontaneous activity alterations were examined using 16S rRNA gene amplicon sequencing analysis and regional homogeneity (ReHo). Spearman correlation analyses were conducted to assess the associations between the gut microbiome and brain function. Compared with HC, MDD patients exhibited distinct alterations in the gut microbiota and elevated ReHo in the frontal regions. In the MDD group, a positive relationship was noted between the relative abundance of Blautia and the HAMD-17 and HAMA scores, as well as between the relative abundance of Oxalobacteraceae and the HAMD-17 score. The relative abundances of Porphyromonadaceae and Parabacteroides were negatively correlated with the ReHo values of frontal regions. Our study utilized a cross-sectional design, and the number of subjects was relatively small. We found that some specific gut microbiomes were associated with frontal function, and others were associated with clinical symptoms in MDD patients, which may support the role of the MGB axis underlying MDD.

Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.

Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.

Functional abnormalities in different brain regions are related to major depressive disorder (MDD). In our previous study, we demonstrated that DNA methylation of Tryptophan Hydroxylase-2 (TPH2) is related to the occurrence of MDD. The present study aimed to identify the interaction of the functional activities of brain regions identified as regions of interest (RoI) in MDD with TPH2 gene methylation to explore their relationship. Data from 98 patients with MDD and 63 controls were utilized. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and fractional ALFF (fALFF), were used to identify ROIs regions in the RESTPlus Software of MATLAB. General linear regression (GLM) was performed to analyze the association between functional connectivity (FC) found in rs-fMRI and the effect of TPH2 DNA methylation in patients with MDD and controls. In the rs-fMRI analysis, the ALFF of right superior generalized gyrus (STG) was significantly different between the MDD and HCs groups (p < 0.05). The ReHo of right Middle temporal gyrus (MTG) and left middle occipital gyrus (MOG) were significantly different between the two groups (p < 0.05). These ROIs were used to further analyze the FC differences between MDD and HCs, and it was found that the FC of right STG and right superior frontal gyrus (SFG) and the FC of right MTG and right MOG were significantly different between the two groups (p < 0.05). It was further found that the interaction between ALFF activity of the right STG and TPH2-5-203 methylation (β=-2.108, p = 0.004), ReHo activity level of the right MTG, and TPH2-5-203 methylation were correlated with the occurrence of MDD (β=-1.720, p = 0.018). This study found that the functional activities of the temporal lobe, middle occipital gyrus, and superior frontal gyrus were abnormal in patients with MDD compared to HCs. Furthermore, the interaction of functional activities of the right superior temporal gyrus /middle temporal gyrus and TPH2 methylation were associated with the occurrence of MDD, suggesting that the combination of functional activities and DNA methylation was helpful for diagnosis of MDD.

Anhedonia is a core feature of depression. It contains a consummatory and a motivational aspect. Whilst much neuroimaging research in patients with depression focused on the consummatory aspect of anhedonia, less is known about its motivational aspect. This study aimed to explore the neurobiology of networks related to motivational anhedonia. Thirty-eight patients with major depressive disorder (MDD) and 19 healthy controls underwent diffusion-weighted and resting state functional magnetic resonance imaging (rs-fMRI). For assessment of motivational anhedonia, we summed the values of the CORE non-interactiveness score, and the items 1 (hopelessness) and 7 (work and activities) of the Hamilton Depression Rating Scale. Whole-brain voxel-wise statistical analysis of fractional anisotropy (FA) data was performed using Tract-Based Spatial Statistics (TBSS). Additionally, we performed a whole-brain comparison of integrated local correlation of rs-fMRI signal (LCOR), to investigate regional functional differences between patients and healthy controls. Whole brain correlations between motivational anhedonia and measures of structural and functional connectivity (FA, and LCOR) were calculated. TBSS-analyses revealed reduced FA in the left superior longitudinal fasciculus (SLF) in patients with MDD. LCOR was reduced in patients with depression in an adjacent cluster localized in bilateral precunei. Within patients, there was a positive correlation between motivational anhedonia and LCOR in the precunei and a negative correlation in bilateral sensorimotor areas. FA-values did not show significant correlations. These findings suggest that motivational anhedonia in depression is linked to alterations of functional connectivity within bilateral precunei. Observed white matter microstructural alterations in the SLF do not show such an association.

