儿童青少年难治性抑郁的机制，治疗，早期干预

This study explores the responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited fifty-five outpatients with TRD who were randomised into three approximately equal-sized groups (A: 0.5 mg/kg ketamine; B: 0.2 mg/kg ketamine; and C: normal saline) under double-blind conditions. The ketamine responses were measured by EEG signals and Hamilton Depression Rating Scale (HDRS) scores. At baseline, responders showed a significantly weaker EEG theta power than did non- responders (p < 0.05). Responders exhibited a higher EEG alpha power but lower EEG alpha asymmetry and theta cordance at post-treatment than at baseline (p < 0.05). Furthermore, our baseline EEG predictor classified responders and non-responders with 81.3 +- 9.5% accuracy, 82.1 +- 8.6% sensitivity and 91.9 +- 7.4% specificity. In conclusion, the rapid antidepressant effects of mixed doses of ketamine are associated with prefrontal EEG power, asymmetry and cordance at baseline and early post-treatment changes. The prefrontal EEG patterns at baseline may account for recognising ketamine effects in advance. Our randomised, double- blind, placebo-controlled study provides information regarding clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.

In contemporary society, the escalating pressures of life and work have propelled psychological disorders to the forefront of modern health concerns, an issue that has been further accentuated by the COVID-19 pandemic. The prevalence of depression among adolescents is steadily increasing, and traditional diagnostic methods, which rely on scales or interviews, prove particularly inadequate for detecting depression in young people. Addressing these challenges, numerous AI-based methods for assisting in the diagnosis of mental health issues have emerged. However, most of these methods center around fundamental issues with scales or use multimodal approaches like facial expression recognition. Diagnosis of depression risk based on everyday habits and behaviors has been limited to small-scale qualitative studies. Our research leverages adolescent census data to predict depression risk, focusing on children's experiences with depression and their daily life situations. We introduced a method for managing severely imbalanced high-dimensional data and an adaptive predictive approach tailored to data structure characteristics. Furthermore, we proposed a cloud-based architecture for automatic online learning and data updates. This study utilized publicly available NSCH youth census data from 2020 to 2022, encompassing nearly 150,000 data entries. We conducted basic data analyses and predictive experiments, demonstrating significant performance improvements over standard machine learning and deep learning algorithms. This affirmed our data processing method's broad applicability in handling imbalanced medical data. Diverging from typical predictive method research, our study presents a comprehensive architectural solution, considering a wider array of user needs.

The glutamatergic modulator ketamine has been shown to rapidly reduce depressive symptoms in patients with treatment-resistant major depressive disorder (TRD). Although its mechanisms of action are not fully understood, changes in cortical excitation/inhibition (E/I) following ketamine administration are well documented in animal models and could represent a potential biomarker of treatment response. Here, we analyse neuromagnetic virtual electrode timeseries collected from the primary somatosensory cortex in 18 unmedicated patients with TRD and in an equal number of age-matched healthy controls during a somatosensory 'airpuff' stimulation task. These two groups were scanned as part of a clinical trial of ketamine efficacy under three conditions: a) baseline; b) 6-9 hours following subanesthetic ketamine infusion; and c) 6-9 hours following placebo-saline infusion. We obtained estimates of E/I interaction strengths by using Dynamic Causal Modelling (DCM) on the timeseries, thereby allowing us to pinpoint, under each scanning condition, where each subject's dynamics lie within the Poincaré diagram - as defined in dynamical systems theory. We demonstrate that the Poincaré diagram offers classification capability for TRD patients, in that the further the patients' coordinates were shifted (by virtue of ketamine) toward the stable (top-left) quadrant of the Poincaré diagram, the more their depressive symptoms improved. The same relationship was not observed by virtue of a placebo effect - thereby verifying the drug-specific nature of the results. We show that the shift in neural dynamics required for symptom improvement necessitates an increase in both excitatory and inhibitory coupling. We present accompanying MATLAB code made available in a public repository, thereby allowing for future studies to assess individually-tailored treatments of TRD.

Treatment-resistant depression (TRD) is a severe form of major depressive disorder in which patients do not achieve remission despite multiple adequate treatment trials. Evidence across pharmacologic options for TRD remains limited, and trials often do not fully capture patient-reported tolerability. Large-scale online peer-support narratives therefore offer a complementary lens on how patients describe and evaluate medications in real-world use. In this study, we curated a corpus of 5,059 Reddit posts explicitly referencing TRD from 3,480 subscribers across 28 mental health-related subreddits from 2010 to 2025. Of these, 3,839 posts mentioned at least one medication, yielding 23,399 mentions of 81 generic-name medications after lexicon-based normalization of brand names, misspellings, and colloquialisms. We developed an aspect-based sentiment classifier by fine-tuning DeBERTa-v3 on the SMM4H 2023 therapy-sentiment Twitter corpus with large language model based data augmentation, achieving a micro-F1 score of 0.800 on the shared-task test set. Applying this classifier to Reddit, we quantified sentiment toward individual medications across three categories: positive, neutral, and negative, and tracked patterns by drug, subscriber, subreddit, and year. Overall, 72.1% of medication mentions were neutral, 14.8% negative, and 13.1% positive. Conventional antidepressants, especially SSRIs and SNRIs, showed consistently higher negative than positive proportions, whereas ketamine and esketamine showed comparatively more favorable sentiment profiles. These findings show that normalized medication extraction combined with aspect-based sentiment analysis can help characterize patient-perceived treatment experiences in TRD-related Reddit discourse, complementing clinical evidence with large-scale patient-generated perspectives.

Depression is a severe mental disorder, and reliable identification plays a critical role in early intervention and treatment. Multimodal depression detection aims to improve diagnostic performance by jointly modeling complementary information from multiple modalities. Recently, numerous multimodal learning approaches have been proposed for depression analysis; however, these methods suffer from the following limitations: 1) inter-modal inconsistency and depression-unrelated interference, where depression-related cues may conflict across modalities while substantial irrelevant content obscures critical depressive signals, and 2) diverse individual depressive presentations, leading to individual differences in modality and cue importance that hinder reliable fusion. To address these issues, we propose Individual-aware Multimodal Depression-related Representation Learning Framework (IDRL) for robust depression diagnosis. Specifically, IDRL 1) disentangles multimodal representations into a modality-common depression space, a modality-specific depression space, and a depression-unrelated space to enhance modality alignment while suppressing irrelevant information, and 2) introduces an individual-aware modality-fusion module (IAF) that dynamically adjusts the weights of disentangled depression-related features based on their predictive significance, thereby achieving adaptive cross-modal fusion for different individuals. Extensive experiments demonstrate that IDRL achieves superior and robust performance for multimodal depression detection.

Traditional depression screening methods, such as the PHQ-9, are particularly challenging for children in pediatric primary care due to practical limitations. AI has the potential to help, but the scarcity of annotated datasets in mental health, combined with the computational costs of training, highlights the need for efficient, zero-shot approaches. In this work, we investigate the feasibility of state-of-the-art LLMs for depressive symptom extraction in pediatric settings (ages 6-24). This approach aims to complement traditional screening and minimize diagnostic errors. Our findings show that all LLMs are 60% more efficient than word match, with Flan leading in precision (average F1: 0.65, precision: 0.78), excelling in the extraction of more rare symptoms like "sleep problems" (F1: 0.92) and "self-loathing" (F1: 0.8). Phi strikes a balance between precision (0.44) and recall (0.60), performing well in categories like "Feeling depressed" (0.69) and "Weight change" (0.78). Llama 3, with the highest recall (0.90), overgeneralizes symptoms, making it less suitable for this type of analysis. Challenges include the complexity of clinical notes and overgeneralization from PHQ-9 scores. The main challenges faced by LLMs include navigating the complex structure of clinical notes with content from different times in the patient trajectory, as well as misinterpreting elevated PHQ-9 scores. We finally demonstrate the utility of symptom annotations provided by Flan as features in an ML algorithm, which differentiates depression cases from controls with high precision of 0.78, showing a major performance boost compared to a baseline that does not use these features.

Major depressive disorder (MDD) impacts more than 300 million people worldwide, highlighting a significant public health issue. However, the uneven distribution of medical resources and the complexity of diagnostic methods have resulted in inadequate attention to this disorder in numerous countries and regions. This paper introduces a high-performance MDD diagnosis tool named MDD-LLM, an AI-driven framework that utilizes fine-tuned large language models (LLMs) and extensive real-world samples to tackle challenges in MDD diagnosis. Therefore, we select 274,348 individual information from the UK Biobank cohort to train and evaluate the proposed method. Specifically, we select 274,348 individual records from the UK Biobank cohort and design a tabular data transformation method to create a large corpus for training and evaluating the proposed approach. To illustrate the advantages of MDD-LLM, we perform comprehensive experiments and provide several comparative analyses against existing model-based solutions across multiple evaluation metrics. Experimental results show that MDD-LLM (70B) achieves an accuracy of 0.8378 and an AUC of 0.8919 (95% CI: 0.8799 - 0.9040), significantly outperforming existing machine learning and deep learning frameworks for MDD diagnosis. Given the limited exploration of LLMs in MDD diagnosis, we examine numerous factors that may influence the performance of our proposed method, such as tabular data transformation techniques and different fine-tuning strategies.

Many supervised machine learning frameworks have been proposed for disease classification using functional magnetic resonance imaging (fMRI) data, producing important biomarkers. More recently, data pooling has flourished, making the result generalizable across a large population. But, this success depends on the population diversity and variability introduced due to the pooling of the data that is not a primary research interest. Here, we look at hierarchical Sparse Connectivity Patterns (hSCPs) as biomarkers for major depressive disorder (MDD). We propose a novel model based on hSCPs to predict MDD patients from functional connectivity matrices extracted from resting-state fMRI data. Our model consists of three coupled terms. The first term decomposes connectivity matrices into hierarchical low-rank sparse components corresponding to synchronous patterns across the human brain. These components are then combined via patient-specific weights capturing heterogeneity in the data. The second term is a classification loss that uses the patient-specific weights to classify MDD patients from healthy ones. Both of these terms are combined with the third term, a robustness loss function to improve the reproducibility of hSCPs. This reduces the variability introduced due to site and population diversity (age and sex) on the predictive accuracy and pattern stability in a large dataset pooled from five different sites. Our results show the impact of diversity on prediction performance. Our model can reduce diversity and improve the predictive and generalizing capability of the components. Finally, our results show that our proposed model can robustly identify clinically relevant patterns characteristic of MDD with high reproducibility.

