The Application of EEG in Olfactory Research

Introduction Alzheimer's disease has been considered one of the most dangerous neurodegenerative health problems. This disease, which is characterized by memory loss, leads to conditions that adversely affect daily life. Early diagnosis is crucial for effective treatment and is achieved through various imaging technologies. However, these methods are quite costly and their results depend on the expertise of the specialist physician. Therefore, deep learning techniques have recently been utilized as decision support tools for Alzheimer's disease. Methods In this research, the detection of Alzheimer's disease was investigated using a deep learning model applied to electroencephalography signals, taking advantage of olfactory memory. The dataset comprises three categories: healthy individuals, those with amnestic mild cognitive impairment, and Alzheimer's disease patients. The proposed model integrates three distinct feature types through a transformer-based fusion approach for classification. These feature vectors are derived from the Common Spatial Pattern, Covariance matrix-Tangent Space and a Tunable Q-Factor wavelet coefficient mapping. Results The results demonstrated that subject-based classification of rose aroma attained a 93.14% accuracy using EEG-recorded olfactory memory responses. Conclusion This output has demonstrated superiority over EEG-based results reported in the literature.

As the need for food authenticity verification increases, sensory evaluation of food odors has become widely recognized. This study presents a theory based on electroencephalography (EEG) to create an Olfactory Perception Dimensional Space (EEG-OPDS), using feature engineering and ensemble learning to establish material and emotional spaces based on odor perception and pleasure. The study examines the intrinsic connection between these two spaces and explores the mechanisms of integration and differentiation in constructing the OPDS. This method effectively visualizes various types of food odors while identifying their perceptual intensity and pleasantness. The average classification accuracy for odor recognition in an eight-category experiment is 96.1%. Conversely, the average classification accuracy for sensory pleasantness recognition in a two-category experiment is 98.8%. The theoretical approach proposed in this study, based on olfactory EEG signals to construct an OPDS, captures the subtle perceptual differences and individualized pleasantness responses to food odors.

At present, the sensory evaluation of food mostly depends on artificial sensory evaluation and machine perception, but artificial sensory evaluation is greatly interfered with by subjective factors, and machine perception is difficult to reflect human feelings. In this article, a frequency band attention network (FBANet) for olfactory electroencephalogram (EEG) was proposed to distinguish the difference in food odor. First, the olfactory EEG evoked experiment was designed to collect the olfactory EEG, and the preprocessing of olfactory EEG, such as frequency division, was completed. Second, the FBANet consisted of frequency band feature mining and frequency band feature self-attention, in which frequency band feature mining can effectively mine multiband features of olfactory EEG with different scales, and frequency band feature self-attention can integrate the extracted multiband features and realize classification. Finally, compared with other advanced models, the performance of the FBANet was evaluated. The results show that FBANet was better than the state-of-the-art techniques. In conclusion, FBANet effectively mined the olfactory EEG data information and distinguished the differences between the eight food odors, which proposed a new idea for food sensory evaluation based on multiband olfactory EEG analysis.

BACKGROUND Decoding olfactory-induced electroencephalography (olfactory EEG) signals has gained significant attention in recent years, owing to its potential applications in several fields, such as disease diagnosis, multimedia applications, and brain-computer interaction (BCI). Extracting discriminative features from olfactory EEG signals with low spatial resolution and poor signal-to-noise ratio is vital but challenging for improving decoding accuracy. NEW METHODS By combining discrete wavelet transform (DWT) with one-versus-rest common spatial pattern (OVR-CSP), we develop a novel feature, named wavelet-spatial domain feature (WSDF), to decode the olfactory EEG signals. First, DWT is employed on EEG signals for multilevel wavelet decomposition. Next, the DWT coefficients obtained at a specific level are subjected to OVR-CSP for spatial filtering. Correspondingly, the variance is extracted to generate a discriminative feature set, labeled as WSDF. RESULTS To verify the effectiveness of WSDF, a classification of olfactory EEG signals was conducted on two data sets, i.e., a public EEG dataset 'Odor Pleasantness Perception Dataset (OPPD)', and a self-collected dataset, by using support vector machine (SVM) trained based on different cross-validation methods. Experimental results showed that on OPPD dataset, the proposed method achieved a best average accuracy of 100% and 94.47% for the eyes-open and eyes-closed conditions, respectively. Moreover, on our own dataset, the proposed method gave a highest average accuracy of 99.50%. COMPARISON WITH EXISTING METHODS Compared with a wide range of EEG features and existing works on the same dataset, our WSDF yielded superior classification performance. CONCLUSIONS The proposed WSDF is a promising candidate for decoding olfactory EEG signals.

Olfactory-induced electroencephalogram (EEG) signal classification is of great significance in a variety of fields, such as disorder treatment, neuroscience research, multimedia applications and brain-computer interface. In this paper, a trapezoid difference-based electrode sequence hashing method is proposed for olfactory EEG signal classification. First, an N-layer trapezoid feature set whose size ratio of the top, bottom and height is 1:2:1 is constructed for each frequency band of each EEG sample. This construction is based on N optimized power-spectral-density features extracted from N real electrodes and N nonreal electrode's features. Subsequently, the N real electrodes' sequence (ES) codes of each layer of the constructed trapezoid feature set are obtained by arranging the feature values in ascending order. Finally, the nearest neighbor classification is used to find a class whose ES codes are the most similar to those of the testing sample. Thirteen-class olfactory EEG signals collected from 11 subjects are used to compare the classification performance of the proposed method with six traditional classification methods. The comparison shows that the proposed method gives average accuracy of 94.3%, Cohen's kappa value of 0.94, precision of 95.0%, and F1-measure of 94.6%, which are higher than those of the existing methods.

Objective. The classification of olfactory-induced electroencephalogram (olfactory EEG) signals has potential applications in disease diagnosis, emotion regulation, multimedia, and so on. To achieve high-precision classification, numerous EEG channels are usually used, but this also brings problems such as information redundancy, overfitting and high computational load. Consequently, channel selection is necessary to find and use the most effective channels. Approach. In this study, we proposed a multi-strategy fusion binary harmony search (MFBHS) algorithm and combined it with the Riemannian geometry classification framework to select the optimal channel sets for olfactory EEG signal classification. MFBHS was designed by simultaneously integrating three strategies into the binary harmony search algorithm, including an opposition-based learning strategy for generating high-quality initial population, an adaptive parameter strategy for improving search capability, and a bitwise operation strategy for maintaining population diversity. It performed channel selection directly on the covariance matrix of EEG signals, and used the number of selected channels and the classification accuracy computed by a Riemannian classifier to evaluate the newly generated subset of channels. Main results. With five different classification protocols designed based on two public olfactory EEG datasets, the performance of MFBHS was evaluated and compared with some state-of-the-art algorithms. Experimental results reveal that our method can minimize the number of channels while achieving high classification accuracy compatible with using all the channels. In addition, cross-subject generalization tests of MFBHS channel selection show that subject-independent channels obtained through training can be directly used on untrained subjects without greatly compromising classification accuracy. Significance. The proposed MFBHS algorithm is a practical technique for effective use of EEG channels in olfactory recognition.

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Electroencephalography (EEG) enables the investigation of olfactory perception through neuronal electrical activity. Decoding dynamic oscillatory changes in sensory-cognitive processing is critical to understanding odor-induced brain responses. First, the EEG signals of almond were obtained and transformed into the frequency domain. Welch's method was implemented to extract power spectral density (PSD). Subsequently, the power spectral analysis of brain responses across different regions and frequency bands was investigated. Moreover, the machine learning approach was employed to explore the primary discriminative features. As a result, pronounced oscillatory activity was obtained in delta and alpha bands inducing distinct spatial-frequency responses of increased δ-power in left temporal region and β-power in parieto-occipital region. Critically, the β-band frequencies of 18 Hz and 25.5 Hz, and channels of FP2, FZ, and C3 were confirmed as key features contributing to olfactory analysis. This study provides valuable insights for olfactory perception and applications for quality assessment and storage monitoring.

Cognitive disturbances following COVID-19 have been widely reported, yet the neural dynamics underpinning such phenomena remain incompletely understood. This exploratory study examined cortical neurodynamics using electroencephalographic (EEG) analysis in three groups: individuals with severe COVID-19 (Group S), individuals recovered from moderate COVID-19 (Group M), and healthy controls (Group H). EEG recordings were obtained during the resting state and exposure to three odorants—ammonia (trigeminal), isoamyl acetate (olfactory), and mountain pine (mixed)—to assess reactivity under different sensory conditions. Power Spectral Density (PSD) and detrended moving average (DMA) analyses were applied to quantify both spectral power and long-range temporal correlations, respectively. Group S showed consistently elevated β-band PSD and α-scaling exponent values across all conditions, indicative of globally rigid and hyperexcitable dynamics. Group M exhibited partially recovered oscillatory patterns, including α3 enhancements, without statistically significant stimulus-driven modulation. Group H maintained physiologically typical EEG responses with limited olfactory reactivity. While these results suggest differential patterns of neurodynamic adaptation and rigidity among groups, interpretations regarding cognitive status remain tentative due to the absence of behavioral or neuropsychological testing. The findings underscore the utility of DMA as a complementary EEG analysis tool and provide a basis for hypothesis-driven research on post-COVID cortical reorganization. Future studies incorporating direct cognitive measures are essential to validate EEG-based biomarkers of brain function.

Olfactory impairment is an early symptom of Alzheimer's disease (AD). However, currently used olfactory task-based functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalogram features are not powerful enough to detect the impairment. To address this issue, we propose an explainable Artificial Intelligence (XAI) framework that comprises discriminant analysis/naive Bayes/thresholding classifiers driven by the sample entropy (SE) of olfactory event-related potentials (OERPs) at the Fz/Pz electrodes. The proposed XAI framework exhibits a higher accuracy (92.14%) than methods in the literature, namely, support vector machine (88.20%), logistic regression (67.42%), thresholding (82.5%), and light gradient boosting (80.68%) classifiers fed respectively by inter-electrode β-γ magnitude squared coherence of OERPs, P2 latency of OERPs, fMRI activation pattern of primary olfactory cortex, and NIRS oxygenation difference in the orbito-frontal cortex. Reduction in SE in AD patients is caused by low dynamicity of OERPs as a consequence of diminished sensitivity to the olfactory stimulus.

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Effectively regulating anger driving has become critical in ensuring road safety. The existing research lacks a feasible exploration of anger-driving regulation. This paper delves into the effect and neural mechanisms of emotional olfactory stimuli (EOS) on regulating anger driving based on EEG. First, this study designed an angry driving regulation experiment based on EOS to record EEG signals. Second, brain activation patterns under various EOS conditions are explored by analyzing functional brain networks (FBNs). Additionally, the paper analyzes dynamic alterations in anger-related characteristics to explore the intensity and persistence of regulating anger driving under different EOS. Finally, the paper studies the frequency energy of EEG changes under EOS through time-frequency analysis. The results indicate that EOS can effectively regulate a driver's anger emotions, especially with the banana odor showing superior effects. Under banana odor stimulus, synchronization between the parietal and temporal lobes significantly decreased. Notably, the regulatory effect of banana odor is optimal and exhibits sustained efficacy. The regulatory effect of banana odor on anger emotions is persistent. Furthermore, the impact of banana odor significantly reduces the distribution of high-energy activation states in the parietal lobe region. Our findings provide new insights into the dynamic characterization of functional connectivity during anger-driving regulation and demonstrate the potential of using EOS as a reliable tool for regulating angry driving.

The sense of smell, or olfaction, can enhance brain-computer interfaces (BCIs). Different scents can be assigned to specific commands to allow users to interact with technology naturally, but challenges remain. Accurate odor delivery systems and robust algorithms for detecting and interpreting brain activity patterns are necessary. We propose combining electroencephalography (EEG) and electrobulbography (EBG) to improve classification accuracy. Our pilot study shows promising results for a new olfactory brain-computer interface (BCI) modality that combines common spatial pattern (CSP) filtration applied to EEG and EBG to classify responses to six scent stimuli in a classical oddball paradigm.

The hypothesis that odor-specific patterns of mitral cell activity during odor discrimination might be found in the corresponding spatial patterns of electroencephalogram (EEG) amplitude over a surgically accessible segment of the bulbar surface was tested in rabbits with chronically implanted electrode arrays. The spatial spectrum of the bulbar EEG was derived and compared with the spectrum predicted for the granule cell generator. Spatial filters were devised to identify, enhance, or remove the granule cell contribution to the EEG. Spatial deconvolution was applied to the filtered granule cell activity patterns to correct for distortion caused by volume conduction. The results indicated that the bulb generated odor-specific spatial patterns in rabbits trained to discriminate between two odors. The odor-specific information was not localizable to subsets of channels. This suggested that the discriminative output of the bulb involved the entire structure, even though the receptor input was delivered to limited subsets of mitral cells.

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Volitional respiratory manoeuvres such as sniffing and apnoea play a key role in the active olfactory exploration of the environment. Their impairment by neurodegenerative processes could thus impair olfactory abilities with the ensuing impact on quality of life. Functional brain imaging studies have identified brain networks engaged in sniffing and voluntary apnoea, comprising the primary motor and somatosensory cortices, the insula, the anterior cingulate cortex and the amygdala. The temporal organization and the oscillatory activities of these networks are not known. To elucidate these aspects, we recorded intracranial electroencephalograms in six patients during voluntary sniffs and short apnoeas (12 s). The preparation phase of both manoeuvres involved increased alpha and theta activity in the posterior insula, amygdala and temporal regions, with a specific preparatory activity in the parahippocampus for the short apnoeas and the hippocampus for sniff. Subsequently, it narrowed to the superior and median temporal areas, immediately after the manoeuvres. During short apnoeas, a particular dynamic was observed, consisting of a rapid decline in alpha and theta activity followed by a slow recovery and increase. Volitional respiratory manoeuvres involved in olfactory control involve corticolimbic structures in both a preparatory and executive manner. Further studies are needed to determine whether diseases altering deep brain structures can disrupt these mechanisms and if such disruption contributes to the corresponding olfactory deficits.

Rabbits were conditioned to lick (CR+) in response to one odor (CS+); another odor (CS-) served as a discriminative control (CR-). Electroencephalograms (EEGs) were recorded from arrays of 64 electrodes on the olfactory bulb in three stages, each with six sessions: in Stage I, odors A+ and B-; in Stage II, odors C+ and B-; and in Stage III, odors C+ and A-. Spatial EEG amplitude patterns were measured for multiple control (C), CS+, and CS- EEG bursts in each trial. Data were transformed via factor analysis and expressed by factor scores as spatial patterns specified by factor loadings. In discriminant analysis of the factor scores, we correctly classified the C and CS bursts on the average by 65-80% from all trials for each subject and session and by 75-90% for trials with correct CRs. The latter was confirmed with a stepwise linear discriminant analysis of the original 64-variable data. Factor patterns were relatively invariant within but changed between stages. The results implied that stable spatial patterns of bulbar activity emerged in respect to CSs under reinforcement and persisted until the stimulus-response contingencies were changed.

Oscillatory electroencephalographic bursts were measured from 64 electrodes implanted on the olfactory bulbs of rabbits. Oscillatory bursts that occurred before and during presentation of odorant conditioned stimuli (CSs) were selected in brief segments. Comparisons between the 64 traces and their spectra showed that, despite amplitude differences between channels, every burst had a common waveform over the entire array. The spectra showed 2 to 5 distinct peaks in each burst. Each trace was fitted with the sum of 5 cosines to express the burst in ten 8 X 8 matrices of amplitude and phase values at its peak frequencies. Two types of burst were identified. Those with dominant frequencies greater than 55 Hz had one narrow dominant spectral peak and reproducible spatial patterns of its amplitude within subgroups of bursts relating to control and odorant CS conditions. Those with dominant frequencies less than 55 Hz were disorderly; their spectra were broad, and their spatial patterns of amplitude did not reproduce within subgroups. A behavioral assay showed that the high- and not the low-frequency bursts contained odor-specific information.

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The dataset presented in this article contains preprocessed cleaned electroencephalography (EEG) recording from 35 participants including 13 Alzheimer's disease (AD) patients, 7 amnestic mild cognitive impairment (aMCI) patients, and 15 healthy elderly. All participants performed the same olfactory task which consisted of 120 trials of 2 s olfactory stimulation and 8 s rest (no odorant). The olfactory stimulation consisted of rose and lemon odorants. Odor trials were presented randomly with a probability of 0.75 presenting lemon and 0.25 presenting rose. The impedance of the electrodes was kept under 15 KΩ during the experiment. The data was filtered from 0.5 to 40 Hz using a bandpass filter and epoched from 1 s pre-stimulus to 2 s post-stimulus. Artifacts related to eye blinks were removed by running independent component analysis (ICA) and the remaining noisy trials were identified by eye and removed from further analysis. Mini Mental State Examination (MMSE) test scores for all participants are also provided in the dataset. Olfactory dysfunction has been shown to be associated with neurodegenerative diseases such as dementia and Alzheimer's disease. Therefore, studying the response of the olfactory system may lead to identifying early biomarkers for related brain disorders.