Major depressive disorder (MDD) is mainly characterized by its core dysfunction in higher-order brain cortices involved in emotional and cognitive processes, whose neurobiological basis remains unclear. In this study, we applied a relatively new developed resting-state functional magnetic resonance imaging (rs-fMRI) method of intrinsic neural timescale (INT), which reflects how long neural information is stored in a local brain area and reflects an ability of information integration, to investigate the local intrinsic neural dynamics using univariate and multivariate analyses in adolescent depression. Based on the rs-fMRI data of sixty-six treatment-naïve adolescents with MDD and fifty-two well-matched healthy controls (HCs), we calculated an INT by assessing the magnitude of autocorrelation of the resting-state brain activity, and then compared the difference of INT between the two groups. Correlation between abnormal INT and clinical features was performed. We also utilized multivariate pattern analysis to determine whether INT could differentiate MDD patients from HCs at the individual level. Compared with HCs, patients with MDD showed shorter INT widely distributed in cortical and partial subcortical regions. Interestingly, the decreased INT in the left hippocampus was related to disease severity of MDD. Furthermore, INT can distinguish MDD patients from HCs with the most discriminative regions located in the dorsolateral prefrontal cortex, angular, middle occipital gyrus, and cerebellar posterior lobe. Our research aids in advancing understanding the brain abnormalities of treatment-naïve adolescents with MDD from the perspective of the local neural dynamics, highlighting the significant role of INT in understanding neurophysiological mechanisms. This study shows that the altered intrinsic timescales of local neural signals widely distributed in higher-order brain cortices regions may be the neurodynamic basis of cognitive and emotional disturbances in MDD patients, and provides preliminary support for the suggestion that these could be used to aid the identification of MDD patients in clinical practice.

Major depressive disorder (MDD) is associated with disrupted interhemispheric cooperation. However, the relationship between structural and functional alterations in interhemispheric cooperation in patients with MDD remains unclear. We investigated the associations between voxel-mirrored homotopic connectivity (VMHC) and radial diffusivity (RD) within the corpus callosum (CC) and their links to depressive symptoms in patients with MDD. Sixty patients with MDD and 38 healthy controls (HCs) were assessed using resting-state functional MRI (rs-fMRI) and diffusion MRI (dMRI) to evaluate interhemispheric functional connectivity (VMHC) and structural integrity (RD) in the CC subregions. Group comparisons, correlation analyses, and mediation analyses were conducted to identify the significant differences, relationships, and indirect effects. Patients with MDD showed significantly reduced VMHC in the bilateral postcentral gyrus and lingual gyrus and increased RD in the CC subregions CC3, CC4, and CC5, indicating impaired functional and structural connectivity. Lower VMHC in the lingual gyrus was negatively correlated with depressive severity, whereas increased RD in the CC4 and CC5 was positively correlated with depressive symptoms. Mediation analysis revealed that the VMHC in the lingual gyrus fully mediated the relationship between RD in CC5 and depressive symptoms, suggesting a pathway through which structural impairments may affect mood through abnormal functional connectivity. The cross-sectional design limits the assessment of changes over time, and focusing solely on interhemispheric connectivity may overlook other networks involved in MDD. These findings provide preliminary evidence for disrupted interhemispheric coordination in MDD, with both functional and structural connectivity impairments linked to depressive symptoms. The mediating effect of the VMHC in the lingual gyrus highlights the potential role of interhemispheric connectivity in the pathophysiology of MDD. Our results provide an integrative perspective on the functional and microstructural organization of the brain in patients with MDD.

Frailty and late-life depression (LLD) often coexist and share several structural brain changes. We aimed to study the joint effect LLD and frailty have on brain structure. Cross-sectional study. Academic Health Center. Thirty-one participants (14 LLD+Frail and 17 Never-depressed+Robust). LLD was diagnosed by a geriatric psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition for single episode or recurrent major depressive disorder without psychotic features. Frailty was assessed using the FRAIL scale (0-5), classifying subjects as robust (0), prefrail (1-2), and frail (3-5). Participants underwent T1-weighted magnetic resonance imaging in which covariance analysis of subcortical volumes and vertex-wise analysis of cortical thickness values were performed to access changes in grey matter. Participants also underwent diffusion tensor imaging in which tract-based spatial statistics was used with voxel-wise statistical analysis on fractional anisotropy and mean diffusion values to assess changes in white matter (WM). We found a significant difference in mean diffusion values (48,225 voxels; peak voxel: pFWER=0.005, MINI coord. (X,Y,Z) = -26,-11,27) between the LLD-Frail group and comparison group. The corresponding effect size (f=0.808) was large. We showed the LLD+Frailty group is associated with significant microstructural changes within WM tracts compared to Never-depressed+Robust individuals. Our findings indicate the possibility of a heightened neuroinflammatory burden as a potential mechanism underlying the co-occurrence of both conditions and the possibility of a depression-frailty phenotype in older adults.