Major depressive disorder persistently stands as a major public health problem. While some progress has been made toward effective treatments, the neural mechanisms that give rise to the disorder remain poorly understood. In this Perspective, we put forward a new theory of the pathophysiology of depression. More precisely, we spotlight three previously separate bodies of research, showing how they can be fit together into a previously overlooked larger picture. The first piece of the puzzle is provided by pathophysiology research implicating dopamine in depression. The second piece, coming from computational psychiatry, links depression with a special form of reinforcement learning. The third and final piece involves recent work at the intersection of artificial intelligence and basic neuroscience research, indicating that the brain may represent value using a distributional code. Fitting these three pieces together yields a new model of depression's pathophysiology, which spans circuit, systems, computational and behavioral levels, opening up new directions for research.

Major depressive disorder (MDD) is a debilitating health condition affecting a substantial part of the world's population. At present, there is no biological theory of MDD, and treatment is partial at best. Here I present a theory of MDD that explains its etiology, symptoms, pathophysiology, and treatment. MDD involves stressful life events that the person does not manage to resolve. In this situation animals normally execute a 'disengage' survival response. In MDD, this response is chronically executed, leading to depressed mood and the somatic MDD symptoms. To explain the biological mechanisms involved, I present a novel theory of opioids, where each opioid mediates one of the basic survival responses. The opioid mediating 'disengage' is dynorphin. The paper presents strong evidence for chronic dynorphin signaling in MDD and for its causal role in the disorder. The theory also explains bipolar disorder, and the mechanisms behind the treatment of both disorders.

Mental disorders like major depressive disorder can be seen as complex dynamical systems. In this study we investigate the dynamic behaviour of individuals to see whether or not we can expect a transition to another mood state. We introduce a mean field model to a binomial process, where we reduce a dynamic multidimensional system (stochastic cellular automaton) to a one-dimensional system to analyse the dynamics. Using maximum likelihood estimation, we can estimate the parameter of interest which, in combination with a bifurcation diagram, reflects the expectancy that someone has to transition to another mood state. After validating the proposed method with simulated data, we apply this method to two empirical examples, where we show its use in a clinical sample consisting of patients diagnosed with major depressive disorder, and a general population sample. Results showed that the majority of the clinical sample was categorized as having an expectancy for a transition, while the majority of the general population sample did not have this expectancy. We conclude that the mean field model has great potential in assessing the expectancy for a transition between mood states. With some extensions it could, in the future, aid clinical therapists in the treatment of depressed patients.

On the increase of major depressive disorders (MDD), many researchers paid attention to their recognition and treatment. Existing MDD recognition algorithms always use a single time-frequency domain method method, but the single time-frequency domain method is too simple and is not conducive to simulating the complex link relationship between brain functions. To solve this problem, this paper proposes a recognition method based on multi-layer brain functional connectivity networks (MBFCN) for major depressive disorder and conducts cognitive analysis. Cognitive analysis based on the proposed MBFCN finds that the Alpha-Beta1 frequency band is the key sub-band for recognizing MDD. The connections between the right prefrontal lobe and the temporal lobe of the extremely depressed disorders (EDD) are deficient in the brain functional connectivity networks (BFCN) based on phase lag index (PLI). Furthermore, potential biomarkers by the significance analysis of depression features and PHQ-9 can be found.

Background: The evolution of symptoms over time is at the heart of understanding and treating mental disorders. However, a principled, quantitative framework explaining symptom dynamics remains elusive. Here, we propose a Network Control Theory of Psychopathology allowing us to formally derive a theoretical control energy which we hypothesize quantifies resistance to future symptom improvement in Major Depressive Disorder (MDD). We test this hypothesis and investigate the relation to genetic and environmental risk as well as resilience. Methods: We modelled longitudinal symptom-network dynamics derived from N=2,059 Beck Depression Inventory measurements acquired over a median of 134 days in a sample of N=109 patients suffering from MDD. We quantified the theoretical energy required for each patient and time-point to reach a symptom-free state given individual symptom-network topology (E 0 ) and 1) tested if E 0 predicts future symptom improvement and 2) whether this relationship is moderated by Polygenic Risk Scores (PRS) of mental disorders, childhood maltreatment experience, and self-reported resilience. Outcomes: We show that E 0 indeed predicts symptom reduction at the next measurement and reveal that this coupling between E 0 and future symptom change increases with higher genetic risk and childhood maltreatment while it decreases with resilience. Interpretation: Our study provides a mechanistic framework capable of predicting future symptom improvement based on individual symptom-network topology and clarifies the role of genetic and environmental risk as well as resilience. Our control-theoretic framework makes testable, quantitative predictions for individual therapeutic response and provides a starting-point for the theory-driven design of personalized interventions. Funding: German Research Foundation and Interdisciplinary Centre for Clinical Research, Münster

Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N=328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r=0.61, p=4 x 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1=53, N2=154). Results showed high significant correlations between the predicted and the measured depression scale scores (r1= 0.49, r2=0.37, p<0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.

Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder. However, it is becoming increasingly clear that these biomarkers lack specificity, limiting their capacity to yield clinical impact. We discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development. We provide a framework for tying these subfields together that draws on tools from machine learning and network science.

Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizability. This challenge is particularly acute in magnetic resonance imaging (MRI), where image appearance varies widely across pulse sequences and scanner hardware. A recent domain-randomization strategy addresses the generalization problem by training deep neural networks on synthetic images with randomized intensities and anatomical content. By generating diverse data from anatomical segmentation maps, the approach enables models to accurately process image types unseen during training, without retraining or fine-tuning. It has demonstrated effectiveness across modalities including MRI, computed tomography, positron emission tomography, and optical coherence tomography, as well as beyond neuroimaging in ultrasound, electron and fluorescence microscopy, and X-ray microtomography. This tutorial paper reviews the principles, implementation, and potential of the synthesis-driven training paradigm. It highlights key benefits, such as improved generalization and resistance to overfitting, while discussing trade-offs such as increased computational demands. Finally, the article explores practical considerations for adopting the technique, aiming to accelerate the development of generalizable tools that make deep learning more accessible to domain experts without extensive computational resources or machine learning knowledge.

We consider a problem of diagnostic pattern recognition/classification from neuroimaging data. We propose a common data analysis pipeline for neuroimaging-based diagnostic classification problems using various ML algorithms and processing toolboxes for brain imaging. We illustrate the pipeline application by discovering new biomarkers for diagnostics of epilepsy and depression based on clinical and MRI/fMRI data for patients and healthy volunteers.

Understanding relationships across multiple imaging modalities is central to neuroimaging research. We introduce the Integrative Variational Autoencoder (InVA), the first hierarchical VAE framework for image-on-image regression in multimodal neuroimaging. Unlike standard VAEs, which are not designed for predictive integration across modalities, InVA models outcome images as functions of both shared and modality-specific features. This flexible, data-driven approach avoids rigid assumptions of classical tensor regression and outperforms conventional VAEs and nonlinear models such as BART. As a key application, InVA accurately predicts costly PET scans from structural MRI, offering an efficient and powerful tool for multimodal neuroimaging.

Non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS) provides a powerful tool to directly modulate brain oscillations that mediate complex cognitive processes. While the body of evidence about the effect of tACS on behavioral and cognitive performance is constantly growing, those studies fail to address the importance of subject- specific stimulation protocols. With this study here, we set the foundation to combine tACS with a recently presented framework that utilizes real-time fRMI and Bayesian optimization in order to identify the most optimal tACS protocol for a given individual. While Bayesian optimization is particularly relevant to such a scenario, its success depends on two fundamental choices: the choice of covariance kernel for the Gaussian process prior as well as the choice of acquisition function that guides the search. Using empirical (functional neuroimaging) as well as simulation data, we identified the squared exponential kernel and the upper confidence bound acquisition function to work best for our problem. These results will be used to inform our upcoming real- time experiments.

The present literature about possible mechanisms behind the effectivity of noninvasive electromagnetic stimulation in major depressive disorder (MDD) is not very rich. Despite extensive research in applications for clinical practice, the exact effects are yet not clear. We are comparing our previous results about the complexity changes induced by repetitive Transcranial Magnetic Stimulation (rTMS), and transcranial direct current stimulation (tDCS) which are known to modulate neural dynamics. Also, we are reviewing different biomarkers of complexity changes connected to depression, and how they change with the stimulation. TDCS is low-intensity TES, known to have polarity specific effects (neuromodulatory effects), and rTMS is inducing an electric field in the tissue circumstantially via Faraday's law. Both nonlinear modalities of electromagnetic stimulation may affect the levels of physiological complexity in the brain. We also compare the changes of complexity in electroencephalogram (EEG) and electrocardiogram (ECG), as potential future predictors of therapy outcome.

Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent. Emerging evidence suggests that artificial intelligence can predict rTMS treatment outcomes for most patients using fMRI connectivity features. While these models can reliably predict treatment outcomes for many patients for some underrepresented fMRI connectivity measures DNN models are unable to reliably predict treatment outcomes. As such we propose a novel method, Diversity Enhancing Conditional General Adversarial Network (DE-CGAN) for oversampling these underrepresented examples. DE-CGAN creates synthetic examples in difficult-to-classify regions by first identifying these data points and then creating conditioned synthetic examples to enhance data diversity. Through empirical experiments we show that a classification model trained using a diversity enhanced training set outperforms traditional data augmentation techniques and existing benchmark results. This work shows that increasing the diversity of a training dataset can improve classification model performance. Furthermore, this work provides evidence for the utility of synthetic patients providing larger more robust datasets for both AI researchers and psychiatrists to explore variable relationships.

Currently, depression treatment relies on closely monitoring patients response to treatment and adjusting the treatment as needed. Using self-reported or physician-administrated questionnaires to monitor treatment response is, however, burdensome, costly and suffers from recall bias. In this paper, we explore using location sensory data collected passively on smartphones to predict treatment outcome. To address heterogeneous data collection on Android and iOS phones, the two predominant smartphone platforms, we explore using domain adaptation techniques to map their data to a common feature space, and then use the data jointly to train machine learning models. Our results show that this domain adaptation approach can lead to significantly better prediction than that with no domain adaptation. In addition, our results show that using location features and baseline self-reported questionnaire score can lead to F1 score up to 0.67, comparable to that obtained using periodic self-reported questionnaires, indicating that using location data is a promising direction for predicting depression treatment outcome.