Electroencephalography (EEG) correlates of olfaction are of fundamental and practical interest for many reasons. In the field of neural technologies, olfactory-based brain-computer interfaces (BCIs) represent an approach that could be useful for neurorehabilitation of anosmia, dysosmia and hyposmia. While the idea of a BCI that decodes neural responses to different odors and/or enables odor-based neurofeedback is appealing, the results of previous EEG investigations into the olfactory domain are rather inconsistent, particularly when non-primary processing of olfactory signals is concerned. Here we developed an experimental paradigm where EEG recordings are conducted while a participant executes an olfaction-based instructed-delay task. We utilized an olfactory display and a sensor of respiration to deliver odors in a strictly controlled fashion. We showed that with this approach spatial and spectral EEG properties could be analyzed to assess neural processing of olfactory stimuli and their conversion into a motor response. We conclude that EEG recordings are suitable for detecting active processing of odors. As such they could be integrated in a BCI that strives to rehabilitate olfactory disabilities or uses odors for hedonistic purposes.

Today, many people suffer from neurological and psychological issues. Neurological diseases are commonly characterized by a loss of olfactory sensation. Getting proper treatment for these neurologic diseases at an early stage increases the chances of recovery. A patient often is not able to comprehend the loss of olfactory perception in the early stages of neurological diseases such as Alzheimer's disease, Parkinson's disease, etc. In order to identify olfactory sensory loss early, deep learning frameworks can be very useful. The main objective of our study is to build a deep learning-based model for classifying five different aromatic stimuli, i.e., no smell, rose water's smell, perfume's smell, cinnamon's smell, and odonil's smell using the EEG signals of subjects. In this paper, an Attention mechanism-based 1D Convolution Neural Network (CNN) architecture has been introduced. The designed model successfully classified five types of olfactory stimuli using EEG signals with 97.92% accuracy. The study is novel in two respects, i.e., i) appending attention mechanism to the designed 1D CNN model to improve its performance, and ii) analyzing changes occurring in different brain regions in response to different olfactory stimuli.

Traumatic brain injury (TBI) leads to long-term cognitive impairments, with an increased risk for neurodegenerative and psychiatric disorders. Among these various impairments, olfactory dysfunction is one of the most common symptoms in TBI patients. However, there are very few studies that show the association between olfactory dysfunction and repetitive TBI. To investigate the effects of repetitive TBI on olfactory functioning and the related pathological neuronal injuries in mice, we applied a weight-drop model of TBI and performed neuropathological examinations and electroencephalography (EEG) in olfactory-bulb-associated areas. Through neuropathological examinations, we found significant increases of amyloid precursor protein (APP) and phosphorylated Tau (p-Tau) (S202/T205) in olfactory-bulb-associated areas. Neuronal atrophy in the lateral anterior olfactory nucleus (AOL), granule layer olfactory bulb (GrO), and dorsal tenia tecta (DTT) was also found to be correlated with p-Tau levels. However, there was no difference in the total Tau levels in the olfactory-bulb-associated areas of TBI mice. Electroencephalography (EEG) of repetitive TBI mouse models showed impaired spontaneous delta oscillation, as well as altered cross-frequency coupling between delta phase and amplitudes of the fast oscillations in the resting-state olfactory bulb. Furthermore, abnormal alterations in EEG band powers were observed during the olfactory oddball paradigm test. TBI also led to impairments of the olfactory-function-associated behaviors. This study provides evidence of behavioral, neuropathological, and physiological alterations in the mouse olfactory system caused by repetitive TBI. Together, p-Tau alterations and EEG impairments may serve as important biomarkers of olfactory-track-associated dysfunctions in repetitive TBI.

BACKGROUND While decoding visual and auditory stimuli using recorded EEG signals has enjoyed significant attention in the past decades, decoding olfactory sensory input from EEG data remains a novelty. Recent interest in the brain's mechanisms of processing olfactory stimuli partly stems from the association of the olfactory system and its deficit with neurodegenerative diseases. NEW METHODS An olfactory stimulus decoder using features that represent nonlinear behavior content in the recorded EEG data has been introduced for classifying 4 olfactory stimuli in 5 healthy male subjects. RESULTS We show that by using nonlinear and chaotic features, a subject-specific classifier can be developed for identifying the odors that subjects perceive with an average accuracy of 96.71% and 88.79% in the eyes-open and eyes-closed conditions, respectively. We also employ our methodology in building cross-subject classifiers: once for identifying pleasant and unpleasant odors, and once for the classification of all four olfactory stimuli. The accuracy of our proposed methodology is 91.7% and 82.1% in the eyes-open and eyes-closed conditions, for the odor pleasantness classification. The accuracy of cross-subject classification of all odors is 64.3% and 54.8% for the eyes-open and eyes-closed conditions, respectively, which is well above chance level. COMPARISON WITH EXISTING METHODS Comparison with similar studies reveals that our proposed method outperforms other classification schemes in terms of accuracy. CONCLUSIONS The results can help researchers design more accurate classifiers for the detection of perceived odors using EEG signals. These results can contribute to gaining more insight into the brain's process of odor perception.

Olfactory perception involves complex processing distributed along several cortical and sub-cortical regions in the brain. Although several studies have shown that the power spectra of the electroencephalography (EEG) contain information that can be used to differentiate between pleasant and unpleasant stimuli, there are still no studies which investigate whether EEG can be used to differentiate between the neural responses to olfactory stimuli of different levels of pleasantness. For this purpose, in the present study, local brain information within established frequency bands (θ, α and γ) has been used to devise discriminative features in a classification approach. A comparative study of four widely used classifiers is presented and SVM gives the best performance (accuracy = 75.71%). The results reveal that is it possible to objectively discriminate using EEG spectral features between fine levels of perceived pleasantness using the SVM-based classifier within a cross-validation procedure.

Objectives The objective of the present study was to investigate the usefulness of time-frequency analysis (TFA) of olfactory-induced EEG change with a low-cost, portable olfactometer in the clinical investigation of smell function. Materials & methods A total of 78 volunteers participated. The study was composed of three parts where olfactory stimuli were presented using a custom-built olfactometer. Part I was designed to optimize the stimulus as well as the recording conditions. In part II EEG-power changes after olfactory/trigeminal stimulation were compared between healthy participants and patients with olfactory impairment. In Part III the test-retest reliability of the method was evaluated in healthy subjects. Results Part I indicated that the most effective paradigm for stimulus presentation was cued stimulus, with an interstimulus interval of 18-20s at a stimulus duration of 1000ms with each stimulus quality presented 60 times in blocks of 20 stimuli each. In Part II we found that central processing of olfactory stimuli analyzed by TFA differed significantly between healthy controls and patients even when controlling for age. It was possible to reliably distinguish patients with olfactory impairment from healthy individuals at a high degree of accuracy (healthy controls vs anosmic patients: sensitivity 75%; specificity 89%). In addition we could show a good test-retest reliability of TFA of chemosensory induced EEG-power changes in Part III. Conclusions Central processing of olfactory stimuli analyzed by TFA reliably distinguishes patients with olfactory impairment from healthy individuals at a high degree of accuracy. Importantly this can be achieved with a simple olfactometer.

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In psychological experiments, behavioral speed varies across trials, and this variation is often associated with corresponding fluctuations in cortical activity. Little is known about such cortical variations in semantic priming tasks where target words are matched with preceding sensory object cues. Here, two visually presented target words (“pear” and “lilac”) were repeatedly cued by corresponding odors or pictures, and the participants were to indicate matching or nonmatching combinations. Data were split in behaviorally “fast” versus “slow” trials. We hypothesized that slow trials would be associated with higher prestimulus alpha activity and reduced ERP amplitudes, and that response-time differences between odor-cued and picture-cued trials would be especially large in slow behavioral trials. Results confirmed that slow trials showed increased alpha-band activity prior to word target onset, as well as amplitude decreases in the sensory P1 and semantic N400 components. However, no interactions between cue-modality and processing speed were observed. Instead, odor-cue integration responses were uniquely delayed on incongruent trials, a novel behavioral effect that was not observed in EEG measures. The results show that semantic integration speed is reflected in cortical activity before and during stimulus processing. Behavioral interactions with cue modality did not correspond to observed cortical activity changes, perhaps because olfactory circuits are not readily observed in scalp-recorded EEG. We conclude that combining behavioral speed variability and cortical EEG measures is useful in understanding the fluctuating nature of cognitive processing sequences.

In this study, we explore the feasibility of single-trial predictions of odor registration in the brain using olfactory bio-signals. We focus on two main aspects: input data modality and the processing model. For the first time, we assess the predictability of odor registration from novel electrobulbogram (EBG) recordings, both in sensor and source space, and compare these with commonly used electroencephalogram (EEG) signals. Despite having fewer data channels, EBG shows comparable performance to EEG. We also examine whether breathing patterns contain relevant information for this task. By comparing a logistic regression classifier, which requires hand-crafted features, with an end-to-end convolutional deep neural network, we find that end-to-end approaches can be as effective as classic methods. However, due to the high dimensionality of the data, the current dataset is insufficient for either classifier to robustly differentiate odor and non-odor trials. Finally, we identify key challenges in olfactory BCIs and suggest future directions for improving odor detection systems.

Throughout history, various odors have been harnessed to invigorate or relax the mind. The mechanisms underlying odors' diverse arousal effects remain poorly understood. We conducted five experiments (184 participants) to investigate this issue, using pupillometry, electroencephalography, and the attentional blink paradigm, which exemplifies the limit in attentional capacity. Results demonstrated that exposure to citral, compared to vanillin, enlarged pupil size, reduced resting-state alpha oscillations and alpha network efficiency, augmented beta-gamma oscillations, and enhanced the coordination between parietal alpha and frontal beta-gamma activities. In parallel, it attenuated the attentional blink effect. These effects were observed despite citral and vanillin being comparable in perceived odor intensity, pleasantness, and nasal pungency, and were unlikely driven by semantic biases. Our findings reveal that odors differentially alter the small-worldness of brain network architecture, and thereby brain state and arousal. Furthermore, they establish arousal as a unique dimension in olfactory space, distinct from intensity and pleasantness.

Brain-Computer Interfaces (BCIs) are devices designed for establishing communication between the central nervous system and a computer. The communication can occur through different sensory modalities, and most commonly visual and auditory modalities are used. Here we propose that BCIs can be expanded by the incorporation of olfaction and discuss the potential applications of such olfactory BCIs. To substantiate this idea, we present results from two olfactory tasks: one that required attentive perception of odors without any overt report, and the second one where participants discriminated consecutively presented odors. In these experiments, EEG recordings were conducted in healthy participants while they performed the tasks guided by computer-generated verbal instructions. We emphasize the importance of relating EEG modulations to the breath cycle to improve the performance of an olfactory-based BCI. Furthermore, theta-activity could be used for olfactory-BCI decoding. In our experiments, we observed modulations of theta activity over the frontal EEG leads approximately 2 s after the inhalation of an odor. Overall, frontal theta rhythms and other types of EEG activity could be incorporated in the olfactory-based BCIs which utilize odors either as inputs or outputs. These BCIs could improve olfactory training required for conditions like anosmia and hyposmia, and mild cognitive impairment.

Our study provides evidence that Mild Cognitive Impairment (MCI) is associated with olfactory dysfunction on both conscious and non-conscious levels. MCI patients and age-matched controls underwent a face processing task during which sympathy decisions had to be made via button presses. Incidentally, some of the faces were associated with a simultaneously presented odour. Although attention was paid to faces, brain activities were analysed with respect to odour versus no-odour conditions. Behavioural differences were found related to overall face recognition performance, but these were not statistically significant. However, odour-related neurophysiology differed between both groups. Normal controls demonstrated brain activity differences between odour and no-odour conditions that resemble difference activity patterns in healthy young participants as described in a previous magnetoencephalography (MEG) study [1]. They showed odour-related activity patterns between about 160 ms and 320 ms after stimulus onset and between about 640 ms and 720 ms. On the other hand, the patient group did not show any such difference activities. Based on previous research we interpret the early odour-related brain activity pattern in controls as being associated with subliminal olfaction and the later activity pattern with conscious olfaction. None of these were found in MCI patients, although it has to be emphasised that our sample size was rather small. We confirm previous findings about olfactory related dysfunction in patients with MCI and conclude from our findings that even subliminal odour-related information processing is impaired.

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Olfactory perception can be studied in deep brain regions at high spatial resolutions with functional magnetic resonance imaging (fMRI), but this is complex and expensive. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are limited to cortical responses and lower spatial resolutions but are easier and cheaper to use. Unlike EEG, available fNIRS studies on olfaction are few, limited in scope, and contradictory. Here, we investigated fNIRS efficacy in assessing the hedonic valence of pleasant and unpleasant odors, using ten channels on each hemisphere, covering the orbitofrontal cortex and adjacent areas involved in olfactory and cognitive tasks. Measurements on 22 subjects (11 males and 11 females) showed statistically significant higher increases in oxygenated hemoglobin concentration for the unpleasant odor, compared to the pleasant one (mean difference = 1.025 × 10−1 μM). No difference in activation was found between the hemispheres. Conversely, differences were observed between the sexes: for the first time, we show that higher activations for the unpleasant odor relative to the pleasant one are detectable by fNIRS in females (mean difference = 1.704 × 10−1 μM), but not in an equal-sized and equal-age group of males. Moreover, females had greater activations relative to males for the unpleasant odor (mean difference = 1.285 × 10−1 μM). Therefore, fNIRS can capture peculiarities of olfactory activations, highlighting differences between odors with opposite valence and between sexes. This evidence positions fNIRS next to EEG as suitable technologies for cortical investigations of olfactory perception, providing complementary information (late and early response components, respectively), with lower costs and easier operation (albeit at lower resolutions) compared to fMRI.

Olfaction, or the sense of smell, presents a promising avenue for enhancing brain-computer interface (BCI) usability and enabling passive cognitive state monitoring. In reactive BCI paradigms, odor cues can be associated with specific commands, facilitating more intuitive interaction. Furthermore, passive BCI applications can leverage olfactory stimuli to monitor cognitive processes. Despite this potential, challenges remain, notably the requirement for precise odor delivery mechanisms and robust algorithms capable of detecting and interpreting associated brain activity. This work proposes a novel approach, combining electroencephalography (EEG) and electrobulbogram (EBG) within an olfactory modality oddball paradigm, for predicting user awareness levels. A pilot study is presented, demonstrating improved user awareness classification performance with a newly developed multiclass, lightweight convolutional neural network (CNN) for this passive olfactory BCI modality, surpassing previously reported results.Clinical relevance— This research demonstrates the feasibility of inferring user awareness levels from concurrently acquired electroencephalographic (EEG) and electrobulbogram (EBG) neurophysiological data.

The dominant aroma smoke of cigars is key to their quality. This study employed gas chromatography‐olfactometry‐mass spectrometry (GC‐O‐MS) combined with odour activity value (OAV) analysis and sensory evaluation to identify critical volatile aroma compounds in mainstream smoke from five cigar brands. Additionally, electroencephalography (EEG) was utilised to investigate corresponding brain electrophysiological responses. A total of 121 volatile compounds were identified for the five cigars, with QJ (132 Miracle) having the highest content, followed by SX (Animals of the Chinese Zodiac), JD (Range 3 Classic), SS (Shengshi No. 5) and HS (Red 132). There were significant differences in aroma profiles and only 10 shared volatile compounds. A total of 48 aroma active compounds were detected by olfaction, of which 26 with OAV > 1 were key aroma active components and correlated with sensory attributes. 4‐methylguaiacol showed a significant positive correlation with caramel and honey flavours. 2‐Ethylhexanol and lauryl alcohol exhibited significant positive correlations with floral and creamy flavours. Brain activity peaked during the middle smoking phase, particularly in the occipital lobe and δ energies were generally active across brands, while γ energies displayed the most pronounced differences, with heightened activity during the middle phase. This study elucidates the key aroma components of cigar mainstream smoke and provides a scientific foundation for exploring neural perception mechanisms of cigar flavour, offering valuable data for enhancing domestic cigar flavour quality.

