Predictability of depression severity based on posterior alpha oscillations

Applications of Alpha Neurofeedback Processes for Enhanced Mental Manipulation of Unfamiliar Molecular and Spatial Structures

This study explores a novel approach to enhancing cognitive proficiency by targeting neural mechanisms that facilitate science and math learning, especially mental rotation. The study specifically examines the relationship between upper alpha intensity and mental rotation skills. Although prior neurofeedback research for increasing upper alpha highlights this correlation, mostly with familiar objects, novel chemistry and math learning prompts envisioning unfamiliar objects which question the persistence of this correlation. This study revisits the upper alpha and mental rotation relationship in the context of unfamiliar objects with a single neurofeedback session and examines the efficiency of manual and automatic neurofeedback protocols. Results will provide a basis for integrating neurofeedback protocols into learning applications for enhanced learning. Our study encompassed three cohorts: Group 1 experienced an automatic neurofeedback protocol, Group 2 received a manual neurofeedback protocol, and the control group had no neurofeedback intervention. The experimental phases involved EEG measurement of individual upper alpha (frequency of maximal power + 2 Hz) intensity, mental rotation tasks featuring geometric and unfamiliar molecular stimuli, one neurofeedback session for applicable groups, post-treatment upper alpha level assessments, and a mental rotation retest. The neurofeedback groups exhibited increased levels of upper alpha power, which was correlated with improved response time in mental rotation, regardless of stimulus type, compared to the control group. Both neurofeedback protocols achieved comparable results. This study advocates integrating neurofeedback into learning software for optimal learning experiences, highlighting a single session's efficacy and the substantial neurofeedback protocol's impact in enhancing upper alpha oscillations.

Electrophysiological predictors of early response to antidepressants in major depressive disorder

BACKGROUND

Psychomotor retardation (PMR) is a core feature of major depressive disorder (MDD), which is characterized by abnormalities in motor control and cognitive processes. PMR in MDD can predict a poor antidepressant response, suggesting that PMR may serve as a marker of the antidepressant response. However, the neuropathological relationship between treatment outcomes and PMR remains uncertain. Thus, this study examined electrophysiological biomarkers associated with poor antidepressant response in MDD.

METHODS

A total of 142 subjects were enrolled in this study, including 49 healthy controls (HCs) and 93 MDD patients. All participants performed a simple right-hand visuomotor task during magnetoencephalography (MEG) scanning. Patients who exhibited at least a 50 % reduction in disorder severity at the endpoint (>2 weeks) were considered to be responders. Motor-related beta desynchronization (MRBD) and inter- and intra-hemispheric functional connectivity were measured in the bilateral motor network.

RESULTS

An increased MRBD and decreased inter- and intra-hemispheric functional connectivity in the motor network during movement were observed in non-responders, relative to responders and HCs. This dysregulation predicted the potential antidepressant response.

CONCLUSION

Abnormal local activity and functional connectivity in the motor network indicate poor psychomotor function, which might cause insensitivity to antidepressant treatment. This could be regarded as a potential neural mechanism for the prediction of a patient's treatment response.

Alpha oscillation mediates the interaction between suicide risk and symptom severity in Major Depressive Disorder

Objective

The aim of our study was to explore the relationship between changes in neural oscillatory power in the EEG, the severity of depressive-anxiety symptoms, and the risk of suicide in MDD.

Methods

350 MDD patients' demographic and clinical data were collected, and their depressive and anxious symptoms were evaluated using HDRS-17 and HAMA-14, along with a suicide risk assessment using the Nurses' Global Assessment of Suicide Risk (NGASR). EEG data were captured, processed, and analyzed to study brain activity patterns related to MDD. The participants were divided based on suicide risk levels, and statistical analyses, including chi-square, t-tests, Pearson's correlations were used to explore the associations between brain activity, symptom severity, and suicide risk. Closely related variables were identified and ultimately the optimal model was screened using stepwise regression analysis with a forward strategy, and mediation effects were further used to determine the possible interactions between the variables in the regression model.

Results

The regression model showed a significant effect of HDRS-17 and alpha power of Medial Occipital Cortex (MOC) on suicide risk, with elevated HDRS-17 increasing suicide risk and elevated alpha power decreasing suicide risk. Mediation effect analyses showed that MOC alpha power partially mediated the effect of depression level on suicide risk, and that an increase in depression severity may lead to a decrease in MOC alpha power, while a decrease in MOC alpha power may lead to an increase in suicide risk.

Conclusion

The severity of depression directly increases suicide risk, whereas higher alpha power in the MOC serves as a protective factor, reducing this risk. Notably, MOC alpha power not only directly impacts suicide risk but also mediates the effects of both depression severity and anxiety levels on this risk.