Predicting treatment non-response for anxiety and depression is challenging, in part because of sparse symptom assessments in real-world care. We examined whether passively captured, fine-grained emotions serve as linguistic markers of treatment outcomes by analyzing 12 weeks of de-identified teletherapy transcripts from 12,043 U.S. patients with moderate-to-severe anxiety and depression symptoms. A transformer-based small language model extracted patients' emotions at the talk-turn level; a state-space model (VISTA-SSM) clustered subgroups based on emotion dynamics over time and produced temporal networks. Two groups emerged: an improving group (n=8,230) and a non-response group (n=3,813) showing increased odds of symptom deterioration, and lower likelihood of clinically significant improvement. Temporal networks indicated that sadness and fear exerted most influence on emotion dynamics in non-responders, whereas improving patients showed balanced joy, sadness, and neutral expressions. Findings suggest that linguistic markers of emotional inflexibility can serve as scalable, interpretable, and theoretically grounded indicators for treatment risk stratification.

A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently. In this work we explore the possibility of predicting future depression using machine learning applied to longitudinal socio-demographic data. In doing so we show that data such as housing status, and the details of the family environment, can provide cues for predicting future psychiatric disorders. To this end, we introduce a novel deep multi-task recurrent neural network to learn time-dependent depression cues. The depression prediction task is jointly optimized with two auxiliary anomaly ranking tasks, including contrastive one-class feature ranking and deviation ranking. The auxiliary tasks address two key challenges of the problem: 1) the high within class variance of depression samples: they enable the learning of representations that are robust to highly variant in-class distribution of the depression samples; and 2) the small labeled data volume: they significantly enhance the sample efficiency of the prediction model, which reduces the reliance on large depression-labeled datasets that are difficult to collect in practice. Extensive empirical results on large-scale child depression data show that our model is sample-efficient and can accurately predict depression 2-4 years before the illness occurs, substantially outperforming eight representative comparators.

There is mounting evidence of a link between the properties of electroencephalograms (EEGs) of depressive patients and the outcome of pharmacotherapy. The goal of this study was to develop an EEG biomarker of antidepressant treatment response which would require only a single EEG measurement. We recorded resting, 21-channel EEG in 17 inpatients suffering from bipolar depression in eyes closed and eyes open conditions. The EEG measurement was performed at the end of the short washout period which followed previously unsuccessful pharmacotherapy. We calculated the normalized wavelet power of alpha rhythm using two referential montages and an average reference montage. In particular, in the occipital (O1, O2, Oz) channels the wavelet power of responders was up to 84% higher than that of nonresponders. Using a novel classification algorithm we were able to correctly predict the outcome of treatment with 90% sensitivity and 100% specificity. The proposed biomarker requires only a single EEG measurement and consequently is intrinsically different from biomarkers which exploit the changes in prefrontal EEG induced by pharmacotherapy over a given time.

Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological substrates could be associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning has shown promise in predicting treatment response in MDD, but one limitation has been the lack of clinical interpretability of machine learning models. We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a neural network model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Model validity and clinical utility were measured based on area under the curve (AUC) and expected improvement in sample remission rate with model-guided treatment, respectively. Post-hoc analyses yielded clusters (subgroups) based on patient prototypes learned during training. Prototypes were evaluated for interpretability by assessing differences in feature distributions and treatment-specific outcomes. A 3-prototype model achieved an AUC of 0.66 and an expected absolute improvement in population remission rate compared to the sample remission rate. We identified three treatment-relevant patient clusters which were clinically interpretable. It is possible to produce novel treatment-relevant patient profiles using machine learning models; doing so may improve precision medicine for depression. Note: This model is not currently the subject of any active clinical trials and is not intended for clinical use.

Modifications of signaling pathways and synapses owing to changing behaviors, environments, numerous neural modulation as well as brain-tissue injuries is defined as neuroplasticity in developmental neurology. The central purpose of the review is to gain a better understanding of the relation between stress, depression and neuroplasticity and explore potential therapeutic interventions for enhancing neural resilience. We have also reviewed the role of different factors like age, stress and sex on inducing neuroplasticity within various brain regions.

Neuroplasticity, the ability of the nervous system to adapt throughout an organism's lifespan, offers potential as both a biomarker and treatment target for neuropsychiatric conditions. Psychedelics, a burgeoning category of drugs, are increasingly prominent in psychiatric research, prompting inquiries into their mechanisms of action. Distinguishing themselves from traditional medications, psychedelics demonstrate rapid and enduring therapeutic effects after a single or few administrations, believed to stem from their neuroplasticity-enhancing properties. This review examines how classic psychedelics (e.g., LSD, psilocybin, N,N-DMT) and non-classic psychedelics (e.g., ketamine, MDMA) influence neuroplasticity. Drawing from preclinical and clinical studies, we explore the molecular, structural, and functional changes triggered by these agents. Animal studies suggest psychedelics induce heightened sensitivity of the nervous system to environmental stimuli (meta-plasticity), re-opening developmental windows for long-term structural changes (hyper-plasticity), with implications for mood and behavior. Translating these findings to humans faces challenges due to limitations in current imaging techniques. Nonetheless, promising new directions for human research are emerging, including the employment of novel positron-emission tomography (PET) radioligands, non-invasive brain stimulation methods, and multimodal approaches. By elucidating the interplay between psychedelics and neuroplasticity, this review informs the development of targeted interventions for neuropsychiatric disorders and advances understanding of psychedelics' therapeutic potential.

Antidepressant responses and the phenotype of treatment-resistant depression (TRD) are believed to have a genetic basis. Genetic susceptibility between the TRD phenotype and other psychiatric disorders has also been established in previous genetic studies, but population-based cohort studies have not yet provided evidence to support these outcomes. To estimate the TRD susceptibility and the susceptibility between TRD and other psychiatric disorders within families in a nationwide insurance cohort with extremely high coverage and comprehensive health care data. This cohort study assessed data from the Taiwan national health insurance database across entire population (N = 26 554 001) between January 2003 and December 2017. Data analysis was performed from August 2021 to April 2023. TRD was defined as having experienced at least 3 distinct antidepressant treatments in the current episode, each with adequate dose and duration, based on the prescribing records. Then, we identified the first-degree relatives of individuals with TRD (n = 34 467). A 1:4 comparison group (n = 137 868) of first-degree relatives of individuals without TRD was arranged for the comparison group, matched by birth year, sex, and kinship. Modified Poisson regression analyses were performed and adjusted relative risks (aRRs) and 95% CIs were calculated for the risk of TRD, the risk of other major psychiatric disorders, and different causes of mortality. This study included 172 335 participants (88 330 male and 84 005 female; mean [SD] age at beginning of follow-up, 22.9 [18.1] years). First-degree relatives of individuals with TRD had lower incomes, more physical comorbidities, higher suicide mortality, and increased risk of developing TRD (aRR, 9.16; 95% CI, 7.21-11.63) and higher risk of other psychiatric disorders than matched control individuals, including schizophrenia (aRR, 2.36; 95% CI, 2.10-2.65), bipolar disorder (aRR, 3.74; 95% CI, 3.39-4.13), major depressive disorder (aRR, 3.65; 95% CI, 3.44-3.87), attention-deficit/hyperactivity disorders (aRR, 2.38; 95% CI, 2.20-2.58), autism spectrum disorder (aRR, 2.26; 95% CI, 1.86-2.74), anxiety disorder (aRR, 2.71; 95% CI, 2.59-2.84), and obsessive-compulsive disorder (aRR, 3.14; 95% CI, 2.70-3.66). Sensitivity and subgroup analyses validated the robustness of the findings. To our knowledge, this study is the largest and perhaps first nationwide cohort study to demonstrate TRD phenotype transmission across families and coaggregation with other major psychiatric disorders. Patients with a family history of TRD had an increased risk of suicide mortality and tendency toward antidepressant resistance; therefore, more intensive treatments for depressive symptoms might be considered earlier, rather than antidepressant monotherapy.

Depression is a relatively common diagnosis in children and adolescents, and is associated with significant morbidity and suicidality in this population. Evidence-based treatment of the acute illness is imperative to try to prevent the development of treatment-resistant depression or other complications. In situations where response to acute treatment is inadequate, clinicians should first consider factors that may influence outcome, such as psychiatric or medical comorbidities, psychosocial stressors, and treatment noncompliance. Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for depression in children and adolescents. For treatment-resistant depression, a switch to an alternate SSRI is recommended before trials of other antidepressants. Psychotherapy, such as cognitive behavioral therapy or interpersonal therapy, may improve treatment response. More research is needed examining medication augmentation strategies for treatment-resistant depression in children and adolescents.

The 75th anniversary of introducing lithium into modern psychiatry is recognized, attested by the 1949 paper of John Cade. About this event, my editorial in the special 2010 issue of Neuropsychobiology was titled "Lithium: Sixty Years Thereafter." Since then, fifteen more years have brought further information about lithium. This paper makes a narrative review of the most important articles published in this period. The selected key literature of 2010-2024 addressed lithium prophylactic efficacy in bipolar disorder (BD), including pediatric, recurrent depression, and lithium augmentation of antidepressants in treatment-resistant depression (TRD). Novel data have been obtained for lithium adverse effects (kidney, thyroid) and beneficial outcomes of long-term lithium administration (anti-suicidal, neuroprotective, antiviral, and others). The results on the mechanisms of lithium action covered genetic investigations of the Consortium of Lithium Genetics (ConLiGen) and in vitro studies with induced pluripotent stem cells and lymphoblastoid cell lines. The underutilization of lithium nowadays was emphasized, and the ways to overcome it were considered. Lithium remains the choice drug for recurrence prevention in BD, also in adolescents, and a significant option for augmentation of antidepressants in TRD. The adverse side effects should be carefully followed and managed according to current guidelines. There are also beneficial lithium impacts, of which anti-suicidal and anti-dementia seem the most important. Most of the results of neurobiological studies on lithium mechanisms may be related to lithium response and some (e.g., immunomodulatory) to the pathogenesis of BD. Better education about lithium could make more patients the beneficiary of this drug.