For suicide in major depression disorder, it is urgent to seek for a reliable neuroimaging biomarker with interpretable links to molecular tissue signatures. Accordingly, we developed an ensemble learning scheme over transcriptome-defined parcellations (TDP) to explore homogeneously parcellated brain patterns and their interactions. 96 depressed patients without suicide attempt (SA), 86 with SA and 102 healthy controls were recruited for resting state fMRI scanning. Six genetic dimensions were created by homogenous transcriptomic delineations from Allen Human Brain Atlas. Spatially-continuous TDPs were generated according to expression-levels of each brain region along diverse dimensions. Subsequently, TDPs were integrated with a three-layer ensemble learning scheme, where brain dysfunction of each TDP related to suicide was quantified with a resting-state functional abnormality (RSFA) score. Then, personalized index of brain dysfunction was produced according to the interactive pattern across TDPs. Ensemble learning over TDPs displayed higher suicide predictive performance, relative to that over the regions level, and over null model (95 % CI of accuracy: 73.23 ± 1.07 %; 64.59 ± 3.00 %; 65.41 ± 3.97 %, respectively). Empowered by specific parieto-occipital TDP (PO-TDP) pattern quantified with RSFA score in suicide risk prediction, its alternations of SA effects were spatially associated with transcriptional profiles of GRIN2A and GABRG2. Moreover, glutamatergic and GABAergic synapse were overrepresented in enrichment analysis. Glutamatergic and GABAergic dysfunction in the visual cortex was suggested via the PO-TDP specific interaction pattern. The inherent excitatory/inhibitory imbalance could contribute to aberrant emotional processing and neurocognitive impairment, ultimately leading to suicide.

Anhedonia is a core symptom of depression that is closely related to prognosis and treatment outcomes. However, accurate and efficient treatments for anhedonia are lacking, mandating a deeper understanding of the underlying mechanisms. A total of 303 patients diagnosed with depression and anhedonia were assessed by the Snaith-Hamilton Pleasure Scale (SHAPS) and magnetic resonance imaging (MRI). The patients were categorized into a low-anhedonia group and a high-anhedonia group using the K-means algorithm. A data-driven approach was used to explore the differences in brain structure and function with different degrees of anhedonia based on MATLAB. A random forest model was used exploratorily to test the predictive ability of differences in brain structure and function on anhedonia in depression. Structural and functional differences were apparent in several brain regions of patients with depression and high-level anhedonia, including in the temporal lobe, paracingulate gyrus, superior frontal gyrus, inferior occipital gyrus, right insular gyrus, and superior parietal lobule. And changes in these brain regions were significantly correlated with scores of SHAPS. These brain regions may be useful as biomarkers that provide a more objective assessment of anhedonia in depression, laying the foundation for precision medicine in this treatment-resistant, relatively poor prognosis group.

Accumulating evidence showed that major depressive disorder (MDD) is characterized by a dysfunction of serotonin neurotransmission. Raphe nuclei are the sources of most serotonergic neurons that project throughout the brain. Incorporating measurements of activity within the raphe nuclei into the analysis of connectivity characteristics may contribute to understanding how neurotransmitter synthesized centers are involved in thepathogenesisof MDD. Here, we analyzed the resting-state functional magnetic resonance imaging (RS-fMRI) dataset from 1,148 MDD patients and 1,079 healthy individuals recruited across nine centers. A seed-based analysis with the dorsal raphe and median raphe nuclei was performed to explore the functional connectivity (FC) alterations. Compared to controls, for dorsal raphe, the significantly decreased FC linking with the right precuneus and median cingulate cortex were found; for median raphe, the increased FC linking with right superior cerebellum (lobules V/VI) was found in MDD patients. In further exploratory analyzes, MDD-related connectivity alterations in dorsal and median raphe nuclei in different clinical factors remained highly similar to the main findings, indicating these abnormal connectivities are a disease-related alteration. Our study highlights a functional dysconnection pattern of raphe nuclei in MDD with multi-site big data. These findings help improve our understanding of the pathophysiology of depression and provide evidence of the theoretical foundation for the development of novel pharmacotherapies.