Limitations

The relatively small sample size of this study may limit the representativeness of the overall MDD patient population and the detailed analysis of different subgroups. This study did not delve into the relationship between the severity of cognitive symptoms in MDD patients and suicide risk.

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB)

BACKGROUND

The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear.

METHODS

In this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB).

RESULTS

We found that the MDD group showed significantly higher power in the high alpha range (10.5-11.5 Hz) and lower power in the low alpha range (7-8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone.

CONCLUSIONS

Our results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder

Background. Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method. The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction.

Analysis of EEG features and study of automatic classification in first-episode and drug-naïve patients with major depressive disorder

BACKGROUND

Major depressive disorder (MDD) has a high incidence and an unknown mechanism. There are no objective and sensitive indicators for clinical diagnosis.

OBJECTIVE

This study explored specific electrophysiological indicators and their role in the clinical diagnosis of MDD using machine learning.

METHODS

Forty first-episode and drug-naïve patients with MDD and forty healthy controls (HCs) were recruited. EEG data were collected from all subjects in the resting state with eyes closed for 10 min. The severity of MDD was assessed by the Hamilton Depression Rating Scale (HAMD-17). Machine learning analysis was used to identify the patients with MDD.

RESULTS

Compared to the HC group, the relative power of the low delta and theta bands was significantly higher in the right occipital region, and the relative power of the alpha band in the entire posterior occipital region was significantly lower in the MDD group. In the MDD group, the alpha band scalp functional connectivity was overall lower, while the scalp functional connectivity in the gamma band was significantly higher than that in the HC group. In the feature set of the relative power of the ROI in each band, the highest accuracy of 88.2% was achieved using the KNN classifier while using PCA feature selection. In the explanatory model using SHAP values, the top-ranking influence feature is the relative power of the alpha band in the left parietal region.

CONCLUSIONS

Our findings reveal that the abnormal EEG neural oscillations may reflect an imbalance of excitation, inhibition and hyperactivity in the cerebral cortex in first-episode and drug-naïve patients with MDD. The relative power of the alpha band in the left parietal region is expected to be an objective electrophysiological indicator of MDD.

Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder

BACKGROUND

Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response.

METHODS

Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions.

RESULTS

After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969).

LIMITATIONS

Relatively small sample size and not a double-blind design.

CONCLUSIONS

The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.

Transcranial alternating current stimulation for the treatment of major depressive disorder: from basic mechanisms toward clinical applications

Non-pharmacological treatment is essential for patients with major depressive disorder (MDD) that is medication resistant or who are unable to take medications. Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation method that manipulates neural oscillations. In recent years, tACS has attracted substantial attention for its potential as an MDD treatment. This review summarizes the latest advances in tACS treatment for MDD and outlines future directions for promoting its clinical application. We first introduce the neurophysiological mechanism of tACS and its novel developments. In particular, two well-validated tACS techniques have high application potential: high-definition tACS targeting local brain oscillations and bifocal tACS modulating interarea functional connectivity. Accordingly, we summarize the underlying mechanisms of tACS modulation for MDD. We sort out the local oscillation abnormalities within the reward network and the interarea oscillatory synchronizations among multiple MDD-related networks in MDD patients, which provide potential modulation targets of tACS interventions. Furthermore, we review the latest clinical studies on tACS treatment for MDD, which were based on different modulation mechanisms and reported alleviations in MDD symptoms. Finally, we discuss the main challenges of current tACS treatments for MDD and outline future directions to improve intervention target selection, tACS implementation, and clinical validations.

Influence of chronic stress on network states governing valence processing: Potential relevance to the risk for psychiatric illnesses

Stress is a major risk factor for psychiatric illnesses and understanding the mechanisms through which stress disrupts behavioral states is imperative to understanding the underlying pathophysiology of mood disorders. Both chronic stress and early life stress alter valence processing, the process of assigning value to sensory inputs and experiences (positive or negative), which determines subsequent behavior and is essential for emotional processing and ultimately survival. Stress disrupts valence processing in both humans and preclinical models, favoring negative valence processing and impairing positive valence processing. Valence assignment involves neural computations performed in emotional processing hubs, including the amygdala, prefrontal cortex, and ventral hippocampus, which can be influenced by neuroendocrine mediators. Oscillations within and between these regions are critical for the neural computations necessary to perform valence processing functions. Major advances in the field have demonstrated a role for oscillatory states in valence processing under physiological conditions and emerging studies are exploring how these network states are altered under pathophysiological conditions and impacted by neuroendocrine factors. The current review highlights what is currently known regarding the impact of stress and the role of neuroendocrine mediators on network states and valence processing. Further, we propose a model in which chronic stress alters information routing through emotional processing hubs, resulting in a facilitation of negative valence processing and a suppression of positive valence processing.