Treatment-refractory depression is a devastating condition with significant morbidity, mortality, and societal cost. At least 15% of cases of major depressive disorder remain refractory to treatment. The authors previously identified a young adult with treatment-refractory depression and multiple suicide attempts with an associated severe deficiency of CSF tetrahydrobiopterin, a critical cofactor for monoamine neurotransmitter synthesis. Treatment with sapropterin, a tetrahydrobiopterin analogue, led to dramatic and long-lasting remission of depression. This sentinel case led the authors to hypothesize that the incidence of metabolic abnormalities contributing to treatment-refractory depression is underrecognized. The authors conducted a case-control, targeted, metabolomic evaluation of 33 adolescent and young adult patients with well-characterized histories of treatment-refractory depression (at least three maximum-dose, adequate-duration medication treatments), and 16 healthy comparison subjects. Plasma, urine, and CSF metabolic profiling were performed by coupled gas chromatography/mass spectrometry and high-performance liquid chromatography electrospray ionization tandem mass spectrometry. CSF metabolite abnormalities were identified in 21 of the 33 participants with treatment-refractory depression. Cerebral folate deficiency (N=12) was most common, with normal serum folate levels and low CSF 5-methyltetrahydrofolate (5-MTHF) levels. All patients with cerebral folate deficiency, including one with low CSF levels of 5-MTHF and tetrahydrobiopterin intermediates, showed improvement in depression symptom inventories after treatment with folinic acid; the patient with low tetrahydrobiopterin also received sapropterin. None of the healthy comparison subjects had a metabolite abnormality. Examination of metabolic disorders in treatment-refractory depression identified an unexpectedly large proportion of patients with potentially treatable abnormalities. The etiology of these abnormalities remains to be determined.

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Suicide continues to be a major cause of mortality in adolescents with limited treatment options.

Despite the clinical benefits of ketamine in treating major depressive disorder (MDD), some patients exhibit drug resistance, and the intricate mechanisms underlying this await comprehensive explication. We used metabolomics to find biomarkers for ketamine efficacy and uncover its mechanisms of action. The study included 40 MDD patients treated with ketamine in the discovery cohort and 24 patients in the validation cohort. Serum samples from the discovery cohort receiving ketamine were analyzed using ultra performance liquid chromatography-mass spectrometry to study metabolomic changes and identify potential biomarkers. Metabolic alterations were evaluated pre- and post-ketamine treatment. Spearman correlation was applied to examine the relationship between metabolite alterations and depressive symptom changes. In addition, potential biomarkers, particularly thyroxine, were investigated through quantitative measurements in the validation cohort. We found that energy metabolite changes (adenosine triphosphate, adenosine diphosphate [ADP], pyruvate) were different in responders versus non-responders. The magnitude of the ADP shift was strongly correlated with the rate of reduction in Montgomery-Asberg Depression Rating Scale (MADRS) scores (Rho = 0.48, p Ketamine ameliorates depressive symptoms by modulating metabolic pathways linked to energy metabolism. Low baseline FT3 levels appear to predict a positive response in MDD patients, suggesting FT3 has potential as a biological marker for clinical ketamine treatment. ChiCTR-OOC-17012239.

Schizophrenia (SCZ), bipolar (BD) and major depression disorder (MDD) are severe psychiatric disorders that are challenging to treat, often leading to treatment resistance (TR). It is crucial to develop effective methods to identify and treat patients at risk of TR at an early stage in a personalized manner, considering their biological basis, their clinical and psychosocial characteristics. Effective translation of theoretical knowledge into clinical practice is essential for achieving this goal. The Psych-STRATA consortium addresses this research gap through a seven-step approach. First, transdiagnostic biosignatures of SCZ, BD and MDD are identified by GWAS and multi-modal omics signatures associated with treatment outcome and TR (steps 1 and 2). In a next step (step 3), a randomized controlled intervention study is conducted to test the efficacy and safety of an early intensified pharmacological treatment. Following this RCT, a combined clinical and omics-based algorithm will be developed to estimate the risk for TR. This algorithm-based tool will be designed for early detection and management of TR (step 4). This algorithm will then be implemented into a framework of shared treatment decision-making with a novel mental health board (step 5). The final focus of the project is based on patient empowerment, dissemination and education (step 6) as well as the development of a software for fast, effective and individualized treatment decisions (step 7). The project has the potential to change the current trial and error treatment approach towards an evidence-based individualized treatment setting that takes TR risk into account at an early stage.

Epidemiologic studies indicate that children exposed to early adverse experiences are at increased risk for the development of depression, anxiety disorders, or both. Persistent sensitization of central nervous system (CNS) circuits as a consequence of early life stress, which are integrally involved in the regulation of stress and emotion, may represent the underlying biological substrate of an increased vulnerability to subsequent stress as well as to the development of depression and anxiety. A number of preclinical studies suggest that early life stress induces long-lived hyper(re)activity of corticotropin-releasing factor (CRF) systems as well as alterations in other neurotransmitter systems, resulting in increased stress responsiveness. Many of the findings from these preclinical studies are comparable to findings in adult patients with mood and anxiety disorders. Emerging evidence from clinical studies suggests that exposure to early life stress is associated with neurobiological changes in children and adults, which may underlie the increased risk of psychopathology. Current research is focused on strategies to prevent or reverse the detrimental effects of early life stress on the CNS. The identification of the neurobiological substrates of early adverse experience is of paramount importance for the development of novel treatments for children, adolescents, and adults.

Depressive and anxiety symptoms are common in childhood-onset systemic lupus erythematosus (cSLE), yet their etiology and course remain unclear. We investigated the frequency of depressive and anxiety symptoms longitudinally in youth with cSLE, and associated socio-demographic and disease factors. Participants 8-18 years with cSLE completed baseline measures [demographic questionnaire, Center for Epidemiologic Studies Depression Scale for Children (CES-DC), Screen for Childhood Anxiety Related Disorders (SCARED), and psychiatric interview] and follow-up measures (CES-DC and SCARED) > 6 months later. Prevalence of clinically significant depressive (score >15 on CES-DC) or anxiety symptoms (score At baseline, of 51 participants with a mean disease duration of 4.3 years (SD 2.7), 35% ( In this sample, depressive and anxiety symptoms were prevalent and persistent. Depressive symptoms correlated with neighborhood-level material deprivation, and family psychiatric history. These findings support routine psychosocial assessment in cSLE, and provision of appropriate resources.

Severe mental illness (SMI) is a broad category that includes schizophrenia, bipolar disorder, and severe depression. Both genetic disposition and environmental exposures play important roles in the development of SMI. Multiple lines of evidence suggest that the roles of genetic and environmental factors depend on each other. Gene-environment interactions may underlie the paradox of strong environmental factors for highly heritable disorders, the low estimates of shared environmental influences in twin studies of SMI, and the heritability gap between twin and molecular heritability estimates. Sons and daughters of parents with SMI are more vulnerable to the effects of prenatal and postnatal environmental exposures, suggesting that the expression of genetic liability depends on environment. In the last decade, gene-environment interactions involving specific molecular variants in candidate genes have been identified. Replicated findings include an interaction between a polymorphism in the AKT1 gene and cannabis use in the development of psychosis and an interaction between the length polymorphism of the serotonin transporter gene and childhood maltreatment in the development of persistent depressive disorder. Bipolar disorder has been underinvestigated, with only a single study showing an interaction between a functional polymorphism in the BDNF gene and stressful life events triggering bipolar depressive episodes. The first systematic search for gene-environment interactions has found that a polymorphism in CTNNA3 may sensitize the developing brain to the pathogenic effect of cytomegalovirus in utero, leading to schizophrenia in adulthood. Strategies for genome-wide investigations will likely include coordination between epidemiological and genetic research efforts, systematic assessment of multiple environmental factors in large samples, and prioritization of genetic variants.

There is inconsistent evidence of interaction between stressful events and a serotonin transporter promoter polymorphism (5-HTTLPR) in depression. Recent studies have indicated that the moderating effect of 5-HTTLPR may be strongest when adverse experiences have occurred in childhood and the depressive symptoms persist over time. However, it is unknown whether this gene-environment interaction is present for recurrent depressive disorder and different forms of maltreatment. Therefore, patients with recurrent clinically diagnosed depression and controls screened for the absence of depression were utilised to examine the moderating effect of 5-HTTLPR on associations between specific forms of childhood adversity and recurrent depression. A sample of 227 recurrent unipolar depression cases and 228 never psychiatrically ill controls completed the Childhood Trauma Questionnaire to assess exposure to sexual, physical and emotional abuse, physical and emotional neglect in childhood. DNA extracted from blood or cheek swabs was genotyped for the short (s) and long (l) alleles of 5-HTTLPR. All forms of childhood maltreatment were reported as more severe by cases than controls. There was no direct association between 5-HTTLPR and depression. Significant interactions with additive and recessive 5-HTTLPR genetic models were found for overall severity of maltreatment, sexual abuse and to a lesser degree for physical neglect, but not other maltreatment types. The cross-sectional design limits causal inference. Retrospective report of childhood adversity may have reduced the accuracy of the findings. This study provides support for the role of interplay between 5-HTTLPR and a specific early environmental risk in recurrent depressive disorder.

Depression remains an important risk factor for Alzheimer's disease, yet few neuroimaging biomarkers are available to identify treatment response in depression. To analyze and compare functional perfusion neuroimaging in persons with treatment resistant depression (TRD) compared to those experiencing full remission. A total of 951 subjects from a community psychiatry cohort were scanned with perfusion single photon emission computed tomography (SPECT) of the brain in both resting and task related settings. Of these, 78% experienced either full remission (n = 506) or partial remission (n = 237) and 11% were minimally responsive (n = 103) or non-responsive (11%. n = 106). Severity of depression symptoms were used to define these groups with changes in the Beck Depression Inventory prior to and following treatment. Voxel-based analyses of brain SPECT images from full remission compared to the worsening group was conducted with the statistical parametric mapping software, version 8 (SPM 8). Multiple comparisons were accounted for with a false discovery rate (p < 0.001). Persons with depression that worsened following treatment had reduced cerebral perfusion compared to full remission in the multiple regions including the bilateral frontal lobes, right hippocampus, left precuneus, and cerebellar vermis. Such differences were observed on both resting and concentration SPECT scans. Our findings identify imaging-based biomarkers in persons with depression related to treatment response. These findings have implications in understanding both depression to prognosis and its role as a risk factor for dementia.