Adolescence is a vulnerable period for major depressive disorder (MDD). The aim of our study was to investigate resting-state functional connectivity (RSFC) in first-episode, medication-naïve adolescent MDD patients. Twenty-three drug-naïve adolescents diagnosed with first-episode MDD and 27 healthy participants were enrolled. Seed-to-voxel RSFC analyses were performed. The frontolimbic circuit regions of interest included the amygdala, anterior cingulate cortex, insula, and hippocampus. A correlation analysis between the RSFC and Children's Depression Inventory, Hamilton depression rating scale, and duration of episodes was performed. The adolescents with MDD exhibited the following characteristics: a lower RSFC between the right amygdala and right superior frontal gyrus; a lower RSFC between the right hippocampus and clusters including the right insula and right middle frontal gyrus; a higher RSFC between the left insula and clusters including the bilateral middle frontal gyrus, right superior frontal gyrus, and right frontal pole; and a higher RSFC between the left dorsal anterior cingulate cortex and a cluster including the left insula. Medication-naïve adolescents with depression display lower connectivity of several brain regions implicated in processing, regulation, and memory of emotions. Higher connectivity was observed in brain regions that potentially explain rumination, impaired concentration, and physiological arousal.

Major Depressive Disorder (MDD) is characterized by aberrant resting-state functional connectivity (FC) in anterior cingulate regions (e.g., subgenual anterior cingulate [sgACC]) and by negative emotional functioning that is inflexible or resistant to change. MDD (N = 33) and control (CTL; N = 31) adults completed a resting-state scan, followed by a smartphone-based Experience Sampling Methodology (ESM) protocol surveying 10 positive and negative emotions 5 times per day for 21 days. We used multilevel modeling to assess moment-to-moment emotional inflexibility (i.e., strong temporal connections between emotions). We examined group differences in whole-brain FC analysis of bilateral sgACC, and then examined associations between emotional experiences and the extracted FC values within each group. As predicted, MDDs had inflexibility in sadness and avoidance (p < .001, FDR-corrected p < .05), indicating that these emotional experiences persist in depression. MDDs showed weaker FC between the right sgACC and pregenual/dorsal anterior cingulate (pg/dACC) than did CTLs (FWE-corrected, voxelwise p = .01). Importantly, sgACC-pg/dACC FC predicted sadness inflexibility in both MDDs (p = .046) and CTLs (p = .033), suggesting that sgACC FC is associated with day-to-day negative emotions. Other maladaptive behaviors likely also affect the flexibility of negative emotions. We cannot generalize our finding of a positive relation between sgACC FC and inflexibility of sadness to individuals with more chronic depression or who have recovered from depression. Our preliminary findings suggest that connections between portions of the ACC contribute to the persistence of negative emotions and are important in identifying a brain mechanism that may underlie the maintenance of sadness in daily life.

Childhood maltreatment (CM) represents a potent risk factor for major depressive disorder (MDD), including poorer treatment response. Altered resting-state connectivity in the fronto-limbic system has been reported in maltreated individuals. However, previous results in smaller samples differ largely regarding localization and direction of effects. We included healthy and depressed samples [ No significant associations between maltreatment and resting-state connectivity of any ROI were found across MDD and HC participants and no interaction effect with diagnosis became significant. Investigating MDD patients only yielded maltreatment-associated increased connectivity between the amygdala and dorsolateral frontal areas [ The majority of previous resting-state connectivity correlates of CM could not be replicated in this large-scale study. The strongest evidence was found for clinically relevant maltreatment associations with altered adult amygdala-dorsolateral frontal connectivity in depression. Future studies should explore the relevance of this pathway for a maltreated subgroup of MDD patients.

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD. Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD. Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up. Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