Attenuated post-movement beta rebound reflects psychomotor alterations in major depressive disorder during a simple visuomotor task: a MEG study

BACKGROUND

Psychomotor alterations are a common symptom in patients with major depressive disorder (MDD). The primary motor cortex (M1) plays a vital role in the mechanism of psychomotor alterations. Post-movement beta rebound (PMBR) in the sensorimotor cortex is abnormal in patients with motor abnormalities. However, the changes in M1 beta rebound in patients with MDD remain unclear. This study aimed to primarily explore the relationship between psychomotor alterations and PMBR in MDD.

METHODS

One hundred thirty-two subjects were enrolled in the study, comprising 65 healthy controls (HCs) and 67 MDD patients. All participants performed a simple right-hand visuomotor task during MEG scanning. PMBR was measured in the left M1 at the source reconstruction level with the time-frequency analysis method. Retardation factor scores and neurocognitive test performance, including the Digit Symbol Substitution Test (DSST), the Making Test Part A (TMT-A), and the Verbal Fluency Test (VFT), were used to measure psychomotor functions. Pearson correlation analyses were used to assess relationships between PMBR and psychomotor alterations in MDD.

RESULTS

The MDD group showed worse neurocognitive performance than the HC group in all three neurocognitive tests. The PMBR was diminished in patients with MDD compared to HCs. In a group of MDD patients, the reduced PMBR was negatively correlated with retardation factor scores. Further, there was a positive correlation between the PMBR and DSST scores. PMBR is negatively associated with the TMT-A scores.

CONCLUSION

Our findings suggested that the attenuated PMBR in M1 could illustrate the psychomotor disturbance in MDD, possibly contributing to clinical psychomotor symptoms and deficits of cognitive functions.

A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment

Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS) have been proven effective non-invasive treatments for patients with drug-resistant major depressive disorder (MDD). However, some depressed patients do not respond to these treatments. Therefore, the investigation of reliable and valid brain oscillations as potential indices for facilitating the precision of diagnosis and treatment protocols has become a critical issue. The current review focuses on brain oscillations that, mostly based on EEG power analysis and connectivity, distinguish between MDD and controls, responders and non-responders, and potential depression severity indices, prognostic indicators, and potential biomarkers for rTMS or iTBS treatment. The possible roles of each biomarker and the potential reasons for heterogeneous results are discussed, and the directions of future studies are proposed.

Spontaneous beta power, motor-related beta power and cortical thickness in major depressive disorder with psychomotor disturbance

INTRODUCTION

The psychomotor disturbance is a common symptom in patients with major depressive disorder (MDD). The neurological mechanisms of psychomotor disturbance are intricate, involving alterations in the structure and function of motor-related regions. However, the relationship among changes in the spontaneous activity, motor-related activity, local cortical thickness, and psychomotor function remains unclear.

METHOD

A total of 140 patients with MDD and 68 healthy controls performed a simple right-hand visuomotor task during magnetoencephalography (MEG) scanning. All patients were divided into two groups according to the presence of psychomotor slowing. Spontaneous beta power, movement-related beta desynchronization (MRBD), absolute beta power during movement and cortical characteristics in the bilateral primary motor cortex were compared using general linear models with the group as a fixed effect and age as a covariate. Finally, the moderated mediation model was tested to examine the relationship between brain metrics with group differences and psychomotor performance.

RESULTS

The patients with psychomotor slowing showed higher spontaneous beta power, movement-related beta desynchronization and absolute beta power during movement than patients without psychomotor slowing. Compared with the other two groups, significant decreases were found in cortical thickness of the left primary motor cortex in patients with psychomotor slowing. Our moderated mediation model showed that the increased spontaneous beta power indirectly affected impaired psychomotor performance by abnormal MRBD, and the indirect effects were moderated by cortical thickness.

CONCLUSION

These results suggest that patients with MDD have aberrant cortical beta activity at rest and during movement, combined with abnormal cortical thickness, contributing to the psychomotor disturbance observed in this patient population.

Convergent and divergent cognitive impairment of unipolar and bipolar depression: A magnetoencephalography resting-state study

BACKGROUND

Unipolar depression (UD) and bipolar depression (BD) showed convergent and divergent cognitive impairments. Neural oscillations are linked to the foundational cognitive processes. We aimed to investigate the underpinning spectral neuronal power patterns by magnetoencephalography (MEG), which combinates high spatial and temporal resolution. We hypothesized that patients with UD and BD exhibit common and distinct patterns, which may contribute to their cognitive impairments.