Obsessive-compulsive disorder (OCD) and adolescent depression are debilitating conditions where standard treatments often yield suboptimal outcomes. Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique with established efficacy in adults with OCD, but its role in adolescent depression remains less defined. This systematic review aims to synthesize the current evidence on the efficacy, safety, and predictors of treatment response for TMS in adults with OCD and adolescents with depression. This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A systematic search of PubMed/MEDLINE, Embase, PsycINFO, Web of Science, IEEE Xplore, and ClinicalTrials.gov was performed for studies published between 2020 and 2025. Eligible studies included randomized controlled trials (RCTs) and non-randomized interventional studies of TMS in adults with OCD or adolescents with depression. Data on efficacy, safety, and predictors of response were extracted. Risk of bias was assessed using the Cochrane RoB 2 and ROBINS-I tools. Nine studies were included (six on OCD and three on adolescent depression). For OCD, four of six RCTs reported significant reductions in Y-BOCS scores with active TMS compared to sham, targeting regions such as the dorsal anterior cingulate cortex (dACC), orbitofrontal cortex (OFC), and supplementary motor area (SMA). Protocols were highly heterogeneous, including accelerated theta-burst stimulation. For adolescent depression, one RCT combining repetitive TMS (rTMS) with fluoxetine in first-episode patients showed very high response rates compared to sham. Two open-label studies in treatment-resistant depression reported symptom reduction and correlated neural changes, though they lacked control groups. TMS was generally well-tolerated, with mostly mild adverse events; one serious event was reported. Neurocognitive testing showed no negative effects. The investigation of predictors of response was notably limited, with only preliminary evidence suggesting roles for neurocognitive performance, neural activation patterns, and symptom subtype. TMS demonstrates promise as an effective and safe intervention for both adults with OCD and adolescents with depression, though the evidence base is still evolving. Significant variability in protocols and a lack of long-term follow-up data exist. The most critical gap is the absence of robust predictors to guide personalized treatment selection. Future research should prioritize large, rigorous trials that focus on identifying biomarkers and clinical factors predictive of responses to optimize TMS therapy for these populations.

People with severe depressive illness have raised levels of cortisol and reduced glucocorticoid receptor function. To obtain a physiological assessment of hypothalamic-pituitary-adrenal (HPA) axis feedback status in an in-patient sample with depression and to relate this to prospectively determined severe treatment resistance. The prednisolone suppression test was administered to 45 in-patients with depression assessed as resistant to two or more antidepressants and to 46 controls, prior to intensive multimodal in-patient treatment. The patient group had higher cortisol levels than controls, although the percentage suppression of cortisol output after prednisolone in comparison with placebo did not differ. Non-response to in-patient treatment was predicted by a more dysfunctional HPA axis (higher cortisol levels post-prednisolone and lower percentage suppression). In patients with severe depression, HPA axis activity is reset at a higher level, although feedback remains intact. However, prospectively determined severe treatment resistance is associated with an impaired feedback response to combined glucocorticoid and mineralocorticoid receptor activation by prednisolone.

Clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) for treatment of treatment-resistant depression (TRD) vary widely and there is no mood rating scale that is standard for assessing rTMS outcome. It remains unclear whether TMS is as efficacious in older adults with late-life depression (LLD) compared to younger adults with major depressive disorder (MDD). This study examined the effect of age on outcomes of rTMS treatment of adults with TRD. Self-report and observer mood ratings were measured weekly in 687 subjects ages 16-100 years undergoing rTMS treatment using the Inventory of Depressive Symptomatology 30-item Self-Report (IDS-SR), Patient Health Questionnaire 9-item (PHQ), Profile of Mood States 30-item, and Hamilton Depression Rating Scale 17-item (HDRS). All rating scales detected significant improvement with treatment; response and remission rates varied by scale but not by age (response/remission ≥ 60: 38%-57%/25%-33%; <60: 32%-49%/18%-25%). Proportional hazards models showed early improvement predicted later improvement across ages, though early improvements in PHQ and HDRS were more predictive of remission in those < 60 years (relative to those ≥ 60) and greater baseline IDS burden was more predictive of non-remission in those ≥ 60 years (relative to those < 60). These results indicate there is no significant effect of age on treatment outcomes in rTMS for TRD, though rating instruments may differ in assessment of symptom burden between younger and older adults during treatment.

Electroconvulsive therapy (ECT) is the most effective treatment for depression, yet its mechanism of action is unknown. Our goal was to investigate the neurobiological underpinnings of ECT response using longitudinally collected resting-state functional magnetic resonance imaging (rs-fMRI) in 16 patients with treatment-resistant depression and 10 healthy controls. Patients received bifrontal ECT 3 times a week under general anesthesia. We acquired rs-fMRI at three time points: at baseline, after the 1st ECT administration and after the course of the ECT treatment; depression was assessed with the Hamilton Depression Rating Scale (HAM-D). The primary measure derived from rs-fMRI was fractional amplitude of low frequency fluctuation (fALFF), which provides an unbiased voxel-wise estimation of brain activity. We also conducted seed-based functional connectivity analysis based on our primary findings. We compared treatment-related changes in HAM-D scores with pre- and post-treatment fALFF and connectivity measures. Subcallosal cingulate cortex (SCC) demonstrated higher BOLD signal fluctuations (fALFF) at baseline in depressed patients, and SCC fALFF decreased over the course of treatment. The baseline level of fALFF of SCC predicted response to ECT. In addition, connectivity of SCC with bilateral hippocampus, bilateral temporal pole, and ventromedial prefrontal cortex was significantly reduced over the course of treatment. These results suggest that the antidepressant effect of ECT may be mediated by downregulation of SCC activity and connectivity. SCC function may serve as an important biomarker of target engagement in the development of novel therapies for depression that is resistant to treatment with standard medications.

Understanding how age affects antidepressant response in patients with major depressive disorder has been complicated by small and heterogeneous studies. Yet, understanding how age-across the lifespan-contributes to variation in response could inform treatment selection across the lifespan. This study sought to identify how age impacts antidepressant response using participant-level data from large, NIH-sponsored trials in individuals with MDD aged 12-74 years. Participant-level data were abstracted from three NIH-sponsored trials of pharmacotherapy (Treatment of SSRI-Resistant Depression in Adolescents (TORDIA) Study, Treatment of Adolescent Depression Study (TADS), and the Combining Medications to Enhance Depression Outcomes Study (COMED)) in patients with MDD. Bayesian Hierarchical Models (BHMs) of individual treatment trajectories were developed using Hamiltonian Monte Carlo No U-Turn Sampling. The individual trajectory of improvement in depressive symptoms (Clinical Global Impression-Severity [CGI-S] and CGI-S equivalent from COMED) was modeled across studies and across individuals with logarithmic trend "random effects" coefficients BHMs. Age and sex (and their interaction) were examined categorically across patients. Study participants (N = 907) were 29.7 ± 17 years of age, 66.3% women, and had a mean baseline CGI-S score of 4.6 ± 0.9. Patients ≤21 years and those >55 years had slower and less response to pharmacotherapy compared to those aged 21-35. Additionally, women improved more than men, and this effect did not differ across ages. The patient's age should be considered in predicting antidepressant response, particularly in older and younger individuals who may benefit from other interventions to enhance treatment response.

Major depressive disorder (MDD) is a disabling condition affecting children, adolescents, and adults worldwide. A high proportion of patients do not respond to one or more pharmacological treatments and are said to have treatment-resistant or difficult-to-treat depression. Inadequate response to current treatments could be due to medication nonadherence, inter-individual variability in treatment response, misdiagnosis, diminished confidence in treatment after many trials, or lack of selectivity. Demonstrating an adequate response in the clinical trial setting is also challenging. Patients with depression may experience non-specific treatment effects when receiving placebo in clinical trials, which may contribute to inadequate response. Studies have attempted to reduce the placebo response rates using adaptive designs such as sequential parallel comparison design. Despite some of these innovations in study design, there remains an unmet need to develop more targeted therapeutics, possibly through precision psychiatry-based approaches to reduce the number of treatment failures and improve remission rates. Examples of precision psychiatry approaches include pharmacogenetic testing, neuroimaging, and machine learning. These approaches have identified neural circuit biotypes of MDD that may improve precision if they can be feasibly bridged to real-world clinical practice. Clinical biomarkers that can effectively predict response to treatment based on individual phenotypes are needed. This review examines why current treatment approaches for MDD often fail and discusses potential benefits and challenges of a more targeted approach, and suggested approaches for clinical studies, which may improve remission rates and reduce the risk of relapse, leading to better functioning in patients with depression.

This exploratory study sought to examine the effect of an acute course of high-frequency repetitive TMS on suicidal ideation in adolescents. Data were pooled from 3 prior protocols providing a 30-session course of open-label TMS treatment for adolescents with treatment-resistant depression. All participants (n = 19) were outpatients taking antidepressant medication, with TMS provided as adjunctive treatment. Suicidality was assessed at baseline, after 10 treatments, after 20 treatments, and after 30 treatments. Outcome measures of suicidal ideation included the Columbia Suicide Severity Rating Scale (C-SSRS) "Intensity of Ideation" subscale and Item 13 "Suicidality" on the Children's Depression Rating Scale, Revised (CDRS-R). The predicted odds of suicidal ideation (CDRS-R Item 13 and C-SSRS Intensity of Ideation subscale) significantly decreased over 6 weeks of acute TMS treatment without adjustments for illness (depression) severity. However, the magnitude of the decrease in the predicted odds of suicidal ideation across 6 weeks of treatment was attenuated and rendered non-significant in subsequent analyses that adjusted for illness (depression) severity. This was an exploratory study with a small sample size and no sham control. Regulatory and ethical barriers constrained enrollment of adolescents with severe suicidality. The present findings suggest that open-label TMS mitigated suicidal ideation in adolescents through the treatment and improvement of depressive symptom severity. Although caution is warranted in the interpretation of these results, the findings can inform the design and execution of future interventional trials targeting suicidal ideation in adolescents.