Many studies have found that the hippocampus plays a very important role in major depressive disorder (MDD). The hippocampus can be divided into three subfields: the cornu ammonis (CA), dentate gyrus (DG) and subiculum. Each subfield of the hippocampus has a unique function and are differentially associated with the pathological mechanisms of MDD. However, no research exists to describe the resting state functional connectivity of each hippocampal subfield in MDD. Fifty-five patients with MDD and 25 healthy controls (HCs) matched for gender, age and years of education were obtained. A seed-based method that imposed a template on the whole brain was used to assess the resting-state functional connectivity (rsFC) of each hippocampal subfield. Patients with MDD demonstrated increased connectivity in the left premotor cortex (PMC) and reduced connectivity in the right insula with the CA seed region. Increased connectivity was reported in the left orbitofrontal cortex (OFC) and left ventrolateral prefrontal cortex (vlPFC) with the DG seed region. The subiculum seed region revealed increased connectivity with the left premotor cortex (PMC), the right middle frontal gyrus (MFG), the left ventrolateral prefrontal cortex (vlPFC) and reduced connectivity with the right insula. ROC curves confirmed that the differences between groups were statistically significant. The results suggest that the CA, DG and subiculum have significant involvement with MDD. Specifically, the abnormal functional connectivity of the CA may be related to bias of coding and integration of information in patients with MDD. The abnormal functional connectivity of the DG may be related to the impairment of working memory in patients with MDD, and the abnormal functional connectivity of the subiculum may be related to cognitive impairment and negative emotions in patients with MDD.

Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Major depressive disorder (MDD) is characterized by abnormal resting-state functional connectivity (RSFC), especially in medial prefrontal cortical (MPFC) regions of the default network. However, prior research in MDD has not examined dynamic changes in functional connectivity as networks form, interact, and dissolve over time. We compared unmedicated individuals with MDD (n=100) to control participants (n=109) on dynamic RSFC (operationalized as SD in RSFC over a series of sliding windows) of an MPFC seed region during a resting-state functional magnetic resonance imaging scan. Among participants with MDD, we also investigated the relationship between symptom severity and RSFC. Secondary analyses probed the association between dynamic RSFC and rumination. Results showed that individuals with MDD were characterized by decreased dynamic (less variable) RSFC between MPFC and regions of parahippocampal gyrus within the default network, a pattern related to sustained positive connectivity between these regions across sliding windows. In contrast, the MDD group exhibited increased dynamic (more variable) RSFC between MPFC and regions of insula, and higher severity of depression was related to increased dynamic RSFC between MPFC and dorsolateral prefrontal cortex. These patterns of highly variable RSFC were related to greater frequency of strong positive and negative correlations in activity across sliding windows. Secondary analyses indicated that increased dynamic RSFC between MPFC and insula was related to higher levels of recent rumination. These findings provide initial evidence that depression, and ruminative thinking in depression, are related to abnormal patterns of fluctuating communication among brain systems involved in regulating attention and self-referential thinking.

Subthreshold depression (StD) affects people who experience clinically relevant depressive symptoms, which does not meet the diagnostic criteria for major depressive disorder (MDD). StD represents an ideal model for understanding the pathophysiological mechanisms of depression. Impaired emotion processing is a core feature of depression; careful investigation is required to better understand the neural correlates of emotion processing in depressed populations. In the current study, we explored whether the resting-state functional connectivity of the amygdala, a hub that taps a wide range of brain areas involved in emotion processing, is altered in individuals with StD when compared with healthy controls. Resting-state imaging data was collected from 59 individuals with StD and 59 age- and gender-matched controls. We found that the resting-state functional connectivity of the left amygdala with the cognitive control network and the left insula was significantly lower in people with StD than that in healthy controls. Such association was not observed in the right amygdala. Furthermore, functional connectivity strength between the left amygdala and the left precuneus was positively associated with depressive symptoms in individuals with StD. Our findings are in line with those reported in subjects with MDD, which may assist in further elucidating the pathophysiological mechanisms of depression, and contribute to the development of tailored treatments for individuals with StD who are at high risk of developing MDD.

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We examined the functional connectivity of subcallosal cingulate gyrus (SCG), nucleus accumbens (NAc), and ventral caudate (VCa), the main target areas for the treatment of major depression disorder (MDD), using deep brain stimulation (DBS). MDD is one of the most common diseases in the world, and approximately 30% of MDD patients do not respond to common therapies, including psychotherapy and antidepressant medications. Alternatively, DBS has been recently used to treat MDD. Resting state fMRI was obtained from seventeen healthy subjects and seven MDD patients. The functional connectivity network of the brain was constructed for all subjects and measured by the `degree' value for each SCG, NAc, and VCa regions using the graph theory analysis. The results show that the degree values of VCa and the left SCG are higher in the MDD group than the healthy group. Furthermore, the patterns of the degree values were different for the right and left hemispheres in MDD patients. Our findings suggest that degree values and their patterns have a potential to be used as diagnosis tools to detect the brain areas with abnormal functional connectivity.