METHODS

Group cognitive tests were performed. Eyes closed resting-state MEG data were collected from 61 UD, 55 BD, and 52 healthy controls (HC). Nonparametric cluster-based permutation tests were performed to deal with the multiple comparison problem on channel-frequency MEG data. Correlation analysis of cognitive dysfunction scores and MEG oscillation were conducted by Spearman or partial correlation analysis.

RESULTS

Wisconsin Card Sorting Test showed similar cognitive impairment in patients with UD and BD. Moreover, patients with BD exhibited extensive cognitive deficits in verbal executive functions and visuospatial processing. Compare to HC, both patients with UD and BD showed increased frontal-central beta power while high gamma power was decreased in UD groups during the resting-state. The significant correlations between cognitive function and average beta power were observed.

CONCLUSIONS

Patients with BD had more cognitive impairments on different dimensions than those with UD, involving disrupted beta power modulations. Our investigation provides a better understanding of the neuroelectrophysiological process underlying cognitive impairments in patients with UD and BD.

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.

Bias of Attentional Oscillations in Individuals with Subthreshold Depression: Evidence from a Pre-Cueing Facial Expression Judgment Task

Background: Study results regarding attentional bias in depressed individuals are inconsistent. Recent studies have found that attention is a discrete process, alternating between periods of either enhanced or diminished attention sensitivity. Whether a visual target can be detected depends on when it occurs relative to these oscillation rhythms. We infer that the inconsistency of attentional bias may be related to the abnormality of attentional oscillations in depressed individuals. Methods: A pre-cueing attentional task was used. We set 48 levels of stimulus onset asynchrony (SOA) between cues and targets and measured the response time (RT) of participants, as well as their EEG signals. Results: The RTs showed patterns of behavioral oscillations. Repeated-measure ANOVA indicated that subthreshold depressed participants had significantly higher RTs for negative expressions than for neutral but significantly lower RTs for positive than for neutral. The frequency analysis indicated that the RT oscillational frequency of subthreshold depressed participants to negative/positive expressions was different from that to neutral. The EEG time-frequency analysis showed that when faced with negative expressions, the intensity of the neural alpha oscillatory power of subthreshold depressed participants was significantly lower than that of normal controls. When faced with positive expressions, the intensity of neural alpha oscillatory power was significantly higher than that of normal controls. Conclusion: Compared to normal persons, subthreshold depressed individuals may have biases in both the amplitude and frequency of attentional oscillations. These attentional biases correspond to the intensity of their neural alpha wave rhythms.

Dissociable Roles of Theta and Alpha in Sub-Second and Supra-Second Time Reproduction: An Investigation of their Links to Depression and Anxiety

A growing collection of observations has demonstrated the presence of multiple neural oscillations participating in human temporal cognition and psychiatric pathologies such as depression and anxiety. However, there remains a gap in the literature regarding the specific roles of these neural oscillations during interval timing, and how these oscillatory activities might vary with the different levels of mental health. The current study examined the participation of the frontal midline theta and occipital alpha oscillations, both of which are prevalent cortical oscillatory markers frequently reported in working memory and time perception paradigms. Participants performed a time reproduction task in the sub- (400, 600, 800 ms) and supra-second timescales (1600, 1800, 2000 ms) while undergoing scalp EEG recordings. Anxiety and depression levels were measured via self-report mental health inventories. Time–frequency analysis of scalp EEG revealed that both frontal midline and occipital alpha oscillations were engaged during the encoding of the durations. Furthermore, we observed that the correlational relationship between frontal midline theta power and the reproduction performance in the sub-second range was modulated by state anxiety. In contrast, the correlational relationship between occipital alpha and the reproduction performance of supra-second intervals was modulated by depression and trait anxiety. The results offer insights on how alpha and theta oscillations differentially play a role in interval timing and how mental health further differentially relates these neural oscillations to sub- and supra-second timescales.

Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome

Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.

An insight into diagnosis of depression using machine learning techniques: a systematic review

BACKGROUND

In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis.

METHODS

This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis.

RESULTS

A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test.

CONCLUSION

The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.

Resting posterior alpha power and adolescent major depressive disorder

For several decades, resting electroencephalogram (EEG) alpha oscillations have been used to characterize neurophysiological alterations related to major depressive disorder. Prior research has generally focused on frontal alpha power and asymmetry despite resting alpha being maximal over posterior electrode sites. Research in depressed adults has shown evidence of hemispheric asymmetry for posterior alpha power, however, the resting posterior alpha-depression link among adolescents remains unclear. To clarify the role of posterior alpha among depressed adolescents, the current study acquired eyes-closed 128-channel resting EEG data from 13 to 18 year-old depressed (n = 31) and healthy (n = 35) female adolescents. Results indicated a significant group by hemisphere interaction, as depressed adolescents exhibited significantly larger posterior alpha (i.e., lower brain activity) over the right versus left hemisphere, whereas healthy adolescents showed no hemispheric differences. Relatively greater alpha over the right versus left hemisphere correlated with depression symptoms, anhedonia symptoms, rumination, and self-criticism. Further, depressed adolescents had reduced overall posterior alpha compared to healthy youth; though, no associations with symptoms and related traits emerged. Resting posterior alpha may be a promising neurophysiological index of adolescent depression, and more broadly, may relate to risk factors characterized by enhanced perseveration.