Depression is a debilitating mental health disorder that affects a significant portion of the adolescent population, and 20% of affected youths do not respond to two trials of treatment with antidepressants, i.e. suffer from treatment resistant depression (TRD). Repetitive transcranial magnetic stimulation (rTMS) has demonstrated efficacy in TRD in adults, but its application in the adolescent population remains relatively unexplored. This open label pilot study investigates the safety, feasibility, and potential efficacy of low-frequency (1 Hz) rTMS over the right dorsolateral prefrontal cortex (DLPFC) for adolescent TRD. Twenty patients, 13–18 years old, with treatment resistant unipolar- or bipolar depression were included (inclusion rate 0.9 patients per month) and two dropped out resulting in 18 participants completing the study (attrition rate 0.10). Daily sessions on 20–30 consecutive days with 1 Hz rTMS was administered. A significant decrease in depressive symptoms (decrease from median score of 35 to 23 on Montgomery-Asberg Depression Rating Scale, p < 0.01) and increase in function measures (increase of median score of 45 to 55 on Children’s Global Assessment Scale, p < 0.05) was observed at the end of treatment compared to baseline. However, only one patient reached the response criterium (< 50% score in Montgomery-Asberg Depression Rating Scale). Adverse effects such as scalp pain, headache, tiredness and nausea were common but tolerable, and tended to decrease during the course of treatment. In conclusion, the results of this study support feasibility of future larger studies of right frontal low-frequency rTMS for TRD in the adolescent population.

OBJECTIVE Adolescent depression is prevalent and is associated with significant morbidity and mortality. Although intravenous ketamine has shown efficacy in adult treatment-resistant depression, its efficacy in pediatric populations is unknown. The authors conducted an active-placebo-controlled study of ketamine's safety and efficacy in adolescents. METHODS In this proof-of-concept randomized, double-blind, single-dose crossover clinical trial, 17 adolescents (ages 13-17) with a diagnosis of major depressive disorder received a single intravenous infusion of either ketamine (0.5 mg/kg over 40 minutes) or midazolam (0.045 mg/kg over 40 minutes), and the alternate compound 2 weeks later. All participants had previously tried at least one antidepressant medication and met the severity criterion of a score >40 on the Children's Depression Rating Scale-Revised. The primary outcome measure was score on the Montgomery-Åsberg Depression Rating Scale (MADRS) 24 hours after treatment. RESULTS A single ketamine infusion significantly reduced depressive symptoms 24 hours after infusion compared with midazolam (MADRS score: midazolam, mean=24.13, SD=12.08, 95% CI=18.21, 30.04; ketamine, mean=15.44, SD=10.07, 95% CI=10.51, 20.37; mean difference=-8.69, SD=15.08, 95% CI=-16.72, -0.65, df=15; effect size=0.78). In secondary analyses, the treatment gains associated with ketamine appeared to remain 14 days after treatment, the latest time point assessed, as measured by the MADRS (but not as measured by the Children's Depression Rating Scale-Revised). A significantly greater proportion of participants experienced a response to ketamine during the first 3 days following infusion as compared with midazolam (76% and 35%, respectively). Ketamine was associated with transient, self-limited dissociative symptoms that affected participant blinding, but there were no serious adverse events. CONCLUSIONS In this first randomized placebo-controlled clinical trial of intravenous ketamine in adolescents with depression, the findings suggest that it is well tolerated acutely and has significant short-term (2-week) efficacy in reducing depressive symptoms compared with an active placebo.

Adolescence is a critical time period for the onset of depression and many patients do not respond to treatment. Transcutaneous auricular vagus nerve stimulation (taVNS) may be a promising alternative. Here we present the case of an adolescent girl with treatment resistant depression, receiving taVNS over the course of 7.5 months.

Objective: Ketamine has proved effective as a rapid-acting antidepressant agent, but treatment is not effective for everyone (approximately a quarter to a half of patients). Some adult studies have begun to investigate predictors of ketamine's antidepressant response, but no studies have examined this in adolescents with depression. Methods: We conducted a secondary data analysis of adolescents who participated in a randomized, single-dose, midazolam-controlled crossover trial of ketamine for adolescents with treatment-resistant depression. We examined the relationship between 19 exploratory demographic and clinical variables and depression symptom improvement (using the Montgomery-Åsberg Depression Rating Scale [MADRS]) at 1 and 7 days postinfusion. Results: Subjects who had fewer medication trials of both antidepressant medications and augmentation treatments were more likely to experience depression symptom improvement with ketamine. Subjects with shorter duration of their current depressive episode were more likely to experience depression symptom improvement with ketamine. Subjects currently being treated with selective serotonin reuptake inhibitor medications, and not being treated with serotonin–norepinephrine reuptake inhibitor medications, also experienced greater symptom improvement with ketamine. When receiving the midazolam control, less severe depressive symptoms, as measured by the Children's Depression Rating Scale (CDRS) (but not MADRS), and a comorbid attention-deficit/hyperactivity disorder diagnosis were associated with increased response. Conclusions: Findings should be viewed as preliminary and exploratory given the small sample size and multiple secondary analyses. Identifying meaningful predictors of ketamine response is important to inform future therapeutic use of this compound, however, considerably more research is warranted before such clinical guidance is established. The trial was registered in clinicaltrials.gov with the identifier NCT02579928.

Background: Adolescent treatment resistant depression (TRD) is increasing in recent years. While ketamine showed rapid antidepressant effects in adult TRD studies, research on its effectiveness in adolescents is limited. Methods: This study examines the effects of intravenous ketamine vs. midazolam on depressive and anxiety symptomatology assessed by the Montgomery–Åsberg Depression Rating Scale (MADRS), Hamilton Anxiety Rating Scale (HAM-A), and Children’s Depression Inventory (CDI) at two time points—2 h after initial infusion (T0+2h) and 24 h after the end of the treatment (Te+24h) in a sample of 55 adolescent TRD females (27 receiving ketamine, 28 midazolam). Results: At T0+2h, within-group comparisons revealed a significant reduction in MADRS and HAM-A scores compared to baseline in the ketamine and midazolam groups. At Te+24h, both groups demonstrated similar significant reductions in MADRS, HAM-A, and CDI scores compared to baseline. The MADRS assessment in the ketamine group showed 33% and 59% responders, and in the midazolam group, 14% and 46% responders at T0+2h and Te+24h, respectively. HAM-A evaluation in the ketamine group revealed 33% and 56% responders, and in the midazolam group, 11% and 39% responders at T0+2h and at Te+24h, respectively. CDI rating discovered 11% and 44% responders in the ketamine group and 4% and 21% responders in the midazolam group at T0+2h and Te+24h, respectively. Moreover, inner tension significantly decreased in ketamine compared to the midazolam group at Te+24h. Conclusions: Ketamine showed a reduction in depressive and anxiety symptoms during a short-term period with particular efficacy in alleviating inner tension over midazolam, suggesting its potential advantages in specific symptom relief in rarely studied adolescent TRD.

This study presents a 16-year-old male with treatment-resistant depression characterised by psychotic symptoms (visual hallucinations) and recurrent suicidal behaviours. Functional magnetic resonance imaging confirmed biological underpinnings of the disorder, revealing significantly reduced prefrontal-limbic functional connectivity (r = 0.18) compared with healthy controls (r = 0.52, p < 0.001). Polysomnography demonstrated shortened REM latency (45 min) and increased REM density. Following 10 sessions of magnetic seizure therapy, Hamilton Depression Rating Scale (HAMD-17) scores decreased from 36 to 6, and Positive and Negative Syndrome Scale (PANSS) scores declined from 105 to 38. Six-month follow-up showed sustained remission with normalised neural circuit connectivity (r = 0.42) on functional magnetic resonance imaging. This first reported case highlights magnetic seizure therapy’s rapid anti-suicidal effects in adolescent treatment-resistant depression (self-harm cessation within 72 hours), with clinical improvement significantly correlated to neuroplasticity marker dynamics (r = 0.82). These findings provide mechanistic evidence for the therapeutic effects of magnetic seizure therapy’s.

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Transgender adolescents experience disproportionately high rates of depression and suicidality, yet effective interventions for those unresponsive to conventional treatments remain scarce. This case report describes the case of a 15-year-old transgender male with severe treatment-resistant depression and active suicidal ideation who underwent an accelerated intermittent theta burst stimulation (iTBS) protocol: 20 sessions over four consecutive days targeting the left dorsolateral prefrontal cortex. Baseline scores indicated severe depression (Patient Health Questionnaire-9 (PHQ-9) = 22) and high suicide risk (Suicidal Ideation Attributes Scale (SIDAS) = 35). At the one-month follow-up, the patient's symptoms improved to minimal depression (PHQ-9 = 4) and below the high-risk suicide cutoff (SIDAS = 10). He reported improved mood, reduced suicidal thoughts, and no adverse effects. To the author's knowledge, this is the first published case of accelerated iTBS in a transgender adolescent demonstrating rapid and substantial symptom reduction with good tolerability. These findings suggest accelerated iTBS may be a safe and time-sensitive option for crisis stabilization in transgender youth when traditional approaches fail, warranting further research in larger cohorts.

Major depressive disorder (MDD) is a psychiatric illness that can jeopardize the normal growth and development of adolescents. Approximately 40% of adolescent patients with MDD exhibit resistance to conventional antidepressants, leading to the development of Treatment-Resistant Depression (TRD). TRD is associated with severe impairments in social functioning and learning ability and an elevated risk of suicide, thereby imposing an additional societal burden. In this study, we conducted plasma metabolomic analysis on 53 adolescents diagnosed with first-episode drug-naïve MDD (FEDN-MDD), 53 adolescents with TRD, and 56 healthy controls (HCs) using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) and reversed-phase liquid chromatography-mass spectrometry (RPLC-MS). We established a diagnostic model by identifying differentially expressed metabolites and applying cluster analysis, metabolic pathway analysis, and multivariate linear support vector machine (SVM) algorithms. Our findings suggest that adolescent TRD shares similarities with FEDN-MDD in five amino acid metabolic pathways and exhibits distinct metabolic characteristics, particularly tyrosine and glycerophospholipid metabolism. Furthermore, through multivariate receiver operating characteristic (ROC) analysis, we optimized the area under the curve (AUC) and achieved the highest predictive accuracy, obtaining an AUC of 0.903 when comparing FEDN-MDD patients with HCs and an AUC of 0.968 when comparing TRD patients with HCs. This study provides new evidence for the identification of adolescent TRD and sheds light on different pathophysiologies by delineating the distinct plasma metabolic profiles of adolescent TRD and FEDN-MDD.