This perspective considers the novel concept of olfactory neurofeedback (O-NFB) within the framework of brain-computer interfaces (BCIs), where olfactory stimuli are integrated in various BCI control loops. In particular, electroencephalography (EEG)-based O-NFB systems are capable of incorporating different components of complex olfactory processing – from simple discrimination tasks to using olfactory stimuli for rehabilitation of neurological disorders. In our own work, EEG theta and alpha rhythms were probed as control variables for O-NFB. Additionaly, we developed an olfactory-based instructed-delay task. We suggest that the unique functions of olfaction offer numerous medical and consumer applications where O-NFB is combined with sensory inputs of other modalities within a BCI framework to engage brain plasticity. We discuss the ways O-NFB could be implemented, including the integration of different types of olfactory displays in the experiment set-up and EEG features to be utilized. We emphasize the importance of synchronizing O-NFB with respiratory rhythms, which are known to influence EEG patterns and cognitive processing. Overall, we expect that O-NFB systems will contribute to both practical applications in the clinical world and the basic neuroscience of olfaction.

Brain activity may manifest itself as oscillations which are repetitive rhythms of neuronal firing. These local field potentials can be measured via intracranial electroencephalography (iEEG). This review focuses on iEEG used to map human brain structures involved in olfaction. After presenting the methodology of the review, a summary of the brain structures involved in olfaction is given, followed by a review of the literature on human olfactory oscillations in different contexts. A single case is provided as an illustration of the olfactory oscillations. Overall, the timing and sequence of oscillations found in the different structures of the olfactory system seem to play an important role for olfactory perception.

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The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard.

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Background It is not straightforward to objectively evaluate the olfactory dysfunction that occurs following forensic incidents. The olfactory event-related potentials method, based on electrophysiological records, may provide objective data in the evaluation of posttraumatic anosmia cases from the medicolegal perspective. This study, where a quantitative evaluation of the cases with the complaints of olfactory sensation disorder was performed using the olfactory event-related potentials test, aims to identify the factors that should be considered in the evaluation of olfactory dysfunction from the medicolegal perspective. Results This study first evaluated the complaints of 98 patients admitted because of posttraumatic impaired smell and then administered electrophysiological odor tests on the patients. Because of this, the relationship between the EEG responses of the cases and the olfactory disorder was examined. Of the 98 cases that participated in the study, 68 (69.4%) were male and 30 (30.6%) were female. Of all cases, 53 (54.1%) had complaints of not being able to smell at all, 14 (14.3%) had complaints of reduced smell, whereas, in addition to the existing complaints of olfactory dysfunction, 44 (44.9%) of them had complaints of taste perception and 18 (18.3%) reported having vision disorders. 21 of 37 cases who reported being unable to smell during the test turned out to be anosmic. Furthermore, 16 cases stated that, though having had a response in the odor test, they had no sense of smell following the test. Conclusions Although it seems possible to prove that there is a relationship between the olfactory event-related potential test and the diagnosis of anosmia, there is still ongoing research on its use in clinical practice. Performing both subjective and electrophysiological tests together to detect olfactory dysfunctions that occur after a forensic incident enable provide more reliable results in diagnosis.

Odor context can affect the recognition of facial expressions. However, there is no evidence to date that odor can regulate the processing of emotional words conveyed by visual words. An emotional word recognition task was combined with event-related potential technology. Briefly, 49 adults were randomly divided into three odor contexts (pleasant odor, unpleasant odor, and no odor) to judge the valence of emotional words (positive, negative, and neutral). Both behavioral and Electroencephalography (EEG) data were collected. Both the pleasant odor and unpleasant odor contexts shortened the response time of the subjects to emotional words. In addition, negative words induced greater amplitudes of early posterior negativity (EPN) and late positive potential (LPP) than the positive and neutral words. However, the neutral words induced a larger N400 amplitude than the positive and negative words. More importantly, the processing of emotional words was found to be modulated by external odor contexts. For example, during the earlier (P2) processing stages, pleasant and unpleasant odor contexts induced greater P2 amplitudes than the no odor context. In the unpleasant odor context, negative words with the same odor valence induced greater P2 amplitudes than the positive words. During the later (N400) stages, various regions of the brain regions exhibited different results. For example, in the left and right frontal areas of the brain, exposure to positive words in a pleasant odor context resulted in a smaller N400 amplitude than exposure to neutral words in the same context. Meanwhile, in the left and right central regions, emotional words with the same valence as pleasant or unpleasant odor contexts elicited the minimum N400 amplitude. Individuals are very sensitive to emotional information. With deeper processing, different cognitive processes are reflected and they can be modulated by external odors. In the early and late stages of word processing, both pleasant and unpleasant odor contexts exhibited an undifferentiated dominance effect and could specifically modulate affectively congruent words.

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Atypical sensory reactivities are pervasive among people with autism spectrum disorder (ASD). With respect to olfaction, most previous studies have used psychophysical or questionnaire-based methodologies; thus, the neural basis of olfactory processing in ASD remains unclear. This study aimed to determine the stages of olfactory processing that are altered in ASD. Fourteen young adults with high-functioning ASD (mean age, 21 years; 3 females) were compared with 19 age-matched typically developing (TD) controls (mean age, 21 years; 4 females). Olfactory event-related potentials (OERPs) for 2-phenylethyl alcohol-a rose-like odor-were measured with 64 scalp electrodes while participants performed a simple odor detection task. Significant group differences in OERPs were found in 3 time windows 542 ms after the stimulus onset. The cortical source activities in these time windows, estimated using standardized low-resolution brain electromagnetic tomography, were significantly higher in ASD than in TD in and around the posterior cingulate cortex, which is known to play a crucial role in modality-general cognitive processing. Supplemental Bayesian analysis provided substantial evidence for an alteration in the later stages of olfactory processing, whereas conclusive evidence was not provided for the earlier stages. These results suggest that olfactory processing in ASD is altered at least at the later, modality-general processing stage.

Odor habituation is a phenomenon that after repeated exposure to an odor, is characterized by decreased responses to it. The central nervous system is involved in odor habituation. To study odor habituation in humans, measurement of event-related potentials (ERPs) has been widely used in the olfactory system and other sensory systems, because of their high temporal resolution. Most previous odor habituation studies have measured the olfactory ERPs of (200–800) ms. However, several studies have shown that the odor signal is processed in the central nervous system earlier than at 200 ms. For these reasons, we studied whether when odors were habituated, olfactory ERP within 200 ms of odors could change. To this end, we performed an odor habituation behavior test and electroencephalogram experiments. In the behavior test, under habituation conditions, odor intensity was significantly decreased. We found significant differences in the negative and positive potentials within 200 ms across the conditions, which correlated significantly with the results of the behavior test. We also observed that ERP latency depended on the conditions. Our study suggests that odor habituation can involve the olfactory ERP of odors within 200 ms in the brain.

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Behavioral and electrophysiological testing of olfactory function was performed in 33 normal human male and female subjects, 18-83 years of age. Acuity for odor identification and odor detection was verified by standard psychophysical tests. For evoked potential testing, a constant flow olfactometer provided odorant stimuli (amyl acetate) or air control stimuli that were presented to the right nostril by a nasal cannula at a flow rate of 5 l/min, duration of 40 msec and random interstimulus intervals of 6-30 sec. The behavioral tests revealed no significant difference between males and females, whereas increasing age was associated with a decline in performance on the odor identification test. No reproducible evoked potentials were recorded in response to the air control stimulus. Potentials to the odorant stimulus consisted of 4 components named P1, N1, P2 and N2. A significant correlation was found between P2 latency and odor identification test scores, suggesting a relationship between the generation of the P2 component and olfactory processing. P2 peak latency increased significantly with age at 2.5 msec/year. An age-related decline in N1-P2 interpeak amplitude was seen in male subjects. Topographic differences were seen in the P2 peak amplitude and the N1-P2 and P2-N2 interpeak amplitudes such that their amplitudes were greatest at Cz and Pz. On average, N1-P2 interpeak amplitudes were larger in the female subjects than in the male subjects, possibly revealing a hormonal influence on the olfactory event-related potential.

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NMDA receptor antagonists, used to model psychotic-like states and treat depression, enhance the power of high-frequency oscillations (HFO) in many mammalian brain regions. In rodents, the olfactory bulb (OB) is a particularly important site for generating this rhythm. OB projection neurons express D1 and D2 receptors (D1R and D2R) which interact with NMDA receptors. The aim of this study was to explore the effect of dopamine (DA) signalling in the OB on MK801-enhanced HFO. Local field potentials from the OB and locomotor activity were recorded in adult male freely moving rats. MK801 was injected systemically or infused locally to the OB. The effects of D1R and D2R agonists (SKF38393, quinpirole) and antagonists (SCH23390, eticlopride), administered systemically or locally to the OB, were examined on MK801-enhanced HFO. Effects of the antipsychotics risperidone and aripiprazole were also examined. Local infusion of MK801 enhanced HFO power in the OB to levels similar to those observed after systemic injection. Neither systemic nor local blockade of D1R or D2R affected the MK801-enhanced HFO, despite reductions in hyperlocomotion. However, direct (systemic and local) D2R, but not D1R, stimulation caused a short-lasting reduction of MK801-enhanced HFO power and longer lasting reduction in frequency. Risperidone, but not aripiprazole, reduced MK801-enhanced HFO frequency. These results suggest that NMDA receptor antagonist-enhanced HFO in the OB is generated predominantly independently of DA influence, however exogenous stimulation of D2R can modulate this rhythm. A second, but not third generation antipsychotic reduced HFO frequency.

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A central baroreceptor monitors arterial pressure to modulate brain activity In 1942, the electrophysiologist E. D. Adrian published recordings from the hedgehog olfactory bulb (OB) indicating three basic classes of electrical oscillation: a respiration-nasal-airflow–related oscillation (RRO), a sensory-odor–induced oscillation, and an oscillation considered intrinsic to the local neural network (1). These oscillations are important because by moving the neuronal resting membrane potential toward and away from the spike threshold, they can synchronize spike activity in local and remote neural networks. All three classes of oscillation are dependent on synaptic transmission; have been recorded in other brain regions, most notably in the human cerebral cortex and hippocampus; and are considered fundamental to how the brain normally processes information (2). On page 494 of this issue, Jammal Salameh et al. (3) report a fourth class of oscillation—a heartbeat- related oscillation (HRO) evoked by arterial pressure pulsations and transduced by central baroreceptors.

Real-time tracking of vigilance states related to both sleep or anaesthesia has been a goal for over a century. However, sleep scoring cannot currently be performed with brain signals alone, despite the deep neuromodulatory transformations that accompany sleep state changes. Therefore, at heart, the operational distinction between sleep and wake is that of immobility and movement, despite numerous situations in which this one-to-one mapping fails. Here we demonstrate, using local field potential (LFP) recordings in freely moving mice, that gamma (50–70 Hz) power in the olfactory bulb (OB) allows for clear classification of sleep and wake, thus providing a brain-based criterion to distinguish these two vigilance states without relying on motor activity. Coupled with hippocampal theta activity, it allows the elaboration of a sleep scoring algorithm that relies on brain activity alone. This method reaches over 90% homology with classical methods based on muscular activity (electromyography [EMG]) and video tracking. Moreover, contrary to EMG, OB gamma power allows correct discrimination between sleep and immobility in ambiguous situations such as fear-related freezing. We use the instantaneous power of hippocampal theta oscillation and OB gamma oscillation to construct a 2D phase space that is highly robust throughout time, across individual mice and mouse strains, and under classical drug treatment. Dynamic analysis of trajectories within this space yields a novel characterisation of sleep/wake transitions: whereas waking up is a fast and direct transition that can be modelled by a ballistic trajectory, falling asleep is best described as a stochastic and gradual state change. Finally, we demonstrate that OB oscillations also allow us to track other vigilance states. Non-REM (NREM) and rapid eye movement (REM) sleep can be distinguished with high accuracy based on beta (10–15 Hz) power. More importantly, we show that depth of anaesthesia can be tracked in real time using OB gamma power. Indeed, the gamma power predicts and anticipates the motor response to stimulation both in the steady state under constant anaesthetic and dynamically during the recovery period. Altogether, this methodology opens the avenue for multi-timescale characterisation of brain states and provides an unprecedented window onto levels of vigilance.

Social communication is crucial for the survival of many species. In most vertebrates, a dedicated chemosensory system, the vomeronasal system (VNS), evolved to process ethologically relevant chemosensory cues. The first central processing stage of the VNS is the accessory olfactory bulb (AOB), which sends information to downstream brain regions via AOB mitral cells (AMCs). Recent studies provided important insights about the functional properties of AMCs, but little is known about the principles that govern their coordinated activity. Here, we recorded local field potentials (LFPs) and single-unit activity in the AOB of adult male and female mice during presentation of natural stimuli. Our recordings reveal prominent LFP theta-band oscillatory episodes with a characteristic spatial pattern across the AOB. Throughout an experiment, the AOB network shows varying degrees of similarity to this pattern, in a manner that depends on the sensory stimulus. Analysis of LFP signal polarity and single-unit activity indicates that oscillatory episodes are generated locally within the AOB, likely representing a reciprocal interaction between AMCs and granule cells. Notably, spike times of many AMCs are constrained to the negative LFP oscillation phase in a manner that can drastically affect integration by downstream processing stages. Based on these observations, we propose that LFP oscillations may gate, bind, and organize outgoing signals from individual AOB neurons to downstream processing stages. Our findings suggest that, as in other neuronal systems and brain regions, population-level oscillations play a key role in organizing and enhancing transmission of socially relevant chemosensory information. SIGNIFICANCE STATEMENT The accessory olfactory bulb (AOB) is the first central stage of the vomeronasal system, a chemosensory system dedicated to processing cues from other organisms. Information from the AOB is conveyed to other brain regions via activity of its principal neurons, AOB mitral cells (AMCs). Here, we show that socially relevant sensory stimulation of the mouse vomeronasal system leads not only to changes in AMC activity, but also to distinct theta-band (∼5 Hz) oscillatory episodes in the local field potential. Notably AMCs favor the negative phase of these oscillatory events. Our findings suggest a novel mechanism for the temporal coordination of distributed patterns of neuronal activity, which can serve to efficiently activate downstream processing stages.

In animal models, oscillations of local field potentials are entrained by nasal respiration at the frequency of breathing cycle in olfactory brain regions, such as the olfactory bulb and piriform cortex, as well as in the other brain regions. Studies in humans also confirmed these respiration-entrained oscillations in several brain regions using intracranial electroencephalogram (EEG). Here we extend these findings by analyzing coherence between cortical activity and respiration using high-density scalp EEG in twenty-seven healthy human subjects. Results indicated the occurrence of significant coherence between scalp EEG and respiration signals, although the number and locations of electrodes showing significant coherence were different among subjects. These findings suggest that scalp EEG can detect respiration-entrained oscillations. It remained to be determined whether these oscillations are volume conducted from the olfactory brain regions or reflect the local cortical activity.

Gamma oscillations are believed to underlie cognitive processes by shaping the formation of transient neuronal partnerships on a millisecond scale. These oscillations are coupled to the phase of breathing cycles in several brain areas, possibly reflecting local computations driven by sensory inputs sampled at each breath. Here, we investigated the mechanisms and functions of gamma oscillations in the piriform (olfactory) cortex of awake mice to understand their dependence on breathing and how they relate to local spiking activity. Mechanistically, we find that respiration drives gamma oscillations in the piriform cortex, which correlate with local feedback inhibition and result from recurrent connections between local excitatory and inhibitory neuronal populations. Moreover, respiration-driven gamma oscillations are triggered by the activation of mitral/tufted cells in the olfactory bulb and are abolished during ketamine/xylazine anesthesia. Functionally, we demonstrate that they locally segregate neuronal assemblies through a winner-take-all computation leading to sparse odor coding during each breathing cycle. Our results shed new light on the mechanisms of gamma oscillations, bridging computation, cognition and physiology.

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Cognitive functions such as working memory require integrated activity among different brain regions. Notably, entorhinal cortex (EC) activity is associated with the successful working memory task. Olfactory bulb (OB) oscillations are known as rhythms that modulate rhythmic activity in widespread brain regions during cognitive tasks. Since the OB is structurally connected to the EC, we hypothesized that OB could modulate EC activity during working memory performance. Herein, we explored OB–EC functional connectivity during spatial working memory performance by simultaneous recording local field potentials when rats performed a Y-maze task. Our results showed that the coherence of delta, theta, and gamma-band oscillations between OB and EC was increased during correct trials compared to wrong trials. Cross-frequency coupling analyses revealed that the modulatory effect of OBs low-frequency phase on EC gamma power and phase was enhanced when animals correctly performed working memory task. The influx of information from OB to EC was also increased at delta and gamma bands within correct trials. These findings indicated that the modulatory influence of OB rhythms on EC oscillations might be necessary for successful working memory performance.