α and θ oscillations in the subthalamic nucleus are potential biomarkers for Parkinson's disease with depressive symptoms

INTRODUCTION

Abnormal α oscillations in the bed nucleus of stria terminalis and subgenual cingulate of patients with depression correlate with symptom severity. Some Parkinson's disease (PD) patients also have abnormal θ-α oscillations in the subthalamic nucleus (STN). However, the relationship between abnormal θ-α oscillations and depressive symptoms in PD patients has not been determined. This study explored the correlation between α and θ oscillations of the STN and depressive symptoms in PD patients.

METHODS

We conducted a retrospective case-control study on 36 PD patients with (dPD group) or without depressive symptoms (nPD group), analyzing the difference in the average power spectral density (PSD) of α and θ oscillations of the local field potential (LFP) recorded in the STN during deep brain stimulation (DBS), and their correlation with the Hamilton depression rating scale (HAMD) of PD patients during the same period.

RESULTS

The dPD group had a higher PSD of α oscillations and a lower PSD of θ oscillations in the left ventral STN. The PSD of α oscillations of the left ventral STN were positively correlated with the severity of depressive symptoms, whereas the PSD of θ oscillations of this location was negatively correlated with severity of depressive symptoms. The PSD of α and θ oscillations did not correlate with motor symptoms, sleep quality, or quality of life score.

CONCLUSION

Abnormal α and θ oscillations of the left ventral STN could be used as biomarkers of PD with depressive symptoms, which might guide STN-DBS treatment.

Transcranial Auricular Vagus Nerve Stimulation (taVNS) and Ear-EEG: Potential for Closed-Loop Portable Non-invasive Brain Stimulation

No matter how hard we concentrate, our attention fluctuates – a fact that greatly affects our success in completing a current task. Here, we review work from two methods that, in a closed-loop manner, have the potential to ameliorate these fluctuations. Ear-EEG can measure electric brain activity from areas in or around the ear, using small and thus portable hardware. It has been shown to capture the state of attention with high temporal resolution. Transcutaneous auricular vagus nerve stimulation (taVNS) comes with the same advantages (small and light) and critically current research suggests that it is possible to influence ongoing brain activity that has been linked to attention. Following the review of current work on ear-EEG and taVNS we suggest that a combination of the two methods in a closed-loop system could serve as a potential application to modulate attention.

No relationship between frontal alpha asymmetry and depressive disorders in a multiverse analysis of five studies

For decades, the frontal alpha asymmetry (FAA) - a disproportion in EEG alpha oscillations power between right and left frontal channels - has been one of the most popular measures of depressive disorders (DD) in electrophysiology studies. Patients with DD often manifest a left-sided FAA: relatively higher alpha power in the left versus right frontal lobe. Recently, however, multiple studies failed to confirm this effect, questioning its reproducibility. Our purpose is to thoroughly test the validity of FAA in depression by conducting a multiverse analysis - running many related analyses and testing the sensitivity of the effect to changes in the analytical approach - on data from five independent studies. Only 13 of the 270 analyses revealed significant results. We conclude the paper by discussing theoretical assumptions underlying the FAA and suggest a list of guidelines for improving and expanding the EEG data analysis in future FAA studies.

Frontolimbic alpha activity tracks intentional rest BCI control improvement through mindfulness meditation

Brain-computer interfaces (BCIs) are capable of translating human intentions into signals controlling an external device to assist patients with severe neuromuscular disorders. Prior work has demonstrated that participants with mindfulness meditation experience evince improved BCI performance, but the underlying neural mechanisms remain unclear. Here, we conducted a large-scale longitudinal intervention study by training participants in mindfulness-based stress reduction (MBSR; a standardized mind-body awareness training intervention), and investigated whether and how short-term MBSR affected sensorimotor rhythm (SMR)-based BCI performance. We hypothesize that MBSR training improves BCI performance by reducing mind wandering and enhancing self-awareness during the intentional rest BCI control, which would mainly be reflected by modulations of default-mode network and limbic network activity. We found that MBSR training significantly improved BCI performance compared to controls and these behavioral enhancements were accompanied by increased frontolimbic alpha activity (9-15 Hz) and decreased alpha connectivity among limbic network, frontoparietal network, and default-mode network. Furthermore, the modulations of frontolimbic alpha activity were positively correlated with the duration of meditation experience and the extent of BCI performance improvement. Overall, these data suggest that mindfulness allows participant to reach a state where they can modulate frontolimbic alpha power and improve BCI performance for SMR-based BCI control.