Major depressive disorder (MDD) frequently emerges during adolescence and can lead to persistent illness, disability, and suicide. The maturational changes that take place in the brain during adolescence underscore the importance of examining neurobiological mechanisms during this time of early illness. However, neural mechanisms of depression in adolescents have been understudied. Research has implicated the amygdala in emotion processing in mood disorders, and adult depression studies have suggested amygdala-frontal connectivity deficits. Resting-state functional magnetic resonance imaging is an advanced tool that can be used to probe neural networks and identify brain-behavior relationships. To examine amygdala resting-state functional connectivity (RSFC) in adolescents with and without MDD using resting-state functional magnetic resonance imaging as well as how amygdala RSFC relates to a broad range of symptom dimensions. A cross-sectional resting-state functional magnetic resonance imaging study was conducted within a depression research program at an academic medical center. Participants included 41 adolescents and young adults aged 12 to 19 years with MDD and 29 healthy adolescents (frequency matched on age and sex) with no psychiatric diagnoses. Using a whole-brain functional connectivity approach, we examined the correlation of spontaneous fluctuation of the blood oxygen level-dependent signal of each voxel in the whole brain with that of the amygdala. Adolescents with MDD showed lower positive RSFC between the amygdala and hippocampus, parahippocampus, and brainstem (z >2.3, corrected P < .05); this connectivity was inversely correlated with general depression (R = -.523, P = .01), dysphoria (R = -.455, P = .05), and lassitude (R = -.449, P = .05) and was positively correlated with well-being (R = .470, P = .03). Patients also demonstrated greater (positive) amygdala-precuneus RSFC (z >2.3, corrected P < .05) in contrast to negative amygdala-precuneus RSFC in the adolescents serving as controls. Impaired amygdala-hippocampal/brainstem and amygdala-precuneus RSFC have not previously been highlighted in depression and may be unique to adolescent MDD. These circuits are important for different aspects of memory and self-processing and for modulation of physiologic responses to emotion. The findings suggest potential mechanisms underlying both mood and vegetative symptoms, potentially via impaired processing of memories and visceral signals that spontaneously arise during rest, contributing to the persistent symptoms experienced by adolescents with depression.

The neural basis of Major Depressive Disorder (MDD) which is a clinical syndrome characterized by emotional and cognitive impairments is poorly understood. Accumulating evidence has suggested that the insula is an important substrate underlying the mechanism of MDD. This study aimed to examine the disrupted resting-state brain regional function in insula and to further investigate the associated resting-state functional connectivity (rs-FC) of insula underlie the MDD in adolescents and young adults. We employed 3.0T resting-state functional magnetic resonance imaging (rs-fMRI) to acquire data from 76 adolescents and young adults with MDD and 44 age and sex matched healthy control subjects. We employed a regional Amplitude of Low-Frequency Fluctuation (ALFF) analysis to explore local intrinsic neural oscillation alterations in insula and an ALFF-based functional connectivity (FC) approach to detect the potential changes in remote connectivity with insula in adolescents and young adults with MDD. By applying ALFF analysis, significantly decreased activities were detected in bilateral insula, and in particular in right anterior insular gyrus (MNI; ROI1: 42, 24, -3), right posterior insular gyrus (Montreal Neurological Institute, MNI; ROI2: 36, -9, 15) and left anterior insular gyrus (MNI; ROI3: -36, 12, 9) in patients with MDD compared to the healthy controls (p < 0.05, 1000 permutations, TFCE corrected). With ROI2 as the seed in the subsequent ALFF-based rs-FC analysis, patients with MDD were observed to have significantly reduced FC with bilateral middle occipital gyrus, lingual gyrus, calcarine, postcentral gyrus, precentral gyrus, supramarginal area, superior temporal gyrus and middle cingulate gyrus as compared to the healthy controls (p < 0.05, 1000 permutations, TFCE corrected). No significant differences of FC were detected between the patients and healthy controls when using ROI1 and ROI3 as the seeds. We found no correlations between ALFF or rs-FC values and the severity of depression as estimated by Hamilton Depression Rating Scale (HAM-D). Clinical information were limited and no significant correlations were found between imaging variables and HAM-D scores, which reduces the power to interpret the present findings. A cross-sectional design was employed in this study so that it is not possible to know whether the abnormal ALFF or altered brain FC of insula reflects a state or trait effect in young people with MDD. This study highlights the regional/network interaction abnormalities of insula in adolescents and young adults with MDD, and could provide further insight into understanding the neural pathomechanism of MDD in young patients.