Neural synchrony generates fast network oscillations throughout the brain, including the main olfactory bulb (MOB), the first processing station of the olfactory system. Identifying the mechanisms synchronizing neurons in the MOB will be key to understanding how network oscillations support the coding of a high-dimensional sensory space. Here, using paired recordings and optogenetic activation of glomerular sensory inputs in MOB slices, we uncovered profound differences in principal mitral cell (MC) vs. tufted cell (TC) spike-time synchrony: TCs robustly synchronized across fast- and slow-gamma frequencies, while MC synchrony was weaker and concentrated in slow-gamma frequencies. Synchrony among both cell types was enhanced by shared glomerular input but was independent of intraglomerular lateral excitation. Cell-type differences in synchrony could also not be traced to any difference in the synchronization of synaptic inhibition. Instead, greater TC than MC synchrony paralleled the more periodic firing among resonant TCs than MCs and emerged in patterns consistent with densely synchronous network oscillations. Collectively, our results thus reveal a mechanism for parallel processing of sensory information in the MOB via differential TC vs. MC synchrony, and further contrast mechanisms driving fast network oscillations in the MOB from those driving the sparse synchronization of irregularly-firing principal cells throughout cortex.

Local field potential oscillations reflect temporally coordinated neuronal ensembles—coupling distant brain regions, gating processing windows, and providing a reference for spike timing-based codes. In phase amplitude coupling (PAC), the amplitude of the envelope of a faster oscillation is larger within a phase window of a slower carrier wave. Here, we characterized PAC, and the related theta phase-referenced high gamma and beta power (PRP), in the olfactory bulb of mice learning to discriminate odorants. PAC changes throughout learning, and odorant-elicited changes in PRP increase for rewarded and decrease for unrewarded odorants. Odorant identity can be decoded from peak PRP in animals proficient in odorant discrimination, but not in naïve mice. As the animal learns to discriminate the odorants the dimensionality of PRP decreases. Therefore, modulation of phase-referenced chunking of information in the course of learning plays a role in early sensory processing in the high input dimensionality sense of olfaction. SIGNIFICANCE Early processing of olfactory information takes place in circuits undergoing slow frequency theta oscillations generated by the interplay of olfactory input modulated by sniffing and centrifugal feedback from downstream brain areas. Studies in the hippocampus and cortex suggest that different information “chunks” are conveyed at different phases of the theta oscillation. Here we show that in the olfactory bulb, the first processing station in the olfactory system, the amplitude of high frequency gamma oscillations encodes for information on odorant identity more accurately when it is observed at the peak phase of the theta oscillation. Furthermore, encoding of information by the theta phase-referenced gamma oscillations becomes more accurate as the animal learns to differentiate two odorants.

The aging process eventually cause a breakdown in critical synaptic plasticity and connectivity leading to deficits in memory function. The olfactory bulb (OB) and the hippocampus, both regions of the brain considered critical for the processing of odors and spatial memory, are commonly affected by aging. Using an aged wild-type C57B/6 mouse model, we sought to define the effects of aging on hippocampal plasticity and the integrity of cortical circuits. Specifically, we measured the long-term potentiation of high-frequency stimulation (HFS-LTP) at the Shaffer-Collateral CA1 pyramidal synapses. Next, local field potential (LFP) spectra, phase-amplitude theta-gamma coupling (PAC), and connectivity through coherence were assessed in the olfactory bulb, frontal and entorhinal cortices, CA1, and amygdala circuits. The OB of aged mice showed a significant increase in the number of histone H2AX-positive neurons, a marker of DNA damage. While the input-output relationship measure of basal synaptic activity was found not to differ between young and aged mice, a pronounced decline in the slope of field excitatory postsynaptic potential (fEPSP) and the population spike amplitude (PSA) were found in aged mice. Furthermore, aging was accompanied by deficits in gamma network oscillations, a shift to slow oscillations, reduced coherence and theta-gamma PAC in the OB circuit. Thus, while the basal synaptic activity was unaltered in older mice, impairment in hippocampal synaptic transmission was observed only in response to HFS. However, age-dependent alterations in neural network appeared spontaneously in the OB circuit, suggesting the neurophysiological basis of synaptic deficits underlying olfactory processing. Taken together, the results highlight the sensitivity and therefore potential use of LFP quantitative network oscillations and connectivity at the OB level as objective electrophysiological markers that will help reveal specific dysfunctional circuits in aging-related neurodegeneration studies.

There seems to be a correlation between soluble amyloid beta protein (Aβ) accumulation in the main olfactory bulb (OB) and smell deterioration in both Alzheimer's disease (AD) patients and animal models. Moreover, this loss of smell appears to be related to alterations in neural network activity in several olfactory-related circuits, including the OB, as has been observed in anesthetized animals and brain slices. It is possible that there is a correlation between these two pathological phenomena, but a direct and simultaneous evaluation of the acute and direct effect of Aβ on OB activity while animals are actually smelling has not been performed. Thus, here, we tested the effects of acute intrabulbar injection of Aβ at a low dose (200 pmol) on the OB local field potential before and during the presence of a hidden piece of smelly food. Our results show that Aβ decreases the power of OB network activity while impairing the animal's ability to reach the hidden food. We found a strong relationship between the power of the OB oscillations and the correlation between OBs and the olfactory detection test scores. These findings provide a direct link between Aβ-induced OB network dysfunction and smell loss in rodents, which could be extrapolated to AD patients.

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Abstract Background Subanesthetic doses of ketamine are used to model psychoses and for the treatment of depression. The mechanism though which ketamine achieves its effects are not fully understood. Ketamine is known to significantly increase the power of high-frequency oscillations (HFO, 130-180 Hz) in many rodent brain regions. Our previous findings have shown that the olfactory bulb is an important generator of ketamine-enhanced HFO, and may orchestrate this activity in other areas. Aims & Objectives The nasal epithelia houses olfactory sensory neurons whose axons directly innervate the olfactory bulb. Here we tested that the enhancement of HFO, after ketamine, is dependent on input from the nasal epithelia. Method Local field potentials were recorded from the olfactory bulb, ventral striatum and prefrontal cortex of freely moving rats. Damage to the nasal epithelia was induced by intranasal gadolinium infusion. Rats received intraperitoneal ketamine 25 mg/kg injection before and at different time points up to two weeks after nasal infusion. Results Gadolinium nasal infusion, but not saline, was associated with a reduction in respiration rhythm recorded in the olfactory bulb. Ketamine-enhanced HFO power were significantly reduced in all regions we recorded. This effect lasted at least 7 days and was associated with a gradual recovery thereafter. Ketamine-enhanced locomotion was not affected by intranasal gadolinium. Discussion And Conclusion These findings show that ketamine-enhanced HFO in the olfactory bulb, and other brain regions, are dependent on input from the nasal epithelium. The authors wish to thank NCN (grant 2021/41/B/NZ4/03882) for funding this project.

Anxiety is among the most fundamental mammalian behaviors. Despite the physiological and pathological importance, its underlying neural mechanisms remain poorly understood. Here, we recorded the activity of olfactory bulb (OB) and medial prefrontal cortex (mPFC) of rats, which are critical structures to brain’s emotional processing network, while exploring different anxiogenic environments. Our results show that presence in anxiogenic contexts increases the OB and mPFC regional theta activities. Also, these local activity changes are associated with enhanced OB-mPFC theta power- and phase-based functional connectivity as well as OB-to-mPFC information transfer. Interestingly, these effects are more prominent in the unsafe zones of the anxiogenic environments, compared to safer zones. This consistent trend of changes in diverse behavioral environments as well as local and long-range neural activity features suggest that the dynamics of OB-mPFC circuit theta oscillations might underlie different types of anxiety behaviors, with possible implications for anxiety disorders.

Introduction: The positive effects of exercise on spatial memory and learning have been demonstrated in research. The olfactory sensory neurons (OSNs) respond to mechanical stimulation induced by nasal airflow which is associated with airflow intensity. Accordingly, nasal breathing can modulate brain oscillations in nonolfactory areas, and respiration-entrained oscillations aid the improvement of cognitive abilities. Given that aerobic exercise increases the rate of respiration and intensity of nasal airflow, this study evaluates the role of OSNs in mediating the effects of aerobic exercise on memory. Methods: We examined spatial memory following exercise in animal models of olfactory sensory neuron impairment (methimazole injection 300 mg/kg/week). Results: Destroying OSNs significantly reduces olfactory bulb (OB) activity at delta and theta frequency bands as well as its coupling to respiration. More importantly, it abolished the positive effect of exercise on spatial memory (P<0.05). Conclusion: The OB activity is one of the probable mechanisms for improving spatial memory following exercise.

In mammals, NMDA receptor antagonists have been linked to the emergence of high-frequency oscillations (HFO, 130–180 Hz) in cortical and subcortical brain regions. The extent to which transmission of this rhythm is dependent on feedforward (bottom-up) or feedback (top-down) mechanisms is unclear. Previously, we have shown that the olfactory bulb (OB), known to orchestrate oscillations in many brain regions, is an important node in the NMDA receptor-dependent HFO network. Since the piriform cortex (PC) receives major input from the OB, and can modulate OB activity via feedback projections, it represents an ideal site to investigate transmission modalities. Here we show, using silicon probes, that NMDA receptor antagonist HFO are present in the PC associated with current dipoles, although of lower power than the OB. Granger causality and peak-lag analyses implicated the OB as the driver of HFO in the PC. Consistent with this, reversible inhibition of the OB resulted in a reduction of HFO power both locally and in the PC. In contrast, inhibition of the PC had minimal impact on OB activity. Collectively, these findings point to bottom-up mechanisms in mediating the transmission of NMDA receptor antagonist-HFO, at least in olfactory circuits.

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Neural oscillations synchronize the activity of brain regions during cognitive functions, such as spatial working memory. Olfactory bulb (OB) oscillations are ubiquitous rhythms that can modulate neocortical and limbic regions. However, the functional connectivity between the OB and areas contributing to spatial working memory, such as the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC), is less understood. Hence, we investigated functional interaction between OB and the vHPC–mPFC circuit during the spatial working memory performance in rats. To this end, we analyzed the simultaneously recorded local field potentials from OB, vHPC, and mPFC when rats explored the Y-maze and compared the brain activities of correct trials vs. wrong trials. We found that coupling between the vHPC and mPFC was augmented during correct trials. The enhanced coherence of OB activity with the vHPC–mPFC circuit at delta (< 4 Hz) and gamma (50–80 Hz) ranges were observed during correct trials. The cross-frequency analysis revealed that the OB delta phase increased the mPFC gamma power within corrected trials, indicating a modulatory role of OB oscillations on mPFC activity during correct trials. Moreover, the correlation between OB oscillations and the vHPC–mPFC circuit was increased at the delta range during correct trials, exhibiting enhanced synchronized activity of these regions during the cognitive task. We demonstrated a functional engagement of OB connectivity with the vHPC–mPFC circuit during spatial working memory task performance.

Visual Abstract The medial forebrain bundle (MFB) is a white matter pathway that traverses through mesolimbic structures and includes dopaminergic neural fibers ascending from the ventral tegmental area (VTA). Since dopaminergic signals represent hedonic responses, electrical stimulation of the MFB in animals has been used as a neural reward for operant and spatial tasks. MFB stimulation strongly motivates animals to rapidly learn to perform a variety of behavioral tasks to obtain a reward. Although the MFB is known to connect various brain regions and MFB stimulation dynamically modulates animal behavior, how central and peripheral functions are affected by MFB stimulation per se is poorly understood. To address this question, we simultaneously recorded electrocorticograms (ECoGs) in the primary motor cortex (M1), primary somatosensory cortex (S1), and olfactory bulb (OB) of behaving rats while electrically stimulating the MFB. We found that MFB stimulation increased the locomotor activity of rats. Spectral analysis confirmed that immediately after MFB stimulation, sniffing activity was facilitated and the power of gamma oscillations in the M1 was increased. After sniffing activity and motor cortical gamma oscillations were facilitated, animals started to move. These results provide insight into the importance of sniffing activity and cortical gamma oscillations for motor execution and learning facilitated by MFB stimulation.

Neuronal oscillations route external and internal information across brain regions. In the olfactory system, the two central nodes—the olfactory bulb (OB) and the piriform cortex (PC)—communicate with each other via neural oscillations to shape the olfactory percept. Communication between these nodes have been well characterized in non-human animals but less is known about their role in the human olfactory system. Using a recently developed and validated EEG-based method to extract signals from the OB and PC sources, we show in healthy human participants that there is a bottom-up information flow from the OB to the PC in the beta and gamma frequency bands, while top-down information from the PC to the OB is facilitated by delta and theta oscillations. Importantly, we demonstrate that there was enough information to decipher odor identity above chance from the low gamma in the OB-PC oscillatory circuit as early as 100ms after odor onset. These data further our understanding of the critical role of bidirectional information flow in human sensory systems to produce perception. However, future studies are needed to determine what specific odor information is extracted and communicated in the information exchange.

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Respiratory rhythm plays an important role in cognitive functions in rodents, as well as in humans. Respiratory related oscillation (RRO), generated in the olfactory bulb (OB), is an extrinsic rhythm imposed on brain networks. In rats, RRO can couple with intrinsic brain oscillations at theta frequency during sniffing and in the delta range outside of such episodes. Disruption of gamma synchronization in cortical networks by ketamine is well established whereas its effects on slow rhythms are poorly understood. We found in this study, that RRO in prefrontal cortex (PFC) and hippocampus (HC) remains present after ketamine injection, even on the background of highly unstable respiratory rate, co-incident with "psychotic-like" behavior and abnormal cortical gamma activity. Guided by the timing of ketamine-induced gamma reaction, pairwise coherences between structures exhibiting RRO and their correlation structure was statistically tested in 5-min segments post-injection (0-25 min) and during recovery (1, 5, 10 hours). As in control, RRO in the OB was firmly followed by cortical-bound OB exits directed toward PFC but not to HC. RRO between these structures, however, significantly correlated with OB-HC but not with OB-PFC. The only exception to this general observation was observed during a short transitional period, immediately after injection. ketamine has a remarkable history in psychiatric research. Modeling chronic NMDA-hypofunction using acute NMDA-receptor blockade shifted the primary focus of schizophrenia research to dysfunctional cortical microcircuitry and the recent discovery of ketamine's antidepressant actions extended investigations to neurophysiology of anxiety and depression. Cortical oscillations are relevant for understanding their pathomechanism.

The olfactory system enables humans to smell different odors, which are closely related to emotions. The high temporal resolution and non-invasiveness of Electroencephalogram (EEG) make it suitable to objectively study human preferences for odors. Effectively learning the temporal dynamics and spatial information from EEG is crucial for detecting odor-induced emotional valence. In this paper, we propose a deep learning architecture called Temporal Attention with Spatial Autoencoder Network (TASA) for predicting odor-induced emotions using EEG. TASA consists of a filter-bank layer, a spatial encoder, a time segmentation layer, a Long Short-Term Memory (LSTM) module, a multi-head self-attention (MSA) layer, and a fully connected layer. We improve upon the previous work by utilizing a two-phase learning framework, using the autoencoder module to learn the spatial information among electrodes by reconstructing the given input with a latent representation in the spatial dimension, which aims to minimize information loss compared to spatial filtering with CNN. The second improvement is inspired by the continuous nature of the olfactory process; we propose to use LSTM-MSA in TASA to capture its temporal dynamics by learning the intercorrelation among the time segments of the EEG. TASA is evaluated on an existing olfactory EEG dataset and compared with several existing deep learning architectures to demonstrate its effectiveness in predicting olfactory-triggered emotional responses. Interpretability analyses with DeepLIFT also suggest that TASA learns spatial-spectral features that are relevant to olfactory-induced emotion recognition.

Olfactory-induced emotion plays an important role in communication, decision-making, multimedia, and disorder treatment. Using electroencephalogram (EEG) technology, this paper focuses on (1) exploring the possibility of recognizing pleasantness induced by different concentrations of odors, (2) finding the EEG rhythm wave that is most suitable for the recognition of different odor concentrations, (3) analyzing recognition accuracies with concentration changes, and (4) selecting a suitable classifier for this classification task. To explore these issues, first, emotions induced by five different concentrations of rose or rotten odors are divided into five kinds of pleasantness by averaging subjective evaluation scores. Then, the power spectral density features of EEG signals and support vector machine (SVM) are used for classification tasks. Classification results on the EEG signals collected from 13 participants show that for pleasantness recognition induced by pleasant or disgusting odor concentrations, considerable average classification accuracies of 93.5% or 92.2% are obtained, respectively. The results indicate that (1) using EEG technology, pleasantness recognition induced by different odor concentrations is possible; (2) gamma frequency band outperformed other EEG rhythm-based frequency bands in terms of classification accuracy, and as the maximum frequency of the EEG spectrum increases, the pleasantness classification accuracy gradually increases; (3) for both rose and rotten odors, the highest concentration obtains the best classification accuracy, followed by the lowest concentration.

Reducing sugar intake is crucial for health, and odor sweetening enhances food enjoyment and quality perception. Current research relies on subjective manual sensory evaluations, which are poorly reproducible. Traditional methods also fail to capture dynamic neural responses to odor-induced sweetness. We propose an electroencephalogram local-global fusion transformer network (EEG-LGFNet) model to decode this impact objectively. Electroencephalogram data were collected from 16 subjects under different odor and sucrose stimuli. The model captures complex neural signals by integrating local and global feature extraction mechanisms. Its performance was validated across three-time windows, demonstrating efficacy over various temporal ranges. Analysis of the coefficient of determination across brain regions confirmed the importance of the frontal, central, and parietal areas of sweetness perception. The EEG-LGFNet model excelled in quantifying odor-enhanced sweetness, significantly outperforming state-of-the-art models. This research offers new insights into odor sweetening, with applications in food development, personalized nutrition, and neuroscience.

How long do the neural and cognitive effects of a brief odor experience last? This study investigated whether short exposures to pleasant and unpleasant odors can induce sustained changes in brain activity and influence memory formation for events occurring several seconds later. Using EEG, we combined univariate ERP analyses with time-resolved multivariate decoding to track neural responses during a 6-sec delay between odor presentation and visual memory encoding. We found that brief odor cues elicited sustained neural activity that persisted well beyond odor offset. Unpleasant odors, in particular, were associated with higher sustained ERP amplitudes compared with pleasant ones. Behaviorally, participants showed greater confidence in recognizing images that had been preceded by unpleasant odors, suggesting that even brief olfactory experiences can modulate memory encoding for temporally distant events. These findings demonstrate that brief olfactory cues have a lasting effect on both neural activity and subsequent memory performance.

BACKGROUND Emotion recognition plays a key role in multimedia. To enhance the sensation of reality, smell has been incorporated into multimedia systems because it can directly stimulate memories and trigger strong emotions. NEW METHOD For the recognition of olfactory-induced emotions, this study explored a combination method using a support vector machine (SVM) with an average frequency band division (AFBD) method, where the AFBD method was proposed to extract the power-spectral-density (PSD) features from electroencephalogram (EEG) signals induced by smelling different odors. The so-called AFBD method means that each PSD feature was calculated based on equal frequency bandwidths, rather than the traditional EEG rhythm-based bandwidth. Thirteen odors were used to induce olfactory EEGs and their corresponding emotions. These emotions were then divided into two types of emotions, pleasure and disgust, or five types of emotions that were very unpleasant, slightly unpleasant, neutral, slightly pleasant, and very pleasant. RESULTS Comparison between the proposed SVM plus AFBD method and other methods found average accuracies of 98.9% and 88.5% for two- and five-emotion recognition, respectively. These values were considerably higher than those of other combination methods, such as the combinations of AFBD or EEG rhythm-based features with naive Bayesian, k-nearest neighbor classification, voting-extreme learning machine, and backpropagation neural network methods. CONCLUSIONS The SVM plus AFBD method represents a useful contribution to olfactory-induced emotion recognition. Classification of the five-emotion categories was generally inferior to the classification of the two-emotion categories, suggesting that the recognition performance decreased as the number of emotions in the category increased.

Consumer neuroscience is a rapidly emerging field, with the ability to detect consumer attitudes and states via real-time passive technologies being highly valuable. While many studies have attempted to classify consumer emotions and perceived pleasantness of olfactory products, no known machine learning approach has yet been developed to directly predict consumer reward-based decision-making, which has greater behavioral relevance. In this proof-of-concept study, participants indicated their decision to have fragrance products repeated after fixed exposures to them. Single-trial power spectral density (PSD) and approximate entropy (ApEn) features were extracted from EEG signals recorded using a wearable device during fragrance exposures, and served as subject-independent inputs for 4 supervised learning algorithms (kNN, Linear-SVM, RBF- SVM, XGBoost). Using a cross-validation procedure, kNN yielded the best classification accuracy (77.6%) using both PSD and ApEn features. Acknowledging the challenging prospects of single-trial classification of high-order cognitive states especially with wearable EEG devices, this study is the first to demonstrate the viability of using sensor-level features towards practical objective prediction of consumer reward experience.

Dementia is a progressive neurodegenerative condition often preceded by Mild Cognitive Impairment (MCI), which is marked by early-stage memory difficulties and reduced cognitive flexibility. Detecting MCI at an early stage is crucial for timely intervention and for improving long-term cognitive health and quality of life. In this paper, we aim to differentiate between normal subjects and those suffering from MCI based on odor-evoked brain potentials from EEG signals. To address this challenge, we used publicly available multichannel EEG data and calculated a set of temporal-spectral components using wavelets, spectral grouping, and canonical correlation. These features are fed separately into attention-based convolutional neural network (CNN) models, which are individually trained on each feature-set, leading to individual feature branches. Later, these branches are fed into a fully connected network for performing the classification task. Our experiments demonstrate that the proposed method outperforms other methods considered in this paper. Ablation studies also reveal the individual strength of each set of features adopted in this study, along with their combined strength when the entire feature set is used for classification.

Recent technological advances have led to innovations like electronic noses and gas sensors, proficient in detecting distinct odors. Despite this, the field of AI and robotics has only marginally explored olfaction, a sense crucial for evoking emotions and memories. Our study investigates the correlation between gas sensor signals and EEG activity during odor recognition. By comparing our findings with questionnaire results, we suggest that individual experiences might influence odor recognition in the human brain. We designed an odor-dispensing system and recorded EEG responses from 15 subjects to six odors, alongside concentration data of four gases for each odor. These EEG and gas sensor data were analyzed using two neural networks for odor classification. Combining EEG and gas sensor data, we attained a 44% accuracy in 6-class odor discrimination, indicating the potential of this integrated approach as a unique ‘odor fingerprint’ for odor identification.

Objective: Accurate odor classification from EEG signals requires informative and interpretable features. Although Local Binary Pattern (LBP) and variants such as the center-symmetric binary pattern are widely used, they lack sufficient explainability and tensor-level implementations. Additionally, neuroscientific understanding of odor processing remains limited. Methods: We propose Tensor Center-Symmetric Binary Pattern (TensorCSBP), a novel tensor-based feature extractor designed for EEG odor analysis. TensorCSBP is integrated into an explainable feature engineering (XFE) pipeline with four steps: (1) TensorCSBP for feature generation, (2) CWNCA for feature selection, (3) tkNN classifier for decision making, and (4) DLob method for symbolic interpretability. Results: TensorCSBP XFE was evaluated on a newly collected 32-channel EEG dataset for odor detection. It achieved 96.68% accuracy under 10-fold cross-validation. Conclusions: The information entropy of the DLob symbol sequence was 3.5675, demonstrating the richness of the interpretability output. Significance: This study presents a high-accuracy, explainable, and computationally efficient model for EEG-based odor classification. TensorCSBP bridges low-level signal patterns with symbolic neuroscience insights, offering real-time potential for BCI and clinical applications.

Olfactory impairment and abnormal frontal EEG oscillations are recognized as early markers of Alzheimer’s disease (AD). Using a publicly available olfactory EEG dataset of 35 subjects spanning normal cognition, amnestic mild cognitive impairment (aMCI), and AD, each with MMSE scores and demographics, stimulus-locked epochs from four electrodes (Fp1, Fz, Cz, Pz) were processed with wavelet-based time–frequency analysis. Band-limited power ratios (delta, theta, alpha, beta) were computed as log-transformed post-odor/baseline values and aggregated to subject-level features. Statistical analyses revealed graded attenuation of odor-evoked frontal (Fp1) band-power ratios across groups, with significant differences in several band–odor combinations. PCA of Fp1 features showed partial separation of diagnostic categories, while multi-channel features offered weaker discrimination. Random forest classifiers trained on Fp1-only features achieved 66.7% test accuracy, outperforming the four-channel model (55.6%), with moderate sensitivity, specificity, and precision. These findings highlight that compact frontal wavelet-derived band-power ratios during olfactory stimulation carry diagnostically relevant information for distinguishing Normal, aMCI, and AD. The transparent pipeline, combining time–frequency processing, subject-level aggregation, and multiclass classification, offers a scalable framework that can be extended to larger cohorts or integrated with multimodal biomarkers.

No abstract available

Background Brain network dysfunction has been characterized by resting-state electroencephalography (EEG) and magnetic resonance imaging (MRI) in the prodromal stage. This study aimed to identify multi-modal electrophysiological and neuroimaging biomarkers for differential diagnosis in synucleinopathies and phenoconversion in isolated rapid eye movement sleep behavior disorder (iRBD). Methods We enrolled 35 patients with multiple system atrophy (MSA), 32 with Parkinson's disease (PD), 30 with iRBD and 30 matched healthy controls (HC). Power spectral density (PSD) was calculated in different frequency bands. EEG functional connectivity (FC) was calculated using the weighted Phase Lag Index (wPLI) after source localization. Significant network disruptions were further confirmed by MRI FC analysis. Results Quantitative EEG analysis demonstrated that delta and theta power spectral density significantly differed among MSA, PD and HC. The increased PSD was correlated with cognitive decline and olfactory dysfunction in PD. Band-specific FC profiles were observed in theta, alpha, and gamma bands. The hypoconnected alpha network significantly correlated with motor dysfunction, while the gamma FC distinguished PD from MSA. By integrating EEG and MRI network analyses, we found that FC between the olfactory cortex and dorsolateral prefrontal cortex was significantly different between MSA and PD. A multimodal discriminative model for MSA and PD, integrating spectral and FC attributes of EEG and MRI, yielded an area under the receiver operating characteristic curve of 0.900. Simultaneously, we found the FC abnormalities were more prominent than spectral features in iRBD indicating prodromal dysfunction. The decreased FC between the angular gyrus and striatum was identified in α-synucleinopathies. This hypoconnectivity was associated with dopaminergic degeneration in iRBD examined by dopamine transporter imaging. Discussion Our study demonstrated EEG spectral and functional profiles in prodromal and clinical-defined synucleinopathies. Multimodal EEG and MRI provided a novel approach to discriminate MSA and PD, and monitor neurodegenerative progression in the preclinical phase.

The human sense of smell is important for many vital functions, but with the current state of the art, there is a lack of objective and non-invasive methods for smell disorder diagnostics. In recent years, increasing attention is being paid to olfactory event-related potentials (OERPs) of the brain, as a viable tool for the objective assessment of olfactory dysfunctions. The aim of this review is to describe the main features of OERPs signals, the most widely used recording and processing techniques, and the scientific progress and relevance in the use of OERPs in many important application fields. In particular, the innovative role of OERPs is exploited in olfactory disorders that can influence emotions and personality or can be potential indicators of the onset or progression of neurological disorders. For all these reasons, this review presents and analyzes the latest scientific results and future challenges in the use of OERPs signals as an attractive solution for the objective monitoring technique of olfactory disorders.

The study of flavor is undergoing a paradigm shift, moving beyond traditional descriptive approaches towards a predictive and mechanistic science. While previous reviews have often focused on singular technological advances, this review provides a unique integrative perspective by synthesizing a multi-scale research framework that spans from molecular-level prediction to central nervous system interpretation. This review first delineates how artificial intelligence and computational simulations are revolutionizing the in-silico prediction of flavor compounds through structure-activity and ligand-receptor modeling. Simultaneously, it elaborates on how biosensors, built upon functional olfactory/taste receptors, provide unprecedented objectivity for flavor recognition at the biomimetic interface. Crucially, the indispensable role of neuroimaging techniques (EEG, fMRI) is emphasized in directly decoding the brain's integrated perceptual response to flavor-the ultimate arbiter of sensory experience. By synthesizing these advances into a cohesive "from molecule to brain" pipeline, this review not only serves as a roadmap for the future of flavor science but also proposes a unified framework for the field, highlighting the critical role of this integrated approach in guiding the rational design of food products and driving industry innovation.

The unclear relationship between structure and saltiness enhancement limits the development and application of savory odorants. The structure characteristic-saltiness enhancement perception (SEP) mechanisms of savory odorants were investigated by machine learning, molecular docking, and site-directed mutagenesis simulations. The XGBoost model (R2 = 0.96) showed better prediction on the maximum saltiness-enhancement ability of odorants based on their structures. The important features of the odorants contributing to SEP were analyzed by Shapley additive explanations (SHAP). Results showed that phenyl and aldehyde groups had significant positive contributions to SEP, with SHAP values of + 2.94 and + 0.74, respectively. Molecular docking and site-directed mutagenesis simulations elucidated the interaction region, forces, and key sites between savory odorants and olfactory receptors. Results showed TM3, TM5 and TM6 were the main interaction regions of the savory odorants prioritize binding with OR1A1 and OR1D2, resulting in the characteristic aromas. Hydrogen bonding and hydrophobic interactions were the key driving forces. Phe203, Asn109, and Asn155 of OR1A1 were partially important residues involved in the interactions with savory odorants. These findings presented a quick screening approach for savory odorants and revealed their SEP mechanism, providing theoretical guidance to facilitate the application of odor-induced salt reduction in food industry.

The human olfactory system's temporal dynamics are crucial for sensory perception. By learning the temporal dynamics of EEG and utilizing breathing signals, we aim to better understand the neural features of olfactory perception from EEG. To decode the olfactory response effectively, we introduce a new method: the Token Alignment and Cross-Attention Fusion network (TACAF), a multimodal deep learning framework that enhances olfactory EEG decoding using wavelet features for time window selection and spectral analysis for data representation. Spatial features are extracted using spatial learning modules, and temporal dynamics are captured through a multi-head self-attention mechanism. The Temporal Token Semantic Alignment (TTSA) module synchronizes breathing information with EEG data for effective fusion. We collected EEG recordings and breathing signals from 20 subjects to study the decoding responses to pleasant and unpleasant odors. Our evaluation shows that TACAF significantly outperforms existing methods. Further analysis indicates that prolonged odor exposure leads to olfactory adaptation, reducing recognition performance. The findings are visualized through spatial topology maps with saliency mappings, providing insights into the neural mechanisms of olfactory perception.

Classification of olfactory-induced electroencephalogram (EEG) signals has shown great potential in many fields. Since different frequency bands within the EEG signals contain different information, extracting specific frequency bands for classification performance is important. Moreover, due to the large inter-subject variability of the EEG signals, extracting frequency bands with subject-specific information rather than general information is crucial. Considering these, the focus of this letter is to classify the olfactory EEG signals by exploiting the spectral-domain information of specific frequency bands. In this letter, we present an olfactory EEG signal classification network based on frequency band feature extraction. A frequency band generator is first designed to extract frequency bands via the sliding window technique. Then, a frequency band attention mechanism is proposed to optimize frequency bands for a specific subject adaptively. Last, a convolutional neural network (CNN) is constructed to extract the spatio-spectral information and predict the EEG category. Comparison experiment results reveal that the proposed method outperforms a series of baseline methods in terms of both classification quality and inter-subject robustness. Ablation experiment results demonstrate the effectiveness of each component of the proposed method.

Odor sensory evaluation has a broad application in food, clothing, cosmetics, and other fields. Traditional artificial sensory evaluation has poor repeatability, and the machine olfaction represented by the electronic nose (E-nose) is difficult to reflect human feelings. Olfactory electroencephalogram (EEG) contains odor and individual features associated with human olfactory preference, which has unique advantages in odor sensory evaluation. However, the difficulty of cross-subject olfactory EEG recognition greatly limits its application. It is worth noting that E-nose and olfactory EEG are more advantageous in representing odor information and individual emotions, respectively. In this paper, an E-nose and olfactory EEG multimodal learning method is proposed for cross-subject olfactory preference recognition. Firstly, the olfactory EEG and E-nose multimodal data acquisition and preprocessing paradigms are established. Secondly, a complementary multimodal data mining strategy is proposed to effectively mine the common features of multimodal data representing odor information and the individual features in olfactory EEG representing individual emotional information. Finally, the cross-subject olfactory preference recognition is achieved in 24 subjects by fusing the extracted common and individual features, and the recognition effect is superior to the state-of-the-art recognition methods. Furthermore, the advantages of the proposed method in cross-subject olfactory preference recognition indicate its potential for practical odor evaluation applications.

Odor detection underpins food safety, environmental monitoring, medical diagnostics, and many more fields. The current artificial sensors developed for odor detection struggle with complex mixtures while non-invasive recordings lack reliable single-trial fidelity. To develop a general system for odor detection, in this study we present a preliminary work where we aim to test two hypotheses: (i) that spectral features of local field potentials (LFPs) are sufficient for robust single-trial odor detection and (ii) that signals from the olfactory bulb alone are adequate. To test two hypotheses, we propose an ensemble of complementary one-dimensional convolutional networks (ResCNN and AttentionCNN) that decodes the presence of odor from multichannel olfactory bulb LFPs. Tested on 2,349 trials from seven awake mice, our final ensemble model supports both hypotheses, achieving a mean accuracy of 86.6%, an F1-score of 81.0%, and an AUC of 0.9247, substantially outperforming previous benchmarks. In addition, the t-SNE visualization confirms that our framework captures biologically significant signatures. These findings establish the feasibility of robust single-trial detection of the presence of odor from extracellular LFPs, as well as demonstrate the potential of deep learning models to provide a deeper understanding of olfactory representations.

This article provides a background and descriptive analysis of insect memory and the coding of olfactory sensation in Drosophila, presenting graphs and summary statistics from a large dataset of neurons and synapses that was recently made publicly available and also discussing findings from the existing empirical literature. Some general principles from Drosophila olfaction are discussed as they apply to the design of analogous systems in artificial neural networks: (1) the networks used for coding are shallow; (2) the level of connectedness varies widely across neurons in the same layer; (3) much communication is between neurons in the same layer; (4) in most olfactory learning, the manner in which sensory inputs are represented in stored memory is largely fixed, and the learning process involves developing positive or negative associations with existing categories of inputs.

The olfactory bulb electroencephalogram (EEG) has been used as a method to imply receptor events12,13. However, experiments to correlate olfactory receptor and bulbar EEG activity in the same species of fish has not been performed. Reported here is the comparison between the simultaneously recorded receptor electroolfactogram (EOG) and the bulbar EEG in the channel catfish, Ictalurus punctatus. With amino acid stimulation of the olfactory mucosa, both the EOG and EEG exhibited an initial phasic response followed by a tonic level maintained throughout the stimulus duration. The relative magnitude of the tonic EEG activity (tonic level/phasic response), however, was significantly less than that for the EOG. Both EOG and integrated EEG responses increased exponentially with logarithmic increase in stimulus concentration from threshold to 10(-3) M. Estimated electrophysiological thresholds for 5 amino acids tested determined by both recording methods did not differ significantly and averaged 10(-9.3) +/- 0.2 M for the EOG and 10(-9.1) +/- 0.2 M for the EEG. There was also a significant correlation between the order of relative effectiveness for 11 amino acids determined by EOG and EEG recordings. These results indicate that in the catfish the olfactory bulb EEG is an indicator of olfactory receptor activity.

Olfaction is dependent on respiration for the delivery of odorants to the nasal cavity. Taking advantage of the time-locked nature of inspiration and olfactory processing, electroencephalogram dipole modeling (EEG/DT) has previously been used to identify a cascade of inspiration-triggered neural activity moving from primary limbic olfactory regions to frontal cortical areas during odor perception. In this study, we leverage the spatial resolution of functional magnetic resonance imaging (fMRI) alongside the temporal resolution of EEG to replicate and extend these findings. Brain activation identified by both modalities converged within association regions of the orbitofrontal cortex that were activated from approximately 150-300 ms after inspiration onset. EEG/DT was additionally sensitive to more transient activity in primary olfactory regions, including the parahippocampal gyrus and amygdala, occurring approximately 50 ms post-inspiration. These results provide a partial validation of the spatial profile of the olfactory cascade identified by EEG source modeling, and inform novel future directions in the investigation of human olfaction.

Temporal lobe electroencephalogram (EEG) activity was quantitatively analyzed in obsessive-compulsive disorder (OCD) when subjects are at rest and during a temporal lobe activating procedure, i.e., olfactory stimulation. At rest with eyes closed, delta-1 and alpha-2 power differences were evident in OCD patients as compared with normal controls. During olfactory stimulation, differences between patients and normal groups were detectable in the slower beta frequencies: Normal subjects showed a power increase, whereas OCD patients showed no modification or slight decrease. Our results support previous findings of temporal lobe EEG abnormalities in OCD patients with an abnormal pattern of response to a temporal lobe activating procedure.

Olfactory function impairment has been observed in patients with idiopathic Parkinson's disease (PD) using psychophysical procedures. In this study, an electrophysiological technique based on spectral analysis and topographic EEG mapping was used to evaluate EEG arousal response to olfactory stimulation in ten normal subjects and ten PD patients in stage II according to Hoehn and Yahr's scale, all of whom had akinetic-rigid syndrome and were receiving chronic L-Dopa therapy. Olfactory function was stimulated using diluted benzaldehyde by means of an apparatus set up in our laboratory. Spontaneous and post-olfactory stimulus EEGs were recorded from 20 electrodes placed upon the scalp according to the Int. 10-20 System. Total EEG power (from 0.5 to 19.5 Hz) was evaluated over 10 sec. artefact-free epochs. An olfactory arousal reaction was detected in nine of the ten PD patients and was characterised by a significant decrease in total power similar to that observed in all of the 10 normal subjects; these variations in EEG were found in all but the anterior regions of the scalp. The use of this technique does not seem to reveal any clear alteration in the olfactory function of PD patients.

Pronounced chemosensory adaptation affects many patients with olfactory loss. The study aimed to investigate adaptation to olfactory and trigeminal nasal stimuli in patients with olfactory loss in comparison to controls using electrophysiological measures. Thirty-four patients with olfactory loss (mean age ± SD = 59 ± 16 years) and 17 healthy volunteers (mean age ± SD = 50 ± 14 years) were recruited. Sniffin’ sticks test was used for evaluation of olfactory function and EEG-derived chemosensory event-related potentials were recorded. Intranasal stimuli were presented using high-precision, computer-controlled stimulators based on the principles of air-dilution olfactometry. Data were analyzed in two different approaches according to the relatively short or long inter-stimulus interval. A decreased peak amplitude or a prolonged latency was considered as an expression of adaptation. The majority of participants (88%) responded reliably to chemosensory stimulation. Patients with olfactory loss exhibited pronounced olfactory and trigeminal adaptation within the long-term design, without such effects in healthy controls. Odor sensitivity correlated with both olfactory and trigeminal amplitude changes: the worse the olfactory sensitivity, the more pronounced chemosensory adaptation. The results help to explain the patients’ complaints in terms of the fast adaptation towards chemosensory stimuli, for example during eating and drinking. The differences in adaptation in patients with olfactory loss and healthy controls could serve as a clinical criterion to gauge olfactory dysfunction.

Electroencephalography (EEG) offers psychophysiologic tools to improve sensitivity for detecting objective effects in complementary and alternative medicine. This current investigation extended prior clinical research studies to evaluate effects of one of two different homeopathic remedies on resting EEG cordance after an olfactory activation protocol on healthy young adults with remedy-relevant, self-perceived characteristics. Ninety-seven (7) young adults (N=97, mean age 19 years, 55% women) with good self-rated global health and screened for homeopathic constitutional types consistent with one of two remedies (either Sulphur or Pulsatilla) underwent three weekly laboratory sessions. At each visit, subjects had 5-minute resting, eyes-closed EEG recordings before and after a placebo-controlled olfactory activation task with their constitutionally relevant verum remedy. One remedy potency (6c, 12c, or 30c) used per week, was presented in a randomized order over the 3 sessions. Prefrontal resting EEG cordance values at Fp1 and Fp2 were computed from artifact-free 2-minute EEG samples from the presniffing and postsniffing rest periods. Cordance derives from an algorithm that incorporates absolute and relative EEG values. The data showed significant two-way oscillatory interactions of remedy by time for ß, α, θ, and δ cordance, controlling for gender and chemical sensitivity. EEG cordance provided a minimally invasive technique for assessing objective nonlinear physiologic effects of two different homeopathic remedies salient to the individuals who received them. Time factors modulated the direction of effects. Given previous evidence of correlations between cordance and single-photon emission computed tomography, these findings encourage additional neuroimaging research on nonlinear psychophysiologic effects of specific homeopathic remedies.

The olfactory EEG of awake animals displays oscillatory bursts of activity in the gamma- (30-100 Hz) range. The bursts are correlated with inflow of air over the receptor layer in the nose. None of the inputs to the cortices that display these oscillations carries periodic signals in the gamma-range. Thus these bursts are generated locally, either by neuronal feedback interactions or by coupling of oscillatory neurons. In the first case if the oscillations are generated by negative feedback, then two classes of cells must exist: excitatory neurons and inhibitory neurons with the same frequency of oscillation but with a quarter cycle phase lag by the inhibitory cells from the excitatory cells. On the other hand, if the EEG's result from coupling of cells that are intrinsically oscillatory, there should be a broad but monomodal distribution of phase values. In order to determine the origin of these bursts, we performed simultaneous recordings of EEG and multi-unit spikes in the 4 parts of the olfactory system (olfactory bulb, anterior olfactory nucleus, prepyriform cortex and lateral entorhinal area) of awake and motivated rats. For each sample, the EEG and the multi-unit spikes were recorded from the same local neighborhood. The multi-unit electrode recorded pulses from the principal output neurons of the respective cortical areas. In all locations tested, the oscillations in pulse probabilities of firing were found to have the same frequency as the dominant EEG frequency. In all 4 structures two sets of cells were found. One set displayed pulses in phase with the EEG and the other set displayed pulses that led or lagged the EEG by approximately 1/4 cycle. These data confirm the negative feedback interaction model rather than the coupled oscillator model for the generation of the bursts in the olfactory system. The relevance of these findings to other cortical systems, in casu the visual cortex is discussed.

In the modern information society, people are constantly exposed to stress due to complex work environments and various interpersonal relationships. Aromatherapy is attracting attention as one of the methods for relieving stress using aroma. A method to quantitatively evaluate such an effect is necessary to clarify the effect of aroma on the human psychological state. In this study, we propose a method of using two biological indexes, electroencephalogram (EEG) and heart rate variability (HRV), to evaluate human psychological states during the inhalation of aroma. The purpose is to investigate the relationship between biological indexes and the psychological effect of aromas. First, we conducted an aroma presentation experiment using seven different olfactory stimuli while collecting data from EEG and pulse sensors. Next, we extracted the EEG and HRV indexes from the experimental data and analyzed them with respect to the olfactory stimuli. Our study found that olfactory stimuli have a strong effect on psychological states during aroma stimuli and that the human response to olfactory stimuli is immediate but gradually adapts to a more neutral state. The EEG and HRV indexes showed significant differences between aromas and unpleasant odors especially for male participants in their 20-30s, while the delta wave and RMSSD indexes showed potential for generalizing the method to evaluate psychological states influenced by olfactory stimuli across genders and generations. The results suggest the possibility of using EEG and HRV indexes to evaluate psychological states toward olfactory stimuli such as aroma. In addition, we visualized the psychological states affected by the olfactory stimuli on an emotion map, suggesting an appropriate range of EEG frequency bands for evaluating psychological states applied to the olfactory stimuli. The novelty of this research lies in our proposed method to provide a more detailed picture of the psychological responses to olfactory stimuli using the integration of biological indexes and emotion map, which contributes to the areas such as marketing and product design by providing insights into the emotional responses of consumers to different olfactory products.

To reveal covert abilities in a minimally conscious state (MCS) through an innovative activation paradigm based on olfactory imagery. Case study. A patient in MCS was asked to 'imagine an unpleasant odour' or to 'relax' in response to the appearance on a screen of a downward pointing arrow or a cross, respectively. Electrophysiological responses to stimuli were investigated by means of an 8-channel EEG equipment and analysed using a specific threshold algorithm. The protocol was repeated for 10 sessions separated from each other by 2 weeks. Accuracy, defined as the number of successes with respect to the total number of trials, was used to evaluate the number of times in which the classification strategy was successful. Analyses of accuracy showed that the patient was able to activate and to relax himself purposefully and that he optimized his performances with the number of sessions, probably as a result of training-related improvements. Subtle signs of consciousness may be under-estimated and need to be revealed through specific activation tasks. This paradigm may be useful to detect covert signs of consciousness, especially when patients are precluded from carrying out more complex cognitive tasks.

Chemosensory function in pregnant women is far from being fully understood due to the lack of data and inconsistencies between the results of self-reports and objective studies. In the present study in pregnant and non-pregnant women (n Results indicate that the olfactory event-related potential amplitudes or latencies of the P1, N1, and P2 components remain unchanged in pregnant women. In accordance with these findings, no difference was observed between pregnant and non-pregnant women in psychophysical olfactory tests. However, pregnant women displayed a lower degree of sensitivity to trigeminal stimuli compared to non-pregnant controls, which was also reflected in the electrophysiological responses to trigeminal stimuli. Counterintuitive as they may seem, our findings demonstrate a "flattening" of chemosomatosensory responses. Psychological processes occurring during pregnancy, such as changes in socioemotional perception of odors resulting from the diminished stress response, may provide a background to these results. Overall, the present results indicate the absence of major differences between non-pregnant and pregnant women in terms of measured olfactory function though chemosomatosensory function of the pregnant women appears to be decreased.

To compare emotional responses elicited by four cosmetic products on different sensory modalities (smell, visual, and touch), and analyze the link between objective instrumental analysis results and subjective evaluation of participants occurring within dimensional valence-arousal model of emotions. In this study, four cream products exhibiting variations in olfactory perception, visual appearance and perception usability were selected. Electroencephalography (EEG) and a subjective emotion scale were used to assess participants' emotional responses during the sensory experience of utilizing the creams. The study revealed that the objective emotional valence and arousal of different cream products exhibited certain variations at distinct stages of usage. The trend of valence differences induced by different products measured by EEG at the same stage was almost as same as measured by subjective evaluation. The correspondence between the valence measured by EEG closely approximated that obtained through subjective evaluation across various products at distinct stages of usage. These findings demonstrate a significant correlation between EEG-based valence and subjective valence, however, no such relationship was observed for arousal. This study demonstrates the feasibility of using EEG as a method to assess emotions elicited by various stages of cosmetics application, including smelling, looking, rubbing, and afterfeel. This technique serves as a valuable supplement to traditional methods for examining emotional responses by providing more objective evidence.

The capacity to pay attention is important for the cognitive ability, for example, evaluating an object for its qualities. Attention can selectively prioritize the neural processes that are relevant to a given task. Neuroimaging investigations on human attention are primarily focused on vision to the exclusion of other sensory systems, particularly olfaction. Neural underpinnings of human olfactory attention are still not clearly understood. Here, we combined electroencephalographic measurements of olfactory event related potential with electrical neuroimaging to investigate how the neural responses after inhaling the same odor differ between conditions with varying levels of attention, and, in which brain areas. We examined the neural responses when participants attended to a rose-like odor of phenylethyl alcohol for evaluating its pleasantness versus its passive inhalation. Our results gathered significant evidence for attentional modulation of the olfactory neural response. The most prominent effect was found for the late positive component, P3, of olfactory event related potential within a second from the odor onset. The source reconstruction of this data revealed activations in a distributed network of brain regions predominantly in inferior frontal cortex, insula, and  inferior temporal gyrus. These results suggest that the neuronal modulations from attention to olfactory pleasantness may be subserved by this network.

Sensory information is initially registered within anatomically and functionally segregated brain networks but is also integrated across modalities in higher cortical areas. Although considerable research has focused on uncovering the neural correlates of multisensory integration for the modalities of vision, audition, and touch, much less attention has been devoted to understanding interactions between vision and olfaction in humans. In this study, we asked how odors affect neural activity evoked by images of familiar visual objects associated with characteristic smells. We employed scalp-recorded EEG to measure visual ERPs evoked by briefly presented pictures of familiar objects, such as an orange, mint leaves, or a rose. During presentation of each visual stimulus, participants inhaled either a matching odor, a nonmatching odor, or plain air. The N1 component of the visual ERP was significantly enhanced for matching odors in women, but not in men. This is consistent with evidence that women are superior in detecting, discriminating, and identifying odors and that they have a higher gray matter concentration in olfactory areas of the OFC. We conclude that early visual processing is influenced by olfactory cues because of associations between odors and the objects that emit them, and that these associations are stronger in women than in men.