Use of machine learning approach to predict depression in the elderly in China: A longitudinal study

BACKGROUND

Early detection of potential depression among elderly people is conducive for timely preventive intervention and clinical care to improve quality of life. Therefore, depression prediction considering sequential progression patterns in elderly needs to be further explored.

METHODS

We selected 1,538 elderly people from Chinese Longitudinal Healthy Longevity Study (CLHLS) wave 3-7 survey. Long short-term memory (LSTM) and six machine learning (ML) models were used to predict different depression risk factors and the depression risks in the elderly population in the next two years. Receiver operating curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction accuracy of the reference model and ML models.

RESULTS

The area under the ROC curve (AUC) values of logistic regression with lasso regularisation (AUC=0.629, p-value=0.020) was the highest among ML models. DCA results showed that the net benefit of six ML models was similar (threshold: 0.00-0.10), the net benefit of lasso regression was the largest (threshold: 0.10-0.17 and 0.22-0.25), and the net benefit of DNN was the largest (threshold: 0.17-0.22 and 0.25-0.40). In two ML models, activities of daily living (ADL)/ instrumental ADL (IADL), self-rated health, marital status, arthritis, and number of cohabiting were the most important predictors for elderly with depression.

LIMITATIONS

The retrospective waves used in the LSTM model need to be further increased.

CONCLUSION

The decision support system based on the proposed LSTM+ML model may be very valuable for doctors, nurses and community medical providers for early diagnosis and intervention.

Increased functional connectivity within alpha and theta frequency bands in dysphoria: A resting-state EEG study

BACKGROUND

The understanding of neurophysiological correlates underlying the risk of developing depression may have a significant impact on its early and objective identification. Research has identified abnormal resting-state electroencephalography (EEG) power and functional connectivity patterns in major depression. However, the entity of dysfunctional EEG dynamics in dysphoria is yet unknown.

METHODS

32-channel EEG was recorded in 26 female individuals with dysphoria and in 38 age-matched, female healthy controls. EEG power spectra and alpha asymmetry in frontal and posterior channels were calculated in a 4-minute resting condition. An EEG functional connectivity analysis was conducted through phase locking values, particularly mean phase coherence.

RESULTS

While individuals with dysphoria did not differ from controls in EEG spectra and asymmetry, they exhibited dysfunctional brain connectivity. Particularly, in the theta band (4-8 Hz), participants with dysphoria showed increased connectivity between right frontal and central areas and right temporal and left occipital areas. Moreover, in the alpha band (8-12 Hz), dysphoria was associated with increased connectivity between right and left prefrontal cortex and between frontal and central-occipital areas bilaterally.

LIMITATIONS

All participants belonged to the female gender and were relatively young. Mean phase coherence did not allow to compute the causal and directional relation between brain areas.

CONCLUSIONS

An increased EEG functional connectivity in the theta and alpha bands characterizes dysphoria. These patterns may be associated with the excessive self-focus and ruminative thinking that typifies depressive symptoms. EEG connectivity patterns may represent a promising measure to identify individuals with a higher risk of developing depression.

Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences

Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

BACKGROUND

Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory-II scores).

METHODS

Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal.

RESULTS

Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band (r = 0.839; P < .0001, root mean square error score of 7.69).

CONCLUSION

We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.

Magnetoencephalography resting-state spectral fingerprints distinguish bipolar depression and unipolar depression

OBJECTIVES

In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level.

METHODS

Eyes closed resting-state magnetoencephalography (MEG) data were collected from 23 BD, 30 UD, and 31 healthy controls (HC). Individual power spectra were estimated by Fourier transform, and statistic spectral differences were assessed via a cluster permutation test. A support vector machine classifier was further applied to predict different mood disorder types based on discriminative oscillatory power.

RESULTS

Both BD and UD showed decreased frontal-central gamma/beta ratios comparing to HC, in which gamma power (30-75 Hz) was decreased in BD while beta power (14-30 Hz) was increased in UD vs HC. The support vector machine model obtained significant high classification accuracies distinguishing three groups based on mean gamma and beta power (BD: 79.9%, UD: 81.1%, HC: 76.3%, P < .01).

CONCLUSIONS

In combination with resting-state MEG data and machine learning technique, it is possible to make an individual and objective prediction for mode disorder types, which in turn has implications for diagnosis precision and treatment decision of mood disorder patients.