Objective: Ketamine has proven effective as a rapid-acting antidepressant agent. Several adult studies have investigated the association between ketamine's acute dissociative effects and depression response, but no studies have examined the association in adolescents with treatment-resistant depression (TRD). Methods: We conducted a secondary data analysis of 16 adolescent participants who participated in a randomized, single-dose, midazolam-controlled crossover trial of ketamine in adolescents with depression. We examined the association between the acute dissociative symptoms (measured at 60 minutes following start of infusion using the Clinician-Administered Dissociative States Scale [CADSS], and its three subscales: depersonalization, derealization, amnesia) and response and depression symptom improvement at 1'day (using the Montgomery–Åsberg Depression Rating Scale). Results: Within the ketamine group, there were no significant associations between dissociation symptoms or CADSS subscale scores and magnitude of depression symptom improvement or likelihood of ketamine response. When receiving midazolam, there was no significant association between overall dissociation symptoms and magnitude or likelihood of response of depressive symptoms. Higher levels of symptoms on the ‘depersonalization’ CADSS subscale when receiving midazolam were associated with less improvement in depression symptoms at 1 day following infusion. Conclusions: In contrast to some adult literature, the current data do not show a relationship between acute dissociative effects and antidepressant response to ketamine in pediatric patients with TRD. Interpretation may be limited by the small sample size, reducing the power to detect small or medium associations. Future research should utilize larger samples to more definitively measure the magnitude of association between acute dissociative symptoms and later antidepressant response to ketamine and to assess the relationship to trial design (e.g., crossover vs. parallel trial, comparison condition utilized and number of infusions) within both adult and pediatric populations. ClinicalTrials.gov identifier: NCT02579928.

Depression is among the most common mental health disorders worldwide and treatment resistant depression (TRD) represents a major challenge for both patients and clinicians. In recent years ketamine has received attention as an antidepressant agent, demonstrating promising results in TRD in adults. To date, few attempts have been made in treating adolescent TRD with ketamine and none have used intranasal application. This paper discusses a case of a 17-year-old female adolescent suffering from TRD who underwent treatment with intranasal esketamine application (Spravato 28 mg). As symptoms showed clinically insignificant improvement despite modest gains in objective assessments (GAF, CGI, MADRS), treatment was prematurely discontinued. However, the treatment was tolerable and side effects were scarce and mild. Although this case report does not demonstrate clinical effectiveness, ketamine may nonetheless be a promising substance in treating TRD in other adolescents. Questions regarding the safety of ketamine use in the rapidly developing brains of adolescents still remain unanswered. To further explore the potential benefits of this treatment method a short term RCTs for adolescents with TRD is recommended.

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Previous studies have shown an association between depression and increased susceptibility to infection in the general population. However, there has been no prior research specifically examining the relationship between treatment-resistant depression (TRD) and severe bacterial infections (SBI) in adolescents and young adults. This retrospective observational cohort study utilized the Taiwan National Health Insurance Research Database (NHIRD) from 2001 to 2010. It included adolescents (12–19 years of age) and young adults (20–29 years of age) diagnosed with major depressive disorder (MDD), comprising 6958 cases of TRD and 27,832 cases of antidepressant-responsive depression (ARPD). The TRD and ARPD groups were further matched (4:1) by chronological age, age at diagnosis of depression, sex, residence, and family income. The primary outcomes were severe bacterial infections (SBI) and septicemia. Cox regression analysis was conducted to identify the risk of hospitalization due to SBI or septicemia during the follow-up period. Compared with controls, the ARPD group had increased risks of SBI (hazard ratio [HR] with 95% confidence interval [CI]: 3.90, 2.73–5.57) and septicemia (HR, 95% CI: 2.56, 1.34–4.91). Notably, the risks of SBI and septicemia appeared to be further elevated in the TRD group. The TRD group exhibited higher incidences of SBI (HR, 95% CI: 6.99, 4.73–10.34) and septicemia (HR, 95% CI: 2.85, 1.28–6.36) than the control group. Adolescents and young adults with TRD had 6.99-fold and 3.90-fold increased risks of SBI and septicemia compared to individuals without MDD, respectively. Therefore, healthcare providers need to be vigilant when monitoring and implementing preventive measures in this population.

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Introduction: Transcranial magnetic stimulation (TMS) is a promising intervention for adolescents with treatment-resistant depression (TRD). However, the durability of TMS-related improvement in adolescents is unclear. This 6-month study followed adolescents with TRD who had responded to TMS and provided TMS retreatment for adolescents with a partial relapse. Methods: The study enrolled adolescents (12–21 years) with TRD who had at least a partial response to sham or active TMS in a randomized controlled trial. Partial response was defined as ≥25% reduction of Hamilton Depression Rating Scale-24 (HAMD24). Participants with a partial relapse (≥1 point increase in Clinical Global Impression–Severity) received retreatment with daily 10 Hz TMS sessions until depressive symptom severity returned to the baseline score or after 30 TMS treatments. Results: There were 84 eligible participants, 66 were enrolled, and 41 completed the 6-month study. Twenty-eight participants (42%) were retreated with TMS. TMS retreatment courses had a mean of 22 sessions. At the 6-month follow-up, the complete sample exhibited reduced depressive symptoms (mean HAMD24 of 5.24) compared with baseline at entry into follow-up (mean HAMD24 of 8.21). Baseline depressive symptom severity was positively correlated with the risk of partial relapse, while the number of previous TMS interventions showed no correlation with the risk of partial relapse. TMS was well tolerated. Conclusions: This is the largest, long-term follow-up study with TMS retreatment for adolescents with TRD. These findings demonstrate the feasibility and clinical effects of a TMS retreatment protocol for adolescents with TRD, following a standard course of acute TMS.

BACKGROUND Transcranial magnetic stimulation (TMS) is an intervention for treatment-resistant depression (TRD) that modulates neural activity. Deep TMS (dTMS) can target not only cortical but also deeper limbic structures implicated in depression. Although TMS has demonstrated safety in adolescents, dTMS has yet to be applied to adolescent TRD. OBJECTIVE/HYPOTHESIS This pilot study evaluated the safety, tolerability and clinical effects of dTMS in adolescents with TRD. We hypothesized dTMS would be safe, tolerable, and efficacious for adolescent TRD. METHODS 15 adolescents with TRD (Age, years: M = 16.4, SD = 1.42) completed a six-week daily dTMS protocol targeting the left dorsolateral prefrontal cortex (BrainsWay H1 coil, 30 sessions, 10 Hz, 3.6 s train duration, 20s inter-train interval, 55 trains; 1980 total pulses per session, 80 % to 120 % of motor threshold). Participants completed clinical, safety, and neurocognitive assessments before and after treatment. The primary outcome was depression symptom severity measured by the Child Depression Rating Scale-Revised (CDRS-R). RESULTS 14 out of 15 participants completed the dTMS treatments. One participant experienced a convulsive syncope; the other participants only experienced mild side effects (e.g., headaches). There were no serious adverse events and minimal to no change in cognitive performance. Depression symptom severity significantly improved pre- to post-treatment and decreased to a clinically significant degree after 10 treatment sessions. Six participants met criteria for treatment response. LIMITATIONS Main limitations include a small sample size and open-label design. CONCLUSIONS These findings provide preliminary evidence that dTMS may be tolerable and associated with clinical improvement in adolescent TRD.

BACKGROUND Major depressive disorder (MDD) is highly prevalent in youth. Conventional treatment paradigms primarily involve selective serotonin reuptake inhibitors (SSRIs) and psychotherapy, yet a significant proportion of this population exhibits treatment-resistant depression (TRD). In adults, interventional therapies like Electroconvulsive Therapy (ECT), repetitive Transcranial Magnetic Stimulation (rTMS), and ketamine have shown promise for TRD, but their comparative efficacy remains underexplored in Adolescent and pediatric population. This systematic review and meta-analysis aims to assess the relative effectiveness of ECT, rTMS, and ketamine in treating TRD among adolescents. METHODS Following PRISMA guidelines, we systematically searched databases for studies of ECT, rTMS, or ketamine for treatment-resistant depression in youth ages 10-24. Three reviewers independently screened for inclusion based on predefined criteria. Included observational and randomized controlled trials reported depression symptoms with measures like HDRS and MADRS in youth treated with ECT, rTMS, or ketamine. Two reviewers extracted data on interventions, patients, and depression symptom outcomes. Chance-adjusted inter-reviewer agreement was calculated. For meta-analysis, we pooled standardized mean differences (SMDs) in depression scores using random effects models and assessed heterogeneity with I2 statistics. RESULTS Meta-analysis of 10 observational studies examined SMD in depression scores for treatment resistant depression patients treated with ECT, ketamine, or rTMS. Patients treated with ECT had a significantly lower SMD of 1.99 (95 % CI 0.92-3.05, p < 0.001) compared to baseline. Patients treated with ketamine also had a significantly lower SMD of 1.58 (95 % CI 1.04-2.12, p < 0.001). Patients treated with rTMS had the lowest SMD of 2.79 (95 % CI 0.79-4.80, p = 0.006). There was no significant difference between the three groups overall (p > 0.05). Comparative analysis between ECT and ketamine found no significant difference in SMD (p = 0.387). Comparison of ECT versus rTMS found a significant difference in SMD favoring rTMS (p = 0.004). Comparison of ketamine versus rTMS suggested a potential difference in SMD favoring rTMS (p = 0.058). In summary, rTMS resulted in significantly larger reductions in depression scores than ECT, and potentially larger reductions than ketamine. CONCLUSIONS This meta-analysis illustrates the ability of rTMS, ECT, and ketamine to improve depression in youth. rTMS resulted in the largest improvements, highlighting its potential as a first-line treatment for pediatric treatment-resistant depression given its favorable side effect profile compared to ECT. Further research directly comparing these modalities is needed.

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BACKGROUND Adolescent major depressive disorder (MDD) is a significant health problem, associated with substantial morbidity, cost, and mortality. Depression is a significant risk factor for suicide, which is now the second leading cause of death in young people. Up to twenty per cent of adolescents will experience MDD before adulthood, and while a substantial proportion will improve with standard-of-care treatments (psychotherapy and medication), roughly one third will not. METHODS Here, we have reviewed the literature in order to discuss the concept of treatment-resistant depression (TRD) in adolescence, examine risk factors, diagnostic difficulties, and challenges in evaluating symptom improvement, and providing guidance on how to define adequate medication and psychotherapy treatment trials. RESULTS We propose a staging model for adolescent TRD and review the treatment literature. The evidence base for first- and second-line treatments primarily derives from four large pediatric clinical trials (TADS, TORDIA, ADAPT, and IMPACT). After two medications and a trial of evidence-based psychotherapy have failed to alleviate depressive symptoms, the evidence becomes quite thin for subsequent treatments. Here, we review the evidence for the effectiveness of medication switches, medication augmentation, psychotherapy augmentation, and interventional treatments (i.e., transcranial magnetic stimulation, electroconvulsive therapy, and ketamine) for adolescent TRD. Comparisons are drawn to the adult TRD literature, and areas for future pediatric depression research are highlighted. CONCLUSIONS As evidence is limited for treatments in this population, a careful consideration of the known risks and side effects of escalated treatments (e.g., mood stabilizers and atypical antipsychotics) is warranted and weighed against potential, but often untested, benefits.

Objective: To describe the clinical characteristics of adolescents with antidepressant treatment-resistant major depressive disorder (MDD) and to examine the utility of the Antidepressant Treatment Record (ATR) in categorizing treatment resistance in this population. Methods: Adolescents with treatment-resistant MDD enrolled in an interventional study underwent a baseline evaluation with the ATR, Children's Depression Rating Scale-Revised (CDRS-R), and Clinical Global Impressions-Severity (CGI-S) scales. Demographic and clinical characteristics were examined with regard to ATR-defined level of resistance (level 1 to ≥3) using analysis of variance and χ2 tests. Results: In adolescents with treatment-resistant MDD (N = 97), aged 12–21 years, most were female (65%), white (89%), and had recurrent illness (78%). Patients were severely ill (median CGI-S score of 5), had a mean CDRS-R score of 63 ± 10, and 17.5% had been hospitalized for depression-related symptoms. Fifty-two patients were classified as ATR 1, whereas 32 were classified as ATR level 2 and 13 patients as ≥3, respectively. For increasing ATR-defined levels, illness duration increased from 12.0 (range: 1.5–31.9) to 14.8 (range: 1.8–31.7) to 19.5 (range: 2.5–36.2) months and the likelihood of treatment with serotonin norepinephrine reuptake inhibitors (SNRIs) and dopamine norepinephrine reuptake inhibitors (DNRIs) similarly increased (p = 0.006 for both SNRIs and DNRIs) as did the likelihood of treatment with mixed dopamine serotonin receptor antagonists (χ2 = 17, p < 0.001). Conclusions: This study underscores the morbidity and chronicity of treatment-resistant MDD in adolescents. The present characterization of related clinical features describes the use of nonselective serotonin reuptake inhibitors in adolescents with treatment-resistant depression and raises the possibility that those with the greatest medication treatment resistance are less likely to have had recurrent episodes. The study also demonstrates the utility of the ATR in categorizing treatment resistance in adolescents with MDD.

Background: Novel interventions for treatment-resistant depression (TRD) in adolescents are urgently needed. Ketamine has been studied in adults with TRD, but little information is available for adolescents. This study investigated efficacy and tolerability of intravenous ketamine in adolescents with TRD, and explored clinical response predictors. Methods: Adolescents, 12–18 years of age, with TRD (failure to respond to two previous antidepressant trials) were administered six ketamine (0.5 mg/kg) infusions over 2 weeks. Clinical response was defined as a 50% decrease in Children's Depression Rating Scale-Revised (CDRS-R); remission was CDRS-R score ≤28. Tolerability assessment included monitoring vital signs and dissociative symptoms using the Clinician-Administered Dissociative States Scale (CADSS). Results: Thirteen participants (mean age 16.9 years, range 14.5–18.8 years, eight biologically male) completed the protocol. Average decrease in CDRS-R was 42.5% (p = 0.0004). Five (38%) adolescents met criteria for clinical response. Three responders showed sustained remission at 6-week follow-up; relapse occurred within 2 weeks for the other two responders. Ketamine infusions were generally well tolerated; dissociative symptoms and hemodynamic symptoms were transient. Higher dose was a significant predictor of treatment response. Conclusions: These results demonstrate the potential role for ketamine in treating adolescents with TRD. Limitations include the open-label design and small sample; future research addressing these issues are needed to confirm these results. Additionally, evidence suggested a dose–response relationship; future studies are needed to optimize dose. Finally, questions remain regarding the long-term safety of ketamine as a depression treatment; more information is needed before broader clinical use.

Despite antidepressant treatment, some patients continue to experience significant symptoms of depression. Literature has demonstrated modest benefit of folate supplementation in treatment-resistant depression among adults, though evidence is lacking in the pediatric population. This case series describes 10 adolescents (mean age 14.4 ± 2.8 years) with treatment-resistant depression prescribed adjunctive l-methylfolate (LM). The patient population was predominantly female (80%), Caucasian (90%), with an average of three comorbid psychiatric diagnoses, and failures of three psychotropic medications before starting LM. The majority of patients (80%) had a single mutation among the two methylene tetrahydrofolate reductase (MTHFR) gene variants evaluated (50% A1298 AC; 30% C677 CT), indicating reduced MTHFR activity. Eighty percent of patients demonstrated improvement in depression, anxiety, and irritability. Overall, LM was well tolerated. These cases suggest that LM as an adjunct to antidepressant treatment may be a safe and effective strategy for managing treatment-resistant depression in pediatric patients.

Objective ‘Treatment-resistant depression’ is depression that does not respond to an adequate regimen of evidence-based treatment. Treatment-resistant depression frequently becomes chronic. Children with treatment-resistant depression might also develop bipolar disorder (BD). The objective of this study was to determine whether serum levels of oxytocin (OXT) in treatment-resistant depression in adolescents (TRDIA) differ from non-treatment-resistant depression in adolescents (non-TRDIA) or controls. We also investigated the relationships between serum OXT levels and the clinical symptoms, severity, and familial histories of adolescent depressive patients. Methods We measured serum OXT levels: TRDIA (n = 10), non-TRDIA (n = 27), and age- and sex- matched, neurotypical controls (n = 25). Patients were evaluated using the Children’s Depression Rating Scale-Revised (CDRS-R) and the Depression Self-Rating Scale for Children-Japanese Version (DSRS-C-J). The patients were also assessed retrospectively using the following variables: familial history of major depressive disorder and BD (1st degree or 2nd degree), history of disruptive mood dysregulation disorder, recurrent depressive disorder (RDD), history of antidepressant activation. Results Serum levels of OXT among the TRDIA and non-TRDIA patients and controls differed significantly. Interestingly, the rates of a family history of BD (1st or 2nd degree), RDD and a history of antidepressant activation in our TRDIA group were significantly higher than those of the non-TRDIA group. Conclusions Serum levels of OXT may play a role in the pathophysiology of TRDIA.

Severe depressive disorders with psychotic features in adolescents present diagnostic and therapeutic challenges, often exceeding the limitations of existing guidelines. A 19-year-old patient with four-year history of treatment-resistant severe depression and psychotic features, addressing a broad spectrum of symptoms, including persistent negative symptoms, cognitive impairments, and motor disturbances such as bradykinesia and hypokinesia, was comprehensively assessed. The therapeutic trajectory included multiple pharmacological regimens, neuroimaging evaluations, and standardized scales such as the Positive and Negative Syndrome Scale (PANSS) to monitor clinical outcomes. Initial treatments yielded limited improvements. However, the final regimen resulted in significant symptomatic relief, reflected by marked reductions in PANSS scores (from 105 to 65 over six months). Neuroimaging revealed structural anomalies, including a medial temporal dysplastic lesion and pituitary microadenoma, which contributed to the psychopathology. Symptoms such as anhedonia, avolition, alogia, bradykinesia, episodic derealization, and cognitive impairments showed substantial improvement following tailored interventions. This case underscores the importance of personalized treatment strategies that integrate advanced neuroimaging and innovative pharmacological approaches to address atypical presentations of treatment-resistant psychiatric disorders effectively. Quantifiable improvements in PANSS scores highlight the efficacy of such interventions in managing complex adolescent psychopathologies.

Adolescent major depressive disorder (MDD) is a prevalent and serious mental health condition. Pharmacological treatments are commonly used but often have poor tolerability and severe side effects, such as suicidal ideation. Deep transcranial magnetic stimulation (TMS) is currently cleared treating MDD in individuals 22-86 years old. This post-marketing surveillance study was designed to evaluate the safety and efficacy of using this tool as a treatment for MDD in younger patients. Data were collected from 56 sites, resulting in 1257 patients that met inclusion criteria (e.g. 11-21 years old, H1 coil, 18 Hz or iTBS, treatment-resistant MDD). Treatment was well tolerated in these younger patients, with an adverse event rate comparable to adults. After 30 sessions the response/remission rates were HDRS: 58.3 %/ 48.6 %, PHQ9:64.4 %, 25.5 %. After 36 sessions the response/remission rates were HDRS: 75.0 %/58.3 %, PHQ9:74.6 %/34.6 %. Median onset of response & remission was after 13 & 20 sessions respectively. Kaplan-Meier analysis showed 80 % of patients responded by 20 sessions and 90 % by 36 sessions. The outcomes from iTBS (1800 pulses) and 18 Hz were not significantly different (Fisher's exact test, p>0.05). There was a significant decrease in depression severity after treatment (p<0.0001, Chi-square test; PHQ-9 categorical distribution). Anxiety symptoms also improved in the majority of patients, with a response/remission rate of 66 %/40.2 % after 36 sessions (GAD-7). As the largest naturalistic study to date, these data demonstrate Deep TMS is a safe and effective therapeutic option for adolescents and young adults suffering from MDD when delivered under routine outpatient treatment conditions.