Feline osteoarthritis (OA) leads to chronic pain and somatosensory sensitisation. In humans, sensory exposure can modulate chronic pain. Recently, electroencephalography (EEG) revealed a specific brain signature to human OA. However, EEG pain characterisation or its modulation does not exist in OA cats, and all EEG were conducted in sedated cats, using intradermal electrodes, which could alter sensory (pain) perception. Cats (n=11) affected by OA were assessed using ten gold-plated surface electrodes. Sensory stimuli were presented in random orders: response to mechanical temporal summation, grapefruit scent and mono-chromatic wavelengths (500 nm-blue, 525 nm-green and 627 nm-red light). The recorded EEG was processed to identify event-related potentials (ERP) and to perform spectral analysis (z-score). The procedure was well-tolerated. The ERPs were reported for both mechanical (F3, C3, Cz, P3, Pz) and olfactory stimuli (Cz, Pz). The main limitation was motion artifacts. Spectral analysis revealed a significant interaction between the power of EEG frequency bands and light wavelengths (p<0.001). All wavelengths considered, alpha band proportion was higher than that of delta and gamma bands (p<0.044), while the latter was lower than the beta band (p<0.016). Compared to green and red, exposure to blue light elicited distinct changes in EEG power over time (p<0.001). This is the first demonstration of EEG feasibility in conscious cats with surface electrodes recording brain activity while exposing them to sensory stimulations. The identification of ERPs and spectral patterns opens new avenues for investigating feline chronic pain and its potential modulation through sensory interventions.

Although behavioral and neuropsychological data regarding the existence of images for odors are inconclusive, reconsideration of earlier EEG work provides reasonably clear evidence for an inner nose. However, further EEG studies and neuroimaging data seem essential for conclusive demonstration of an inner nose.

Traumatic anosmy can be found after any traumatism of the skull, whatever its impact and intensity. A long loss of consciousness or a heavy post-commotionnal syndrom can increase a traumatic anosmy. The best way to really assess a traumatic loss of smell is to use of olfacto-breathing reflex and possibility for confirmation, the olfactive electroencephalography. The suppression of a traumatic anosmy can happen, but the published statistics on this matter seem a little too optimistic. With regard to stimulators, the rate is far too high than it is usually accepted.

In recent years, the evaluation of potential events related to olfactory events (OERPs) and trigeminal events (TERPs) has become increasingly important in the diagnosis of olfactory disorders. This technique is increasingly used in basic research and clinical practice to evaluate people suffering from olfactory disorders. In a pilot project of the first investigations of OERPs and TERPs in the Czech Republic, we analyse the event-related potentials of the data of normosmic participants. In the prospective study, 21 normosmic participants were enrolled for a 2-year period (5/2021-5/2023). OERPs/TERPs were recorded at the scalp vertex (electrode Pz/Cz). Odourants 2-phenylethanol/CO2 were used to selectively activate Nervus olfactorius/ Nervus trigeminus. Brain responses to olfactory/trigeminal stimuli (EEG) were recorded in 21/18 normosmic subjects. In the statistical analysis of the olfactory interval N1-P2 (age, gender), we found no statistically significant differences. In the statistical analysis of the trigeminal interval N1-P2 (age, gender) we found statistically significant differences in amplitude by gender (male amplitudes were higher than female amplitudes, p = 0.006). Our pilot data can function very well as an internal guide for ongoing and future olfactory research studies. Evaluation of the presence of OERPs appears to be an important parameter for the evaluation of olfactory disorders. The absence of OERPs is a strong indicator of the presence of olfactory dysfunction.

Electroencephalography (EEG), and the measure of event-related potentials (ERPs) in particular, are useful methods to study the cognitive and cerebral mechanisms underlying the perception and processing of food cues. Further research on these aspects is necessary to better understand how cognitive functioning may influence food choices in different populations (e.g. obese individuals, individuals with eating disorders). To help researchers in designing future studies, this article provides an overview of the methods used in the current literature on ERPs and food-related cognition. Several methodological aspects are explored to outline interesting perspectives for future research, including discussions on the main experimental tasks used, the cognitive functions assessed (e.g. inhibitory control, attentional processing), the characteristics of the participants recruited (e.g. weight status, eating behaviors), and the stimuli selected (e.g. food pictures, odors). The issues generated by some of these methodological choices are discussed, and a few guidelines are provided.

It is not possible to accurately predict the perceptual response to odorants and odorant mixtures without understanding patterns of suppression and facilitation that result from interactions between the olfactory and trigeminal systems. The current study extends previous findings by exploring the effect of intensive training on the interaction between these systems and also by using a different mixed chemosensory stimulus to examine whether the principles established in earlier studies generalize to different odorants. Stimuli were chosen so as to selectively activate the olfactory (H2S) and trigeminal (CO2) nerves. In addition, linalool was included as a stimulus that activated both systems. Thirty-five participants (19 men, 16 women) rated the intensity of each stimulus when presented both alone and in binary mixtures (linalool + H2S, and linalool + CO2). Chemosensory event-related potentials were obtained from three recording positions. Analysis of intensity ratings showed that linalool was significantly less intense than the other stimuli when presented alone. In binary mixtures, H2S was strongly suppressed by linalool. One week of intensive odor training produced significant and specific reductions in the intensity of linalool and H2S, both alone and in their mixture. Training with a different odor (champignol) had no effect. Chemosensory event-related potential data confirmed previous findings showing changes in topographical distribution that reflected the degree of trigeminal activity. Binary mixtures generally produced larger amplitudes than single stimuli. Latencies clearly differentiated between the three single stimuli and the binary mixtures. Changes were observed in event-related potentials that reflected those obtained for intensity ratings in that they were observed for linalool and H2S in the linalool trained group only. The amplitude of the late 'endogenous' component (P3) was significantly decreased for these odors at frontal recording sites. In summary, strong and specific training effects were observed in intensity ratings for participants trained with the test odor (linalool), but not for those trained with a different odor. This was supported by a significant decrease of amplitudes of the event-related potentials at frontal recording sites following training with the test odor only

Previous studies in schizophrenic patients have suggested that there are changes in olfactory sensitivity. In order to externally validate a psychometrical assessment of the psychosis-risk indicated by schizotypic factors, this study was carried out to determine whether changes in olfactory perception could be determined even for persons merely at risk of developing schizophrenia. These 'psychosis-prone' subjects consistently scored high in either the scale for 'physical anhedonia' (PA) or the scale for 'perceptual aberration' (PAB). Thus, three groups were investigated (control, n = 11; PA, n = 12; PAB, n = 12). Each subject participated in one testing session where the two odorants, vanillin (pleasant) and hydrogen sulphide (unpleasant), were applied by means of a specially designed delivery apparatus. Subjects rated both the intensity and the hedonic quality of the stimuli. In addition, olfactory event-related potentials (OERP) were recorded after dichotomous stimulation. In general, there were only few significant differences between the three groups investigated. Contrary to expectations, ratings for pleasantness of vanillin were highest in PA subjects compared to PAB subjects and controls (p < 0.05). Correspondingly, OERP amplitudes in response to vanillin were largest within the PA group (p < 0.05). For hydrogen sulphide, PAB subjects showed the smallest OERP amplitudes (p < 0.05). In addition, it was observed that female subjects had significantly larger OERP amplitudes when compared to male subjects (p < 0.05), which possibly indicates gender differences in olfactory sensitivity.

Decoding olfactory cognition has been generating significant interest in recent years due to a wide range of applications, from diagnosing neurodegenerative disorders to consumer research and traditional medicine. In this study, we have investigated whether changes in odor stimuli evaluation across repeated stimuli presentation can be attributed to changes in brain perception of the stimuli. Epoch intervals representing olfactory sensory perception were extracted from electroencephalography (EEG) signals using minimum variance distortionless response (MVDR)-based single trial event related potential (ERP) approach to understand the evoked response to high pleasantness and low pleasantness stimuli. We found statistically significant changes in self reported stimuli evaluation between initial and final trials (p < 0.05) for both stimuli categories. However, the changes in ERP amplitude were found to be statistically significant only for the high pleasantness stimuli. This implies that olfactory stimuli of higher hedonic value recruit high-order cognitive processing that may be responsible for initial increased ERP response, as well as for rapid subsequent adaptation in processing the stimuli.

Using flow-olfactometer for chemosensory event related brain potentials (CSERP) the air escapes the contralateral nostril from the stimulated nostril via the nasopharynx. Theoretically, the escaping odorous airflow is able to stimulate the contralateral chemosensory receptors and might activate the olfactory or the trigeminal brain processes. Testing 18 healthy subjects, we were able to show that it was possible to generate CSERP by strictly monorhinal stimulation with closed contralateral nostril. That means that the rectangular shapes of the brief chemosensory stimuli were not disturbed. The latencies of N1 and P2 and the amplitudes (N1P2) of CSERP (stimulants: H2S and CO2) were not different with either open or closed contralateral nostril. The induced CSERP were independent of stimulated nostril side. Additionally we found that with closed contralateral nostril more than 90% of passive monorhinal chemosensory stimuli were perceived. In further imaging studies the presented paradigm should be applied with strictly monorhinal stimulation to investigate the chemosensory processing pathways with high time resolution (EEG/MEG).

Trigeminal/neuronal hyperexcitability and spreading depression activating the trigemino-vascular system are discussed in migraine-pathophysiology. This study investigated trigeminal and olfactory event-related potentials in migraineurs. Nasal chemosensitivity was assessed in 19 female migraineurs with or without aura > 72 h before or after an attack and in 19 healthy females employing event-related cortical potentials (ERPs) after specific trigeminal stimulation of nasal nociceptors with short pulses of CO2, and specific olfactory stimulation with H2S. Odour thresholds and odour identification performance were also tested. Migraineurs exhibited greater responses to trigeminal stimulation, indicated by significantly larger ERP amplitudes N1. In contrast, olfactory ERP amplitudes P1N1 were significantly smaller in migraineurs. A leave-one-out classification procedure on the basis of these two parameters assigned 76.3% cases correctly. The olfactory ERP amplitude discriminated better between groups than trigeminal ERPs (71.1 vs. 68.4% correct classification). Our data suggest trigeminal hyperexcitability in migraineurs. A general increase of nasal chemosensitivity is not supported because of smaller olfactory ERP amplitudes in migraineurs. Olfactory ERPs discriminate better than trigeminal ERPs between migraineurs and controls, emphasizing the significance of the olfactory system in migraine.

The present study aimed to investigate the effects of cognitive/emotional bias on central nervous odor processing. Forty-five female participants were divided into three groups and were either led to believe the odor was a natural, healthy extract (positive bias), potentially hazardous (negative bias), or a common test odorant (control). The odor (isobornyl acetate) was presented via a constant-flow olfactometer and the EEG was recorded from 60 scalp locations. In the negative bias condition, participants reported reduced well-being and judged the odor as less pleasant. However, neither the thresholds nor the intensity ratings were changed by the context condition. Chemosensory event-related potential (CSERP) analysis revealed that the latencies of the N1 and P2 components were prolonged in the negative bias condition and shortened in the positive bias condition. Current source densities were most prominent in the frontal lobe in negatively biased participants. The findings show that expecting to perceive an emotionally significant odor affects the early encoding of odors.

The influence of Huntington's disease (HD) on the olfactory event-related potential (OERP), an electrophysiological measure of olfactory information processing, has not been reported to date. In the present study, olfactory and auditory event-related potentials (ERPs) were recorded monopolarly from Fz, Cz, and Pz electrode sites in 8 patients with HD and 8 age- and gender-matched control participants. Results demonstrated that individuals with HD were delayed compared with controls on the P3 component of the OERP (p<.001), with a trend toward a significant delay on the auditory ERP P3 (p<.06). The effect size for OERP P3 latency (pi(2)=.72) was larger than that for the auditory P3 (pi(2)=.24), which has previously been shown to be delayed in HD. Patients performed significantly worse than controls did on all neuropsychological measures. These measures significantly correlated with several components of the OERP. These findings extend the understanding of olfactory deficits in HD.

The aim of the present research was to investigate the influences of cognition on temporal processing of olfactory information in a health-relevant context. We investigated whether expecting an odor to cause adverse health effects alters perception of that odor. An irritation-free odor (Study 1: H(2)S; Study 2: phenyl ethyl alcohol [PEA]) was presented after which participants expected to experience either adverse sensory irritation (caused by intranasal CO(2) presentation) in one condition or no adverse effects in another condition, depending on a previously presented visual cue. Olfactory event-related potentials (OERPs) were measured to assess effects of expectations on the temporal course of olfactory processing. When participants expected irritancy after perceiving the odor of H(2)S, N1 and P3 peak amplitude and N1 latency were increased and shortened, respectively, suggesting more intense and faster processing of the odor as well as effects on salience and anticipation of sensory irritation. When the odor was PEA, only the N1 amplitude was increased. These results, obtained with OERP, provide converging evidence for comparable conclusions regarding the influence of cognition on odor perception reached with functional magnetic resonance imaging. Furthermore, the results suggest that a priori hedonic valence of an odor affects how susceptible the olfactory percept is to modulation via expectations.

Although the etiology of major depressive disorder remains poorly understood, reduced gamma oscillations is an emerging biomarker. Olfactory bulbectomy, an established model of depression that reduces limbic gamma oscillations, suffers from non-specific effects of structural damage. Here, we show that transient functional suppression of olfactory bulb neurons or their piriform cortex efferents decreased gamma oscillation power in limbic areas and induced depression-like behaviors in rodents. Enhancing transmission of gamma oscillations from olfactory bulb to limbic structures by closed-loop electrical neuromodulation alleviated these behaviors. By contrast, silencing gamma transmission by anti-phase closed-loop stimulation strengthened depression-like behaviors in naive animals. These induced behaviors were neutralized by ketamine treatment that restored limbic gamma power. Taken together, our results reveal a causal link between limbic gamma oscillations and depression-like behaviors in rodents. Interfering with these endogenous rhythms can affect behaviors in rodent models of depression, suggesting that restoring gamma oscillations may alleviate depressive symptoms.

The transmission of the heartbeat through the cerebral vascular system causes intracranial pressure pulsations. We discovered that arterial pressure pulsations can directly modulate central neuronal activity. In a semi-intact rat brain preparation, vascular pressure pulsations elicited correlated local field oscillations in the olfactory bulb mitral cell layer. These oscillations did not require synaptic transmission but reflected baroreceptive transduction in mitral cells. This transduction was mediated by a fast excitatory mechanosensitive ion channel and modulated neuronal spiking activity. In awake animals, the heartbeat entrained the activity of a subset of olfactory bulb neurons within ~20 milliseconds. Thus, we propose that this fast, intrinsic interoceptive mechanism can modulate perception-for example, during arousal-within the olfactory bulb and possibly across various other brain areas.

The need to breathe links the mammalian olfactory system inextricably to the respiratory rhythms that draw air through the nose. In rodents and other small animals, slow oscillations of local field potential activity are driven at the rate of breathing (∼2-12 Hz) in olfactory bulb and cortex, and faster oscillatory bursts are coupled to specific phases of the respiratory cycle. These dynamic rhythms are thought to regulate cortical excitability and coordinate network interactions, helping to shape olfactory coding, memory, and behavior. However, while respiratory oscillations are a ubiquitous hallmark of olfactory system function in animals, direct evidence for such patterns is lacking in humans. In this study, we acquired intracranial EEG data from rare patients (Ps) with medically refractory epilepsy, enabling us to test the hypothesis that cortical oscillatory activity would be entrained to the human respiratory cycle, albeit at the much slower rhythm of ∼0.16-0.33 Hz. Our results reveal that natural breathing synchronizes electrical activity in human piriform (olfactory) cortex, as well as in limbic-related brain areas, including amygdala and hippocampus. Notably, oscillatory power peaked during inspiration and dissipated when breathing was diverted from nose to mouth. Parallel behavioral experiments showed that breathing phase enhances fear discrimination and memory retrieval. Our findings provide a unique framework for understanding the pivotal role of nasal breathing in coordinating neuronal oscillations to support stimulus processing and behavior. Animal studies have long shown that olfactory oscillatory activity emerges in line with the natural rhythm of breathing, even in the absence of an odor stimulus. Whether the breathing cycle induces cortical oscillations in the human brain is poorly understood. In this study, we collected intracranial EEG data from rare patients with medically intractable epilepsy, and found evidence for respiratory entrainment of local field potential activity in human piriform cortex, amygdala, and hippocampus. These effects diminished when breathing was diverted to the mouth, highlighting the importance of nasal airflow for generating respiratory oscillations. Finally, behavioral data in healthy subjects suggest that breathing phase systematically influences cognitive tasks related to amygdala and hippocampal functions.

Spike timing-based representations of sensory information depend on embedded dynamical frameworks within neuronal networks that establish the rules of local computation and interareal communication. Here, we investigated the dynamical properties of olfactory bulb circuitry in mice of both sexes using microelectrode array recordings from slice and in vivo preparations. Neurochemical activation or optogenetic stimulation of sensory afferents evoked persistent gamma oscillations in the local field potential. These oscillations arose from slower, GABA(A) receptor-independent intracolumnar oscillators coupled by GABA(A)-ergic synapses into a faster, broadly coherent network oscillation. Consistent with the theoretical properties of coupled-oscillator networks, the spatial extent of zero-phase coherence was bounded in slices by the reduced density of lateral interactions. The intact in vivo network, however, exhibited long-range lateral interactions that suffice in simulation to enable zero-phase gamma coherence across the olfactory bulb. The timing of action potentials in a subset of principal neurons was phase-constrained with respect to evoked gamma oscillations. Coupled-oscillator dynamics in olfactory bulb thereby enable a common clock, robust to biological heterogeneities, that is capable of supporting gamma-band spike synchronization and phase coding across the ensemble of activated principal neurons.

Studies of neuronal oscillations have contributed substantial insight into the mechanisms of visual, auditory, and somatosensory perception. However, progress in such research in the human olfactory system has lagged behind. As a result, the electrophysiological properties of the human olfactory system are poorly understood, and, in particular, whether stimulus-driven high-frequency oscillations play a role in odor processing is unknown. Here, we used direct intracranial recordings from human piriform cortex during an odor identification task to show that 3 key oscillatory rhythms are an integral part of the human olfactory cortical response to smell: Odor induces theta, beta, and gamma rhythms in human piriform cortex. We further show that these rhythms have distinct relationships with perceptual behavior. Odor-elicited gamma oscillations occur only during trials in which the odor is accurately perceived, and features of gamma oscillations predict odor identification accuracy, suggesting that they are critical for odor identity perception in humans. We also found that the amplitude of high-frequency oscillations is organized by the phase of low-frequency signals shortly following sniff onset, only when odor is present. Our findings reinforce previous work on theta oscillations, suggest that gamma oscillations in human piriform cortex are important for perception of odor identity, and constitute a robust identification of the characteristic electrophysiological response to smell in the human brain. Future work will determine whether the distinct oscillations we identified reflect distinct perceptual features of odor stimuli.

The main olfactory bulb (MOB) is highly plastic and constantly reconfiguring its function and structure depending on sensory experience. Despite the extensive evidence of anatomical, functional and behavioural changes in the olfactory system induced by highly variable olfactory experiences, it is still unknown whether prolonged passive odour experience could reconfigure the MOB at its input and network activity levels and whether these changes impact innate olfaction. Here, by measuring odour-induced glomerular activation, MOB network activity and innate olfactory behaviours, we described a profound MOB reconfiguration induced by prolonged passive olfactory experience in adult animals that impacts MOB input integration at the glomerular layer including an increase in the activated glomerular area and signal intensity, which is combined with a refinement in the number of activated glomeruli and less-overlapped glomerular maps. We also found that prolonged passive olfactory experience dramatically changes MOB population activity in the presence and absence of odours, which is reflected as a decrease in slow oscillations (<12 Hz) and an increase in fast oscillations (>12 Hz). All these functional changes in awake and anaesthetized mice correlate with an increase in brain-derived neurotrophic factor (BDNF) and with improved innate olfactory responses such as habituation/dishabituation and innate preference/avoidance. Our study shows that prolonged passive olfactory experience in adult animals produces a dramatic reconfiguration of the MOB network, possibly driven by BDNF, that improves innate olfactory responses.

In 1929 Hans Berger discovered the alpha oscillations: prominent, ongoing oscillations around 10 Hz in the electroencephalogram of the human brain. These alpha oscillations are among the most widely studied brain signals, related to cognitive phenomena such as attention, memory and consciousness. However, the mechanisms by which alpha oscillations affect human cognition await demonstration. Here, we suggest the honey bee brain as an experimentally more accessible model system for investigating the functional role of alpha oscillations. We found a prominent spontaneous oscillation around 18 Hz that is reduced in amplitude upon olfactory stimulation. Similar to alpha oscillations in primates, the phase of this oscillation biased both timing of neuronal spikes and amplitude of high-frequency gamma activity (40-450 Hz). These results suggest a common role of alpha oscillations across phyla and provide an unprecedented new venue for causal studies on the relationship between neuronal spikes, brain oscillations and cognition.

Nasal respiration influences brain dynamics by phase-entraining neural oscillations at the same frequency as the breathing rate and by phase-modulating the activity of faster gamma rhythms. Despite being widely reported, we still do not understand the functional roles of respiration-entrained oscillations. A common hypothesis is that these rhythms aid long-range communication and provide a privileged window for synchronization. Here we tested this hypothesis by analyzing electrocorticographic (ECoG) recordings in mice, rats, and cats during the different sleep-wake states. We found that the respiration phase modulates the amplitude of cortical gamma oscillations in the three species, although the modulated gamma frequency bands differed with faster oscillations (90-130 Hz) in mice, intermediate frequencies (60-100 Hz) in rats, and slower activity (30-60 Hz) in cats. In addition, our results also show that respiration modulates olfactory bulb-frontal cortex synchronization in the gamma range, in which each breathing cycle evokes (following a delay) a transient time window of increased gamma synchrony. Long-range gamma synchrony modulation occurs during quiet and active wake states but decreases during sleep. Thus, our results suggest that respiration-entrained brain rhythms orchestrate communication in awake mammals.

Parkinson's disease (PD)-associated cognitive decline is heralded by olfactory dysfunction, but the network mechanisms bridging sensory and cognitive impairments remain poorly defined. Combining chronic multisite electrophysiology, behavioral tracking, and machine learning in PD models, a hierarchical disintegration of oscillatory dynamics across the olfactory network that mechanistically drives disease progression is uncovered. Early-stage PD mice are identified to show attenuated odor discrimination, accompanied by hyperexcitability of mitral/tufted (M/T) cells. Causally linking these deficits, aberrant gamma oscillation in the cross-olfactory network is identified as a causal factor underlying olfactory deficits. Notably, cognitive impairment emerged at later stages, correlating with abnormal theta oscillations in the cross-olfactory network. Pharmacological modulation of the olfactory bulb (OB)-lateral entorhinal cortex (LEC) pathway ameliorated cognitive deficits and restored cross-network theta oscillation. Collectively, the findings establish cross-olfactory network oscillations as dual diagnostic and therapeutic targets for PD cognitive impairment, providing a mechanism-guided framework for early intervention.

No abstract

The mammalian olfactory bulb (OB) generates gamma (40-100 Hz) and beta (15-30 Hz) local field potential (LFP) oscillations. Gamma oscillations arise at the peak of inhalation supported by dendrodendritic interactions between glutamatergic mitral cells (MCs) and GABAergic granule cells (GCs). Beta oscillations are induced by odorants in learning or odor sensitization paradigms, but their mechanism and function are still poorly understood. When centrifugal OB inputs are blocked, beta oscillations disappear, but gamma oscillations persist. Centrifugal inputs target primarily GABAergic interneurons in the GC layer (GCL) and regulate GC excitability, suggesting a causal link between beta oscillations and GC excitability. Our previous modeling work predicted that convergence of excitatory/inhibitory inputs onto MCs and centrifugal inputs onto GCs increase GC excitability sufficiently to produce beta oscillations primarily through voltage dependent calcium channel-mediated GABA release, independently of NMDA channels. We test some of the predictions of this model by examining the influence of NMDA and muscarinic acetylcholine (ACh) receptors, which affect GC excitability in different ways, on beta oscillations. A few minutes after intrabulbar infusion, scopolamine (muscarinic antagonist) suppressed odor-evoked beta in response to a strong stimulus but increased beta power in response to a weak stimulus, as predicted by our model. Pyriform cortex (PC) beta power was unchanged. Oxotremorine (muscarinic agonist) suppressed all oscillations, likely from overinhibition. APV, an NMDA receptor antagonist, suppressed gamma oscillations selectively (in OB and PC), lending support to the model's prediction that beta oscillations can be supported independently of NMDA receptors. NEW & NOTEWORTHY Olfactory bulb local field potential beta oscillations appear to be gated by GABAergic granule cell excitability. Reducing excitability with scopolamine reduces beta induced by strong odors but increases beta induced by weak odors. Beta oscillations rely on the same synapse as gamma oscillations but, unlike gamma, can persist in the absence of NMDA receptor activation. Pyriform cortex beta oscillations maintain power when olfactory bulb beta power is low, and the system maintains beta band coherence.

Before the deposition of amyloid-beta plaques and the onset of learning memory deficits, patients with Alzheimer's disease (AD) experience olfactory dysfunction, typified by a reduced ability to detect, discriminate, and identify odors. Rodent models of AD, such as the Tg2576 and APP/PS1 mice, also display impaired olfaction, accompanied by aberrant in vivo or in vitro gamma rhythms in the olfactory pathway. However, the mechanistic relationships between the electrophysiological, biochemical and behavioral phenomena remain unclear. To address the above issues in AD models, we conducted in vivo measurement of local field potential (LFP) with a combination of in vitro electro-olfactogram (EOG), whole-cell patch and field recordings to evaluate oscillatory and synaptic function and pharmacological regulation in the olfactory pathway, particularly in the olfactory bulb (OB). Levels of protein involved in excitation and inhibition of the OB were investigated by western blotting and fluorescence staining, while behavioral studies assessed olfaction and memory function. LFP measurements demonstrated an increase in gamma oscillations in the OB accompanied by altered olfactory behavior in both APP/PS1 and 3xTg mice at 3-5 months old, i.e. an age before the onset of plaque formation. Fewer olfactory sensory neurons (OSNs) and a reduced EOG contributed to a decrease in the excitatory responses of M/T cells, suggesting a decreased ability of M/T cells to trigger interneuron GABA release indicated by altered paired-pulse ratio (PPR), a presynaptic parameter. Postsynaptically, there was a compensatory increase in levels of GABA This study suggests that the concomitant dysfunction of both olfactory behavior and gamma oscillations have important implications for early AD diagnosis: in particular, awareness of aberrant GABAergic signaling mechanisms might both aid diagnosis and suggest therapeutic strategies for olfactory damage in AD.

Oscillations in neuronal population activity, or the synchronous neuronal spiking that underlies them, are thought to play a functional role in sensory processing in the CNS. In the olfactory system, stimulus-induced oscillations are observed both in central processing areas and in the peripheral receptor epithelium. To examine the relationship between these peripheral and central oscillations, we recorded local field potentials simultaneously from the olfactory epithelium and olfactory bulb in tiger salamanders (Ambystoma tigrinum). Stimulus-induced oscillations recorded at these two sites were matched in frequency and slowed concurrently over the time course of the response, suggesting that the oscillations share a common source or are modulated together. Both the power and duration of oscillations increased over a range of amyl acetate concentrations from 2.5 x 10(-2) to 1 x 10(-1) dilution of saturated vapor, but peak frequency was not affected. The frequency of the oscillation did vary with different odorant compounds in both olfactory epithelium and bulb (OE and OB): amyl acetate, ethyl fenchol and d-carvone elicited oscillations of significantly different frequencies, and there was no difference in OE and OB oscillation frequencies. No change in the power or frequency of OE oscillations was observed after sectioning the olfactory nerve, indicating that the OE oscillations have a peripheral source. Finally, application of 1.0 and 10 microM tetrodotoxin to the epithelium blocked OE oscillations in a dose-dependent and reversible manner, suggesting that peripheral olfactory oscillations are related to receptor neuron spiking.

Sensory perception depends on interactions between external inputs transduced by peripheral sensory organs and internal network dynamics generated by central neuronal circuits. In the sensory cortex, desynchronized network states associate with high signal-to-noise ratio stimulus-evoked responses and heightened perception. Cannabinoid-type-1-receptors (CB1Rs) - which influence network coordination in the hippocampus - are present in anterior piriform cortex (aPC), a sensory paleocortex supporting olfactory perception. Yet, how CB1Rs shape aPC network activity and affect odor perception is unknown. Using pharmacological manipulations coupled with multi-electrode recordings or fiber photometry in the aPC of freely moving male mice, we show that systemic CB1R blockade as well as local drug infusion increases the amplitude of gamma oscillations in aPC, while simultaneously reducing the occurrence of synchronized population events involving aPC excitatory neurons. In animals exposed to odor sources, blockade of CB1Rs reduces correlation among aPC excitatory units and lowers behavioral olfactory detection thresholds. These results suggest that endogenous endocannabinoid signaling promotes synchronized population events and dampen gamma oscillations in the aPC which results in a reduced sensitivity to external sensory inputs.

Studying the ability of the brain to recognize different odors is of great significance in the assessment and diagnosis of olfactory dysfunction. The wavelet energy moment (WEM) was proposed as a feature of olfactory electroencephalogram (EEG) signal and used for odor classification. Firstly, the olfactory evoked EEG data of 13 odors were collected by an experiment. Secondly, the WEM was extracted from olfactory evoked EEG data as the signal feature, and the power spectrum density (PSD), approximate entropy, sample entropy and wavelet entropy were used as the contrast features. Finally, 研究大脑对不同气味的识别能力在嗅觉功能障碍评估和诊断等方面具有重要意义。本文提出将小波能量矩（WEM）作为嗅觉诱发脑电图（EEG）信号特征并用于气味分类。首先，通过试验采集 13 种气味的嗅觉诱发 EEG 数据；其次，从嗅觉诱发 EEG 数据中提取 WEM 作为信号特征，并将功率谱密度（PSD）、近似熵、样本熵及小波熵作为对比特征；最后，利用

Each down stroke of an insect's wings accelerates axial airflow over the antennae. Modeling studies suggest that this can greatly enhance penetration of air and air-born odorants through the antennal sensilla thereby periodically increasing odorant-receptor interactions. Do these periodic changes result in entrainment of neural responses in the antenna and antennal lobe (AL)? Does this entrainment affect olfactory acuity? To address these questions, we monitored antennal and AL responses in the moth Manduca sexta while odorants were pulsed at frequencies from 10-72 Hz, encompassing the natural wingbeat frequency. Power spectral density (PSD) analysis was used to identify entrainment of neural activity. Statistical analysis of PSDs indicates that the antennal nerve tracked pulsed odor up to 30 Hz. Furthermore, at least 50% of AL local field potentials (LFPs) and between 7-25% of unitary spiking responses also tracked pulsed odor up to 30 Hz in a frequency-locked manner. Application of bicuculline (200 muM) abolished pulse tracking in both LFP and unitary responses suggesting that GABA(A) receptor activation is necessary for pulse tracking within the AL. Finally, psychophysical measures of odor detection establish that detection thresholds are lowered when odor is pulsed at 20 Hz. These results suggest that AL networks can respond to the oscillatory dynamics of stimuli such as those imposed by the wing beat in a manner analogous to mammalian sniffing.

A core function of the olfactory system is to determine the valence of odors. In humans, central processing of odor valence perception has been shown to take form already within the olfactory bulb (OB), but the neural mechanisms by which this important information is communicated to, and from, the olfactory cortex (piriform cortex, PC) are not known. To assess communication between the 2 nodes, we simultaneously measured odor-dependent neural activity in the OB and PC from human participants while obtaining trial-by-trial valence ratings. By doing so, we could determine when subjective valence information was communicated, what kind of information was transferred, and how the information was transferred (i.e., in which frequency band). Support vector machine (SVM) learning was used on the coherence spectrum and frequency-resolved Granger causality to identify valence-dependent differences in functional and effective connectivity between the OB and PC. We found that the OB communicates subjective odor valence to the PC in the gamma band shortly after odor onset, while the PC subsequently feeds broader valence-related information back to the OB in the beta band. Decoding accuracy was better for negative than positive valence, suggesting a focus on negative valence. Critically, we replicated these findings in an independent data set using additional odors across a larger perceived valence range. Combined, these results demonstrate that the OB and PC communicate levels of subjective odor pleasantness across multiple frequencies, at specific time points, in a direction-dependent pattern in accordance with a two-stage model of odor processing.