Spectral fingerprints of facial affect processing bias in major depression disorder

In major depressive disorder (MDD), processing of facial affect is thought to reflect a perceptual bias (toward negative emotion, away from positive emotion, and interpretation of neutral as emotional). However, it is unclear to what extent and which specific perceptual bias is represented in MDD at the behavior and neuronal level. The present report examined 48 medication naive MDD patients and 41 healthy controls (HCs) performing a facial affect judgment task while magnetoencephalography was recorded. MDD patients were characterized by overall slower response times and lower perceptual judgment accuracies. In comparison with HC, MDD patients exhibited less somatosensory beta activity (20–30 Hz) suppression, more visual gamma activity (40–80 Hz) modulation and somatosensory beta and visual gamma interaction deficit. Moreover, frontal gamma activity during positive facial expression judgment was found to be negatively correlated with depression severity. Present findings suggest that perceptual bias in MDD is associated with distinct spatio-spectral manifestations on the neural level, which potentially establishes aberrant pathways during facial emotion processing and contributes to MDD pathology.

Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

The cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using high-density EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p < 0.001) in patients than in controls. The global efficiency was significantly higher (p < 0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{R}}}^{{\bf{2}}}{\boldsymbol{=}}{\bf{0.59}}\,{\bf{and}}\,{\bf{p}}{\boldsymbol{=}}{\bf{1.52}}{\bf{e}}{\boldsymbol{ \mbox{-} }}{\bf{05}}$$\end{document}R2=0.59andp=1.52e‐05). The amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression.

Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning

OBJECTIVE

We aimed to develop a machine learning algorithm to screen for depression and assess severity based on data from wearable devices.

METHODS

We used a wearable device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, and ultraviolet light exposure. Depressed patients and healthy volunteers wore the device continuously for the study period. The modalities were compared hourly between patients and healthy volunteers. XGBoost was used to build machine learning models and 10-fold cross-validation was applied for the validation.

RESULTS

Forty-five depressed patients and 41 healthy controls participated, creating a combined 5,250 days' worth of data. Heart rate, steps, and sleep were significantly different between patients and healthy volunteers in some comparisons. Similar differences were also observed longitudinally when patients' symptoms improved. Based on seven days' data, the model identified symptomatic patients with 0.76 accuracy and predicted Hamilton Depression Rating Scale-17 scores with a 0.61 correlation coefficient. Skin temperature, sleep time-related features, and the correlation of those modalities were the most significant features in machine learning.

LIMITATIONS

The small number of subjects who participated in this study may have weakened the statistical significance of the study. There are differences in the demographic data among groups although we performed a correction for multiple comparisons. Validation in independent datasets was not performed, although 10-fold cross validation with the internal data was conducted.

CONCLUSION

The results indicated that utilizing wearable devices and machine learning may be useful in identifying depression as well as assessing severity.

Hyperactive frontolimbic and frontocentral resting-state gamma connectivity in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a system-level disorder affecting multiple functionally integrated cerebral networks. Nevertheless, their temporospatial organization and potential disturbance remain mostly unknown. The present report tested the hypothesis that deficient temporospatial network organization separates MDD and healthy controls (HC), and is linked to symptom severity of the disorder.

METHODS

Eyes-closed resting-state magnetoencephalographic (MEG) recordings were obtained from twenty-two MDD and twenty-two HC subjects. Beamforming source localization and functional connectivity analysis were applied to identify frequency-specific network interactions. Then, a novel virtual cortical resection approach was used to pinpoint putatively critical network controllers, accounting for aberrant cerebral connectivity patterns in MDD.

RESULTS

We found significantly elevated frontolimbic and frontocentral connectivity mediated by gamma (30-48 Hz) activity in MDD versus HC, and the right amygdala was the key differential network controller accounting for aberrant cerebral connectivity patterns in MDD. Furthermore, this frontolimbic and frontocentral gamma-band hyper-connectivity was positively correlated with depression severity.

LIMITATIONS

The overall sample size was small, and we found significant effects in the deep limbic regions with resting-state MEG, the reliability of which was difficult to corroborate further.

CONCLUSIONS

Overall, these findings support a notion that the right amygdala critically controls the exaggerated gamma-band frontolimbic and frontocentral connectivity in MDD during the resting-state condition, which potentially constitutes pre-established aberrant pathways during task processing and contributes to MDD pathology.

Resting-State Quantitative Electroencephalography Demonstrates Differential Connectivity in Adolescents with Major Depressive Disorder

Background: Biomarkers for psychiatric disorders in children and adolescents are urgently needed. This cross-sectional pilot study investigated quantitative electroencephalogram (qEEG), a promising intermediate biomarker, in pediatric patients with major depressive disorder (MDD) compared with healthy controls (HCs). We hypothesized that youth with MDD would have increased coherence (connectivity) and absolute alpha power in the frontal cortex compared with HC. Methods: qEEG was obtained in adolescents aged 14-17 years with MDD (n = 25) and age- and gender-matched HCs (n = 14). The primary outcome was overall coherence on qEEG in the four frequency bands (alpha, beta, theta, and delta). Other outcomes included frontal-only coherence, overall and frontal-only qEEG power, and clinician-rated measures of anhedonia and anxiety. Results: Average coherence in the theta band was significantly lower in MDD patients versus HCs, and also lower in frontal cortex among MDD patients. Seven node pairs were significantly different or trending toward significance between MDD and HC; all had lower coherence in MDD patients. Average frontal delta power was significantly higher in MDD versus HCs. Conclusions: Brain connectivity measured by qEEG differs significantly between adolescents with MDD and HCs. Compared with HCs, youth with MDD showed decreased connectivity, yet no differences in power in any frequency bands. In the frontal cortex, youth with MDD showed decreased resting connectivity in the alpha and theta frequency bands. Impaired development of a resting-state brain network (e.g., default mode network) in adolescents with MDD may represent an intermediate phenotype that can be assessed with qEEG.

State and trait neural correlates of the balance between work and nonwork roles

Difficulty managing the demands of work and nonwork roles (often referred to in terms of managing balance) can be detrimental to psychological wellbeing and contribute to occupational burnout. The current study investigated the neural correlates of perceived satisfaction with this balance using both trait and state EEG alpha measures. EEG was recorded from 14 participants in full time employment (12 females, aged 35.1 ± 10.1 years) during a resting state and performance of an auditory oddball task; e-mail and messaging alert sounds were used as target stimuli. It was predicted that dissatisfaction with the balance between work and nonwork roles would be associated with increased resting alpha power, consistent with studies of burnout, and diminished alpha response to oddball distractors, consistent with difficulty suppressing automatic responses to work-related stimuli. Significant correlations between self-reported measures of work/nonwork balance and both resting, and task-related alpha responses, supported our predictions. Furthermore, an exploratory partial correlation between work and nonwork balance and resting EEG, controlling for task-related alpha response, suggested that the three variables were interrelated. We propose that dissatisfaction with work/nonwork balance is associated with a state hypervigilance to work-related cues, and a trait neural marker of fatigue, both symptomatic of lowered cognitive capacity.

Resting state functional connectivity predictors of treatment response to electroconvulsive therapy in depression

There is increasing focus on use of resting-state functional connectivity (RSFC) analyses to subtype depression and to predict treatment response. To date, identification of RSFC patterns associated with response to electroconvulsive therapy (ECT) remain limited, and focused on interactions between dorsal prefrontal and regions of the limbic or default-mode networks. Deficits in visual processing are reported in depression, however, RSFC with or within the visual network have not been explored in recent models of depression. Here, we support prior studies showing in a sample of 18 patients with depression that connectivity between dorsal prefrontal and regions of the limbic and default-mode networks serves as a significant predictor. In addition, however, we demonstrate that including visual connectivity measures greatly increases predictive power of the RSFC algorithm (>80% accuracy of remission). These exploratory results encourage further investigation into visual dysfunction in depression, and use of RSFC algorithms incorporating the visual network in prediction of response to both ECT and transcranial magnetic stimulation (TMS), offering a new framework for the development of RSFC-guided TMS interventions in depression.

IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG.

Targeting Heterogeneous Findings in Neuronal Oscillations in Tinnitus: Analyzing MEG Novices and Mental Health Comorbidities

Tinnitus is a prevalent phenomenon and bothersome for people affected by it. Its occurrence and maintenance have a clear neuroscientific tie and one aspect are differences in the neuronal oscillatory pattern, especially in auditory cortical areas. As studies in this field come to different results, the aim of this study was to analyze a large number of participants to achieve more stable results. Furthermore, we expanded our analysis to two variables of potential influence, namely being a novice to neuroscientific measurements and the exclusion of psychological comorbidities. Oscillatory brain activity of 88 subjects (46 with a chronic tinnitus percept, 42 without) measured in resting state by MEG was investigated. In the analysis based on the whole group, in sensor space increased activity in the delta frequency band was found in tinnitus patients. Analyzing the subgroup of novices, a significant difference in the theta band emerged additionally to the delta band difference (sensor space). Localizing the origin of the activity, we found a difference in theta and gamma band for the auditory regions for the whole group and the same significant difference in the subgroup of novices. However, no differences in oscillatory activity were observed between tinnitus and control groups once subjects with mental health comorbidity were excluded. Against the background of previous studies, the study at hand underlines the fragility of the results in the field of neuronal cortical oscillations in tinnitus. It supports the body of research arguing for low frequency oscillations and gamma band activity as markers associated with tinnitus.

Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence

Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations.