Altered structure of dynamic electroencephalogram oscillatory pattern in major depression

In perspective of specific symptoms of major depressive disorder: Functional connectivity analysis of electroencephalography and potential biomarkers of treatment response

BACKGROUND

The symptom variability in major depressive disorder (MDD) complicates treatment assessment, necessitating a thorough understanding of MDD symptoms and potential biomarkers.

METHODS

In this prospective study, we enrolled 54 MDD patients and 39 controls. Over the course of weeks 1, 2, and 4 participants underwent evaluations, with electroencephalograms (EEG) recorded at baseline and week 1. Our investigation considered five previously identified syndromal factors derived from the 17-item Hamilton Depression Rating Scale (17-item HAMD) for assessing depression: core, insomnia, somatic anxiety, psychomotor-insight, and anorexia. We assessed treatment response and EEG characteristics across all syndromal factors and total scores, all of which are based on the 17-item HAMD. To analyze the topology of brain networks, we employed functional connectivity (FC) and a graph theory-based method across various frequency bands.

RESULTS

The healthy control group had notably higher values in delta band EEG FC compared to the MDD patient group. Similar distinctions were observed between the responder and non-responder patient groups. Further exploration of baseline FC values across distinct syndromal factors revealed significant variations among the core, psychomotor-insight, and anorexia subgroups when using a specific graph theory-based approach, focusing on global efficiency and average clustering coefficient.

LIMITATIONS

Different antidepressants were included in this study. Therefore, the results should be interpreted with caution.

CONCLUSIONS

Our findings suggest that delta band EEG FC holds promise as a valuable predictor of antidepressant efficacy. It demonstrates an ability to adapt to individual variations in depressive symptomatology, offering insights into personalized treatment for patients with depression.

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.

Harnessing Brainwave Entrainment: A Non-invasive Strategy To Alleviate Neurological Disorder Symptoms

From 1990-2019, the burden of neurological disorders varied considerably across countries and regions. Psychiatric disorders, often emerging in early to mid-adulthood, are linked to late-life neurodegenerative diseases like Alzheimer's disease and Parkinson's disease. Individuals with conditions such as Major Depressive Disorder, Anxiety Disorder, Schizophrenia, and Bipolar Disorder face up to four times higher risk of developing neurodegenerative disorders. Contrarily, 65 % of those with neurodegenerative conditions experience severe psychiatric symptoms during their illness. Further, the limitation of medical resources continues to make this burden a significant global and local challenge. Therefore, brainwave entrainment provides therapeutic avenues for improving the symptoms of diseases. Brainwaves are rhythmic oscillations produced either spontaneously or in response to stimuli. Key brainwave patterns include gamma, beta, alpha, theta, and delta waves, yet the underlying physiological mechanisms and the brain's ability to shift between these dynamic states remain areas for further exploration. In neurological disorders, brainwaves are often disrupted, a phenomenon termed "oscillopathy". However, distinguishing these impaired oscillations from the natural variability in brainwave activity across different regions and functional states poses significant challenges. Brainwave-mediated therapeutics represents a promising research field aimed at correcting dysfunctional oscillations. Herein, we discuss a range of non-invasive techniques such as non-invasive brain stimulation (NIBS), neurologic music therapy (NMT), gamma stimulation, and somatosensory interventions using light, sound, and visual stimuli. These approaches, with their minimal side effects and cost-effectiveness, offer potential therapeutic benefits. When integrated, they may not only help in delaying disease progression but also contribute to the development of innovative medical devices for neurological care.

Modulation of neural oscillations in escitalopram treatment: a Canadian biomarker integration network in depression study

Current pharmacological agents for depression have limited efficacy in achieving remission. Developing and validating new medications is challenging due to limited biological targets. This study aimed to link electrophysiological data and symptom improvement to better understand mechanisms underlying treatment response. Longitudinal changes in neural oscillations were assessed using resting-state electroencephalography (EEG) data from two Canadian Biomarker Integration Network in Depression studies, involving pharmacological and cognitive behavioral therapy (CBT) trials. Patients in the pharmacological trial received eight weeks of escitalopram, with treatment response defined as ≥ 50% decrease in Montgomery-Åsberg Depression Rating Scale (MADRS). Early (baseline to week 2) and late (baseline to week 8) changes in neural oscillation were investigated using relative power spectral measures. An association was found between an initial increase in theta and symptom improvement after 2 weeks. Additionally, late increases in delta and theta, along with a decrease in alpha, were linked to a reduction in MADRS after 8 weeks. These late changes were specifically observed in responders. To assess specificity, we extended our analysis to the independent CBT cohort. Responders exhibited an increase in delta and a decrease in alpha after 2 weeks. Furthermore, a late (baseline to week 16) decrease in alpha was associated with symptom improvement following CBT. Results suggest a common late decrease in alpha across both treatments, while modulatory effects in theta may be specific to escitalopram treatment. This study offers insights into electrophysiological markers indicating a favorable response to antidepressants, enhancing our comprehension of treatment response mechanisms in depression.

Resting-State EEG Alterations of Practice-Related Spectral Activity and Connectivity Patterns in Depression

BACKGROUND

Depression presents with altered energy regulation and neural plasticity. Previous electroencephalography (EEG) studies showed that practice in learning tasks increases power in beta range (13-30 Hz) in healthy subjects but not in those with impaired plasticity. Here, we ascertain whether depression presents with alterations of spectral activity and connectivity before and after a learning task.

METHODS

We used publicly available resting-state EEG recordings (64 electrodes) from 122 subjects. Based on Beck Depression Inventory (BDI) scores, they were assigned to either a high BDI (hBDI, BDI > 13, N = 46) or a control (CTL, BDI < 7, N = 75) group. We analyzed spectral activity, theta-beta, and theta-gamma phase-amplitude coupling (PAC) of EEG recorded at rest before and after a learning task.

RESULTS

At baseline, compared to CTL, hBDI exhibited greater power in beta over fronto-parietal regions and in gamma over the right parieto-occipital area. At post task, power increased in all frequency ranges only in CTL. Theta-beta and theta-gamma PAC were greater in hBDI at baseline but not after the task.

CONCLUSIONS

The lack of substantial post-task growth of beta power in depressed subjects likely represents power saturation due to greater baseline values. We speculate that inhibitory/excitatory imbalance, altered plasticity mechanisms, and energy dysregulation present in depression may contribute to this phenomenon.

EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation

BACKGROUND

Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG recordings could potentially yield in silico biomarkers of severe mental disorders.

HYPOTHESIS

Pathological and physiological aging processes influence the electrophysiological signatures of schizophrenia (SCZ) and major depressive disorder (MDD).

STUDY DESIGN

From a single-center cohort (N = 735, 51.6% male) comprising healthy control individuals (HC, N = 245) and inpatients suffering from SCZ (N = 250) or MDD (N = 240), we acquired resting-state 19 channel-EEG recordings. Using repeated nested cross-validation, support vector machine models were trained to (1) classify patients with SCZ or MDD and HC individuals and (2) predict age in HC individuals. The age model was applied to patient groups to calculate Electrophysiological Age Gap Estimation (EphysAGE) as the difference between predicted and chronological age. The links between EphysAGE, diagnosis, and medication were then further explored.

STUDY RESULTS

The classification models robustly discriminated SCZ from HC (balanced accuracy, BAC = 72.7%, P < .001), MDD from HC (BAC = 67.0%, P < .001), and SCZ from MDD individuals (BAC = 63.2%, P < .001). Notably, central alpha (8-11 Hz) power decrease was the most consistently predictive feature for SCZ and MDD. Higher EphysAGE was associated with an increased likelihood of being misclassified as SCZ in HC and MDD (ρHC = 0.23, P < .001; ρMDD = 0.17, P = .01).

CONCLUSIONS

ML models can extract electrophysiological signatures of MDD and SCZ for potential clinical use. However, the impact of aging processes on diagnostic separability calls for timely application of such models, possibly in early recognition settings.

Spatiotemporal alterations in the brain oscillations of Arctic explorers

BACKGROUND

The limited understanding of the physiology and psychology of polar expedition explorers has prompted concern over the potential cognitive impairments caused by exposure to extreme environmental conditions. Prior research has demonstrated that such stressors can negatively impact cognitive function, sleep quality, and behavioral outcomes. Nevertheless, the impact of the polar environment on neuronal activity remains largely unknown.

METHODS

In this study, we aimed to investigate spatiotemporal alterations in brain oscillations of 13 individuals (age range: 22-48 years) who participated in an Arctic expedition. We utilized electroencephalography (EEG) to record cortical activity before and during the Arctic journey, and employed standardized low resolution brain electromagnetic tomography to localize changes in alpha, beta, theta, and gamma activity.

RESULTS

Our results reveal a significant increase in the power of theta oscillations in specific regions of the Arctic, which differed significantly from pre-expedition measurements. Furthermore, microstate analysis demonstrated a significant reduction in the duration of microstates (MS) D and alterations in the local synchrony of the frontoparietal network.

CONCLUSION

Overall, these findings provide novel insights into the neural mechanisms underlying adaptation to extreme environments. These findings have implications for understanding the cognitive consequences of polar exploration and may inform strategies to mitigate potential neurological risks associated with such endeavors. Further research is warranted to elucidate the long-term effects of Arctic exposure on brain function.

Increased Delta and Theta Power Density in Sickle Cell Disease Individuals with Chronic Pain Secondary to Hip Osteonecrosis: A Resting-State Eeg Study

PURPOSE

Identify the presence of a dysfunctional electroencephalographic (EEG) pattern in individuals with sickle cell disease (SCD) and hip osteonecrosis, and assess its potential associations with depression, anxiety, pain severity, and serum levels of brain-derived neurotrophic factor (BDNF).

METHODS

In this cross-sectional investigation, 24 SCD patients with hip osteonecrosis and chronic pain were matched by age and sex with 19 healthy controls. Resting-state EEG data were recorded using 32 electrodes for both groups. Power spectral density (PSD) and peak alpha frequency (PAF) were computed for each electrode across Delta, Theta, Alpha, and Beta frequency bands. Current Source Density (CSD) measures were performed utilizing the built-in Statistical nonparametric Mapping Method of the LORETA-KEY software.

RESULTS

Our findings demonstrated that SCD individuals exhibited higher PSD in delta and theta frequency bands when compared to healthy controls. Moreover, SCD individuals displayed increased CSD in delta and theta frequencies, coupled with decreased CSD in the alpha frequency within brain regions linked to pain processing, motor function, emotion, and attention. In comparison to the control group, depression symptoms, and pain intensity during hip abduction were positively correlated with PSD and CSD in the delta frequency within the parietal region. Depression symptoms also exhibited a positive association with PSD and CSD in the theta frequency within the same region, while serum BDNF levels showed a negative correlation with CSD in the alpha frequency within the left insula.

CONCLUSION

This study indicates that individuals with SCD experiencing hip osteonecrosis and chronic pain manifest a dysfunctional EEG pattern characterized by the persistence of low-frequency PSD during a resting state. This dysfunctional EEG pattern may be linked to clinical and biochemical outcomes, including depression symptoms, pain severity during movement, and serum BDNF levels.

Intracranial Directed Connectivity Links Subregions of the Prefrontal Cortex to Major Depression

Understanding the neural basis of major depressive disorder (MDD) is vital to guiding neuromodulatory treatments. The available evidence supports the hypothesis that MDD is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neural systems lead to diminished emotion regulation and intrusive ruminations. Recent research also points towards network changes in the brain, especially within the prefrontal cortex (PFC), as primary sources of MDD etiology. However, due to limitations in spatiotemporal resolution and clinical opportunities for intracranial recordings, this hypothesis has not been directly tested. We recorded intracranial EEG from the dorsolateral (dlPFC), orbitofrontal (OFC), and anterior cingulate cortices (ACC) in neurosurgical patients with MDD. We measured daily fluctuations in self-reported depression severity alongside directed connectivity between these PFC subregions. We focused primarily on delta oscillations (1-3 Hz), which have been linked to GABAergic inhibitory control and intracortical communication. Depression symptoms worsened when connectivity within the left vs. right PFC became imbalanced. In the left hemisphere, all directed connectivity towards the ACC, from the dlPFC and OFC, was positively correlated with depression severity. In the right hemisphere, directed connectivity between the OFC and dlPFC increased with depression severity as well. This is the first evidence that delta oscillations flowing between prefrontal subregions transiently increase intensity when people are experiencing more negative mood. These findings support the overarching hypothesis that MDD worsens with prefrontal disinhibition.

Effect of add-on transcranial alternating current stimulation (tACS) in major depressive disorder: A randomized controlled trial

BACKGROUND

The effect of transcranial alternating current stimulation (tACS) on major depressive disorder (MDD) was not confirmed.

OBJECTIVE

To evaluate the feasibility, safety, and efficacy of tACS as an add-on treatment for the symptoms of depression and to understand how tACS affects brain activity.

METHODS

The 4-week, double-blind, randomized, sham-controlled trial was performed from January 29, 2023 to December 22, 2023. Sixty-six participants were recruited and randomly assigned to receive 20 40-min sessions of either active (77.5Hz, 15 mA) or sham stimulation, with one electrode on the forehead and two on the mastoid, each day (n = 33 for each group) for four weeks (till Week 4). The participants were followed for 4 more weeks (till Week 8) without stimulation for efficacy/safety assessment. During the 4-week trial, all participants were required to take 10-20 mg of escitalopram daily. The primary efficacy endpoint was the change in HAMD-17 scores from baseline to Week 4 (with 20 treatment sessions completed). Resting-state electroencephalography (EEG) was collected with a 64-channel EEG system (Brain Products, Germany) at baseline and the Week 4 follow-up. The chi-square test, Fisher's exact test, independent-sample t-test, or Wilcoxon rank-sum test were used, as appropriate, to compare the differences in variables between groups. The effect of the intervention on the HAMD-17 score was also evaluated with linear mixed modeling (LMM) as sensitivity analysis. The correlation between the mean reduction in EEG and the mean reduction in the HAMD-17 total score was evaluated using Spearman correlation analysis.

RESULTS

A total of 66 patients (mean [SD] age, 28.4 [8.18] years; 52 [78.8 %] female) were randomized, and 57 patients completed the study. Significant differences were found in the reductions in the HAMD-17 scores at Week 4 (t = 3.44, P = 0.001). Response rates at Week 4 were significantly higher in the active tACS group than in the sham tACS group (22 out of 33 patients [66.7 %] versus 11 out of 33 [33.3 %], P = 0.007). In the active tACS group, a correlation between the mean change in alpha power and HAMD-17 scores at Week 4 was found (r = 2.38, P = 0.024), and the mean change in alpha power was significantly bigger for responders (Z = 2.46, P = 0.014). No serious adverse events were observed in this trial.

CONCLUSION

The additional antidepressant effect of tACS is significant, and the combination of tACS with antidepressants is a feasible and effective approach for the treatment of MDD. The antidepressant mechanism of tACS may be the reduction in alpha power in the left frontal lobe. Future research directions may include exploring more appropriate treatment parameters of tACS.

Altered beta band spatial-temporal interactions during negative emotional processing in major depressive disorder: An MEG study

BACKGROUND

The mood-concordance bias is a key feature of major depressive disorder (MDD), but the spatiotemporal neural activity associated with emotional processing in MDD remains unclear. Understanding the dysregulated connectivity patterns during emotional processing and their relationship with clinical symptoms could provide insights into MDD neuropathology.

METHODS

We enrolled 108 MDD patients and 64 healthy controls (HCs) who performed an emotion recognition task during magnetoencephalography recording. Network-based statistics (NBS) was used to analyze whole-brain functional connectivity (FC) across different frequency ranges during distinct temporal periods. The relationship between the aberrant FC and affective symptoms was explored.

RESULTS

MDD patients exhibited decreased FC strength in the beta band (13-30 Hz) compared to HCs. During the early stage of emotional processing (0-100 ms), reduced FC was observed between the left parahippocampal gyrus and the left cuneus. In the late stage (250-400 ms), aberrant FC was primarily found in the cortex-limbic-striatum systems. Moreover, the FC strength between the right fusiform gyrus and left thalamus, and between the left calcarine fissure and left inferior temporal gyrus were negatively associated with Hamilton Depression Rating Scale (HAMD) scores.

LIMITATIONS

Medication information was not involved.

CONCLUSION

MDD patients exhibited abnormal temporal-spatial neural interactions in the beta band, ranging from early sensory to later cognitive processing stages. These aberrant interactions involve the cortex-limbic-striatum circuit. Notably, aberrant FC in may serve as a potential biomarker for assessing depression severity.

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.

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.

Alterations in EEG functional connectivity in individuals with depression: A systematic review

The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.

Altered electroencephalography resting state network coherence in remitted MDD

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

Correlation between electroencephalographic markers in the healthy brain

Mental disorders have an increasing tendency and represent the main burden of disease to society today. A wide variety of electroencephalographic (EEG) markers have been successfully used to assess different symptoms of mental disorders. Different EEG markers have demonstrated similar classification accuracy, raising a question of their independence. The current study is aimed to investigate the hypotheses that different EEG markers reveal partly the same EEG features reflecting brain functioning and therefore provide overlapping information. The assessment of the correlations between EEG signal frequency band power, dynamics, and functional connectivity markers demonstrates that a statistically significant correlation is evident in 37 of 66 (56%) comparisons performed between 12 markers of different natures. A significant correlation between the majority of the markers supports the similarity of information in the markers. The results of the performed study confirm the hypotheses that different EEG markers reflect partly the same features in brain functioning. Higuchi's fractal dimension has demonstrated a significant correlation with the 82% of other markers and is suggested to reveal a wide spectrum of various brain disorders. This marker is preferable in the early detection of symptoms of mental disorders.

High-frequency repetitive transcranial magnetic stimulation regulates neural oscillations of the hippocampus and prefrontal cortex in mice by modulating endocannabinoid signalling

BACKGROUND

Neural oscillations play a role in the antidepressant effects of repetitive transcranial magnetic stimulation (rTMS). However, the effects of high-frequency rTMS on the neural oscillations of the medial prefrontal cortex (mPFC) and hippocampus (HPC) and its molecular mechanism have not been fully clarified.

METHODS

The depressive-like behaviours, local field potentials (LFPs) of the ventral HPC (vHPC)-mPFC, and alternations of endocannabinoid system (ECS) in the HPC and mPFC were observed after rTMS treatment. Meanwhile, depressive-like behaviours and LFPs were also observed after cannabinoid type-1 receptor (CB1R) antagonist AM281 or monoacylglycerol lipase inhibitor JZL184 injection. Moreover, the antidepressant effect of rTMS was further assessed in glutamatergic-CB1R and gamma-amino butyric acid (GABA)-ergic -CB1R knockout mice.

RESULTS

Alternations of endocannabinoids and energy value and synchronisation of mPFC-vHPC, especially the decrease of theta oscillation induced by CUMS, were alleviated by rTMS. JZL184 has similar effects to rTMS and AM281 blocked the effects of rTMS. GABAergic-CB1R deletion inhibited CUMS-induced depressive-like behaviours whereas Glutaminergic-CB1R deletion dampened the antidepressant effects of rTMS.

LIMITATIONS

The immediate effect of rTMS on field-potential regulation was not observed. Moreover, the role of region-specific regulation of the ECS in the antidepressant effect of rTMS was unclear and the effects of cell-specific CB1R knockout on neuronal oscillations of the mPFC and vHPC should be further investigated.

CONCLUSION

Endocannabinoid system mediated the antidepressant effects and was involved in the regulation of LFP in the vHPC-mPFC of high-frequency rTMS.

Depression screening using hybrid neural network

Depression is a common cause of increased suicides worldwide, and studies have shown that the number of patients suffering from major depressive disorder (MDD) increased several-fold during the COVID-19 pandemic, highlighting the importance of disease detection and depression management, while increasing the need for effective diagnostic tools. In recent years, machine learning and deep learning methods based on electroencephalography (EEG) have achieved significant results in the field of automatic depression detection. However, most current studies have focused on a small number of EEG signal channels, and experimental data require special processing by professionals. In this study, 128 channels of EEG signals were simply filtered and 24-fold leave-one-out cross-validation experiments were performed using 2DCNN-LSTM classifier, support vector machine, K-nearest neighbor and decision tree. The current results show that the proposed 2DCNN-LSTM model has an average classification accuracy of 95.1% with an AUC of 0.98 for depression detection of 6-second participant EEG signals, and the model is much better than 72.05%, 79.7% and 79.49% for support vector machine, K nearest neighbor and decision tree. In addition, we found that the model achieved a 100% probability of correctly classifying the EEG signals of 300-second participants.

N,N-dimethyltryptamine affects electroencephalography response in a concentration-dependent manner-A pharmacokinetic/pharmacodynamic analysis

N,N-dimethyltryptamine (DMT) is a psychedelic substance and is being used as a research tool in investigations of the neurobiology behind the human consciousness using different brain imaging techniques. The effects of psychedelics have commonly been studied using electroencephalography (EEG) and have been shown to produce suppression of alpha power and increase in signal diversity. However, the relationship between DMT exposure and its EEG effects has never been quantified. In this work, a population pharmacokinetic/pharmacodynamic analysis was performed investigating the relationship between DMT plasma concentrations and its EEG effects. Data were obtained from a clinical study where DMT was administered by intravenous bolus dose to 13 healthy subjects. The effects on alpha power, beta power, and Lempel-Ziv complexity were evaluated. DMT was shown to fully suppress alpha power. Beta power was only partially suppressed, whereas an increase in Lempel-Ziv complexity was observed. The relationship between plasma concentrations and effects were described using effect compartment models with sigmoidal maximum inhibitory response or maximum stimulatory response models. Values of the concentration needed to reach half of the maximum response (EC_50,e ) were estimated at 71, 137, and 54 nM for alpha, beta, and Lempel-Ziv complexity, respectively. A large amount of between-subject variability was associated with both beta power and Lempel-Ziv complexity with coefficients of variability of 75% and 77% for the corresponding EC_50,e values, respectively. Alpha power appeared to be the most robust response, with a between-subject variability in EC_50,e of 29%. Having a deeper understanding of these processes might prove beneficial in choosing appropriate doses and response biomarkers in the future clinical development of DMT.

Alpha peak activity in resting-state EEG is associated with depressive score

Introduction

Depression is a serious psychiatric disorder characterized by prolonged sadness, loss of interest or pleasure. The dominant alpha peak activity in resting-state EEG is suggested to be an intrinsic neural marker for diagnosis of mental disorders.

Methods

To investigate an association between alpha peak activity and depression severity, the present study recorded resting-state EEG (EGI 128 channels, off-line average reference, source reconstruction by a distributed inverse method with the sLORETA normalization, parcellation of 68 Desikan-Killiany regions) from 155 patients with depression (42 males, mean age 35 years) and acquired patients' scores of Self-Rating Depression Scales. We measured both the alpha peak amplitude that is more related to synchronous neural discharging and the alpha peak frequency that is more associated with brain metabolism.

Results

The results showed that over widely distributed brain regions, individual patients' alpha peak amplitudes were negatively correlated with their depressive scores, and individual patients' alpha peak frequencies were positively correlated with their depressive scores.

Discussion

These results reveal that alpha peak amplitude and frequency are associated with self-rating depressive score in different manners, and the finding suggests the potential of alpha peak activity in resting-state EEG acting as an important neural factor in evaluation of depression severity in supplement to diagnosis.

Central and peripheral nervous system responses to chronic and paced hyperventilation in anxious and healthy subjects

The aim of the present study was to examine self-report, peripheral nervous system, and central nervous system correlates of naturally-occurring, chronic hyperventilation (HV, assessed by hypocapnia or low resting state low end-tidal CO2), and to examine the additional effect of acute, experimentally-induced HV in anxious and healthy participants. By identifying the biomarkers of anxiety-related chronic HV and examining responses to acute HV, we hope to identify meaningful, mechanistic targets for further treatment development. Seventy anxious patients and 34 healthy control participants completed electroencephalogram (EEG) and peripheral nervous system recording at baseline and following a paced breathing task. Diagnosis x baseline hypnocapnia group analyses indicated that anxious/hypocapnic patients exhibited greater nonspecific skin conductance response amplitude than did anxious/normocapnic patients, and the anxious group reported greater HV-related symptoms and anxiety sensitivity than did the control group. However, no EEG abnormalities were noted as a function of anxiety group or baseline hypocapnia status. Following paced HV, anxious patients (but not controls) exhibited an increase in left-frontal alpha 1 power. Hypocapnic, but not normocapnic, participants exhibited an increase in skin conductance levels. Anxious patients reported an increase in negative cognitive appraisals of HV symptoms, and anxious/hypocapnic participants reported an increase in affective responses to HV. Thus, chronic HV is associated with greater arousal, and increased self-reported and physiological sensitivity to paced HV. Patients who chronically hyperventilate appear to be more sensitive to respiratory distress, responding with higher levels of anxiety and poorer tolerance of the physiological sensations accompanying acute HV.

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.

Reduced subgenual cingulate-dorsolateral prefrontal connectivity as an electrophysiological marker for depression

Major Depressive Disorder (MDD) is a widespread mental illness that causes considerable suffering, and neuroimaging studies are trying to reduce this burden by developing biomarkers that can facilitate detection. Prior fMRI- and neurostimulation studies suggest that aberrant subgenual Anterior Cingulate (sgACC)-dorsolateral Prefrontal Cortex (DLPFC) functional connectivity is consistently present within MDD. Combining the need for reliable depression markers with the electroencephalogram's (EEG) high clinical utility, we investigated whether aberrant EEG sgACC-DLPFC functional connectivity could serve as a marker for depression. Source-space Amplitude Envelope Correlations (AEC) of 20 MDD patients and 20 matched controls were contrasted using non-parametric permutation tests. In addition, extracted AEC values were used to (a) correlate with characteristics of depression and (b) train a Support Vector Machine (SVM) to determine sgACC-DLPFC connectivity's discriminative power. FDR-thresholded statistical maps showed reduced sgACC-DLPFC AEC connectivity in MDD patients relative to controls. This diminished AEC connectivity is located in the beta-1 (13-17 Hz) band and is associated with patients' lifetime number of depressive episodes. Using extracted sgACC-DLPFC AEC values, the SVM achieved a classification accuracy of 84.6% (80% sensitivity and 89.5% specificity) indicating that EEG sgACC-DLPFC connectivity has promise as a biomarker for MDD.

Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients with Major Depressive Disorder

Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.

Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis

Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90-89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747-0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05-88.70), and 84.60% (95% CI: 67.89-92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45-94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45-94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.

Resting-state electroencephalography changes in poststroke patients with visuospatial neglect

Background

This study aimed to explore the electrophysiological characteristics of resting-state electroencephalography (rsEEG) in patients with visuospatial neglect (VSN) after stroke.

Methods

A total of 44 first-event sub-acute strokes after right hemisphere damage (26 with VSN and 18 without VSN) were included. Besides, 18 age-matched healthy participants were used as healthy controls. The resting-state electroencephalography (EEG) of 64 electrodes was recorded to obtain the power of the spectral density of different frequency bands. The global delta/alpha ratio (DAR), DAR over the affected hemispheres (DAR_AH), DAR over the unaffected hemispheres (DAR_UH), and the pairwise-derived brain symmetry index (pdBSI; global and four bands) were compared between groups and receiver operating characteristic (ROC) curve analysis was conducted. The Barthel index (BI), Fugl-Meyer motor function assessment (FMA), and Berg balance scale (BBS) were used to assess the functional state of patients. Visuospatial neglect was assessed using a battery of standardized tests.

Results

We found that patients with VSN performed poorly compared with those without VSN. Analysis of rsEEG revealed increased delta and theta power and decreased alpha and beta power in stroke patients with VSN. Compared to healthy controls and poststroke non-VSN patients, patients with VSN showed a higher DAR (P < 0.001), which was significantly positively correlated with the BBS (DAR: r = –0.522, P = 0.006; DAR_AH: r = –0.521, P = 0.006; DAR_UH: r = –0.494, P = 0.01). The line bisection task was positively correlated with DAR (r = 0.458, P = 0.019) and DAR_AH (r = 0.483, P = 0.012), while the star cancellation task was only positively correlated with DAR_AH (r = 0.428, P = 0.029). DAR_AH had the best discriminating value between VSN and non-VSN, with an area under the curve (AUC) of 0.865. Patients with VSN showed decreased alpha power in the parietal and occipital areas of the right hemisphere. A higher parieto-occipital pdBSI_alpha was associated with a worse line bisection task (r = 0.442, P = 0.024).

Conclusion

rsEEG may be a useful tool for screening for stroke patients with visuospatial neglect, and DAR and parieto-occipital pdBSI_alpha may be useful biomarkers for visuospatial neglect after stroke.

Abnormal Brain Topological Structure of Mild Depression During Visual Search Processing Based on EEG Signals

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

Evaluating the Different Stages of Parkinson's Disease Using Electroencephalography With Holo-Hilbert Spectral Analysis

Electroencephalography (EEG) can reveal the abnormalities of dopaminergic subcortico-cortical circuits in patients with Parkinson's disease (PD). However, conventional time-frequency analysis of EEG signals cannot fully reveal the non-linear processes of neural activities and interactions. A novel Holo-Hilbert Spectral Analysis (HHSA) was applied to reveal non-linear features of resting state EEG in 99 PD patients and 59 healthy controls (HCs). PD patients demonstrated a reduction of β bands in frontal and central regions, and reduction of γ bands in central, parietal, and temporal regions. Compared with early-stage PD patients, late-stage PD patients demonstrated reduction of β bands in the posterior central region, and increased θ and δ2 bands in the left parietal region. θ and β bands in all brain regions were positively correlated with Hamilton depression rating scale scores. Machine learning algorithms using three prioritized HHSA features demonstrated "Bag" with the best accuracy of 0.90, followed by "LogitBoost" with an accuracy of 0.89. Our findings strengthen the application of HHSA to reveal high-dimensional frequency features in EEG signals of PD patients. The EEG characteristics extracted by HHSA are important markers for the identification of depression severity and diagnosis of PD.

Single session gamma transcranial alternating stimulation does not modulate working memory in depressed patients and healthy controls

OBJECTIVES

Gamma transcranial alternating current stimulation (gamma tACS) is considered a non-invasive brain stimulation technique for modulation of cognitive performance and for treatment of psychiatric disorders. There is heterogeneous data on its effectiveness in improving working memory.

METHODS

In this randomized crossover study, we tested 22 patients with major depression and 21 healthy volunteers who received 20 min of active and sham 40 Hz gamma tACS over bilateral dorsolateral prefrontal cortex during a computerized n-back task in a cross-over design.

RESULTS

We showed no improvement in reaction time and accuracy of working memory during active or sham stimulation in both groups, and no interaction between cognitive load and stimulation conditions.

CONCLUSION

The present study suggests that a single session of gamma tACS does not affect cognition in depression. However, the bilateral electrode montage and learning or ceiling effects may have affected results. Overall, this study is in line with the heterogeneous results of previous gamma tACS studies, emphasizing that methodologies and study designs should be harmonized.

Exploring Oscillatory Dysconnectivity Networks in Major Depression during Resting State Using Coupled Tensor Decomposition

Dysconnectivity of large-scale brain networks has been linked to major depression disorder (MDD) during resting state. Recent researches show that the temporal evolution of brain networks regulated by oscillations reveals novel mechanisms and neural characteristics of MDD. Our study applied a novel coupled tensor decomposition model to investigate the dysconnectivity networks characterized by spatio-temporal-spectral modes of covariation in MDD using resting electroencephalography. The phase lag index is used to calculate the functional connectivity within each time window at each frequency bin. Then, two adjacency tensors with the dimension of time frequency connectivity subject are constructed for the healthy group and the major depression group. We assume that the two groups share the same features for group similarity and retain individual characteristics for group differences. Considering that the constructed tensors are nonnegative and the components in spectral and adjacency modes are partially consistent among the two groups, we formulate a double-coupled nonnegative tensor decomposition model. To reduce computational complexity, we introduce the lowrank approximation. Then, the fast hierarchical alternative least squares algorithm is applied for model optimization. After clustering analysis, we summarize four oscillatory networks characterizing the healthy group and four oscillatory networks characterizing the major depression group, respectively. The proposed model may reveal novel mechanisms of pathoconnectomics in MDD during rest, and it can be easily extended to other psychiatric disorders.

Mindfulness-Based Cognitive Therapy in Recurrent MDD Patients With Residual Symptoms: Alterations in Resting-State Theta Oscillation Dynamics Associated With Changes in Depression and Rumination

Many patients with major depressive disorder (MDD) suffer from residual symptoms. Rumination is a specific known risk factor for the onset, severity, prolongation, and relapse of MDD. This study aimed to examine the efficacy and EEG substrates of mindfulness-based cognitive therapy (MBCT) in alleviating depression and rumination in an MDD population with residual symptoms. We recruited 26 recurrent MDD individuals who had residual symptoms with their current antidepressants to participate in the 8-week MBCT intervention. We evaluated the efficacy and changes in the dynamics of resting-state theta rhythm after the intervention, as well as the associations between theta alterations and improvements in depression and rumination. The participants showed reduced depression, enhanced adaptive reflective rumination, and increased theta power and phase synchronization after MBCT. The increased theta-band phase synchronizations between the right occipital regions and the right prefrontal, central, and parietal regions were associated with reduced depression, while the increase in theta power in the left parietal region was associated with improvements in reflective rumination. MBCT could alleviate depression and enhance adaptive, reflective rumination in recurrent MDD individuals with residual symptoms through the modulation of theta dynamics in specific brain regions.

Altered directed connectivity during processing of predictive stimuli in psychiatric patient populations

OBJECTIVES

The study investigated the role of top-down versus bottom-up connectivity, during the processing of predictive information, in three different psychiatric disorders.

METHODS

Electroencephalography (EEG) was recorded during the performance of a task, which evaluates the ability to use predictive information in order to facilitate predictable versus random target detection. We evaluated EEG event-related directed connectivity, in patients with schizophrenia (SZ), major depressive disorder (MDD), and autism spectrum disorder (ASD), compared with healthy age-matched controls. Directed connectivity was evaluated using phase transfer entropy.

RESULTS

We showed that top-down frontal-parietal connectivity was weaker in SZ (theta and beta bands) and ASD (alpha band) compared to control subjects, during the processing of stimuli consisting of the predictive sequence. In SZ patients, top-down connectivity was also attenuated, during the processing of predictive targets in the beta frequency band. In contrast, compared with controls, MDD patients displayed an increased top-down flow of information, during the processing of predicted targets (alpha band).

CONCLUSIONS

The findings suggest that top-down frontal-parietal connectivity is altered differentially across three major psychiatric disorders, specifically during the processing of predictive stimuli.

SIGNIFICANCE

Altered top-down connectivity may contribute to the specific prediction deficits observed in each of the patient populations.

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Neural oscillations exert a key role in coordinating the activity of remote brain regions, and various assemblies of oscillations can modulate different networks to support different cognitive tasks. Studies have demonstrated that the dysconnectivity of electroencephalography (EEG) oscillatory networks is related with MDD. In this study, we investigated the oscillatory hyperconnectivity and hypoconnectivity networks in MDD under a naturalistic and continuous stimuli condition of music listening. With the assumption that the healthy group and the MDD group share similar brain topology from the same stimuli and also retain individual brain topology for group differences, we applied the coupled nonnegative tensor decomposition algorithm on two adjacency tensors with the dimension of time × frequency × connectivity × subject, and imposed double-coupled constraints on spatial and spectral modes. The music-induced oscillatory networks were identified by a correlation analysis approach based on the permutation test between extracted temporal factors and musical features. We obtained three hyperconnectivity networks from the individual features of MDD and three hypoconnectivity networks from common features. The results demonstrated that the dysfunction of oscillatory networks could affect the involvement in music perception for MDD patients. Those oscillatory dysconnectivity networks may provide promising references to reveal the pathoconnectomics of MDD and potential biomarkers for the diagnosis of MDD.

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.

Threshold of radiofrequency electromagnetic field effect on human brain

PURPOSE

This review aims to estimate the threshold of radiofrequency electromagnetic field (RF EMF) effects on human brain based on analyses of published research results. To clarify the threshold of the RF EMF effects, two approaches have been applied: (1) the analyses of restrictions in sensitivity for different steps of the physical model of low-level RF EMF mechanism and (2) the analyses of experimental data to clarify the dependence of the RF EMF effect on exposure level based on the results of published original neurophysiological and behavioral human studies for 15 years 2007-2021.

CONCLUSIONS

The analyses of the physical model of nonthermal mechanisms of RF EMF effect leads to conclusion that no principal threshold of the effect can be determined. According to the review of experimental data, the rate of detected RF EMF effects is 76.7% in resting EEG studies, 41.7% in sleep EEG and 38.5% in behavioral studies. The changes in EEG probably appear earlier than alterations in behavior become evident. The lowest level of RF EMF at which the effect in EEG was detected is 2.45 V/m (SAR = 0.003 W/kg). There is a preliminary indication that the dependence of the effect on the level of exposure follows rather field strength than SAR alterations. However, no sufficient data are available for clarifying linearity-nonlinearity of the dependence of effect on the level of RF EMF. The finding that only part of people are sensitive to RF EMF exposure can be related to immunity to radiation or hypersensitivity. The changes in EEG caused by RF EMF appeared similar in the majority of analyzed studies and similar to these in depression. The possible causal relationship between RF EMF effect and depression among young people is highly important problem.

Characterizing Cortical Oscillatory Responses in Major Depressive Disorder Before and After Convulsive Therapy: A TMS-EEG Study

BACKGROUND

Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is emerging as a powerful technique for interrogating neural circuit dysfunction in psychiatric disorders. Here, we utilized time-frequency analyses to characterize differences in neural oscillatory dynamics between subjects with major depressive disorder (MDD) and healthy controls (HC). We further examined changes in TMS-related oscillatory power following convulsive therapy.

METHODS

Oscillatory power was examined following TMS over the dorsolateral prefrontal and motor cortices (DLPFC and M1) in 38 MDD subjects, and 22 HCs. We further investigated how these responses changed in the MDD group following an acute course of convulsive therapy (either magnetic seizure therapy [MST, n = 24] or electroconvulsive therapy [ECT, n = 14]).

RESULTS

Prior to treatment, MDD subjects exhibited increased oscillatory power within delta, theta, and alpha frequency bands with TMS-EEG over the DLPFC, but showed no differences to HCs with stimulation over M1. Following MST, DLPFC stimulation revealed attenuated baseline-normalized power in the delta and theta bands, with reductions in the delta, theta, and alpha power following ECT. TMS over M1 revealed reduced delta and theta power following ECT, with no changes observed following MST. An association was also observed between the treatment- induced change in alpha power and depression severity score.

LIMITATIONS

Limitations include the modest sample size, open-label MST and ECT treatment designs, and lack of a placebo condition.

CONCLUSIONS

These results provide evidence of alterations in TMS-related oscillatory activity in MDD, and further suggest modulation of oscillatory power following ECT and MST.

Standardization and Personalized Medicine Using Quantitative EEG in Clinical Settings

Two major trends have been dominant in health care in recent years. First, there is a growing consensus that standardization of health care procedures and methods can result in improved effectiveness and safety of treatments. Second, there is increased interest in "personalized medicine," which refers to the tailoring of treatments to individual patients. Here I discuss how these trends apply to the field of quantitative EEG (qEEG), where de-artifacted resting state EEGs of individuals are compared with a normative database in order to assess clinically meaningful deviations, which can be used for diagnostic procedures, to guide personalized treatment protocols, and to assess treatment effectiveness. Standardized and automated de-artifacting procedures are increasingly being used in scientific research and in clinical practice. The advantages of these procedures over manual de-artifacting will be discussed. The results of a systematic comparison between 2 commonly used qEEG databases show that these databases produce very comparable results, illustrating not only the validity and reliability of both databases but also the opportunity to move forward to a standardized use of qEEG in clinical practice. Finally, the standardization of qEEG interpretation as both a diagnostic and treatment selection tool provides an example of how qEEG can merge both personalized medicine and standardization in the treatment of psychological disorders.

Electrophysiological scarring in remitted depressed patients: Elevated EEG functional connectivity between the posterior cingulate cortex and the subgenual prefrontal cortex as a neural marker for rumination

INTRODUCTION

Prior resting state fMRI studies have revealed that elevated connectivity between the default mode network (DMN) and subgenual prefrontal cortex (sgPFC) connectivity may underly maladaptive rumination, which is a major risk factor for depression. To further evaluate such relationship, we investigated whether posterior regions of the DMN, showed elevated connectivity with the sgPFC in remitted depressed patients (rMDD) and whether this connectivity was related to maladaptive rumination.

METHODS

We examined whether rMDD (N = 20) had elevated EEG posterior DMN - sgPFC functional connectivity when compared to age and sex matched healthy controls (N = 17), and whether this posterior DMN - sgPFC connectivity positively correlated with rumination. Using minimum norm as the source estimation method, we extracted current density maps from six regions of interest (ROIs) within the posterior DMN. EEG source-space functional connectivity was calculated using the Amplitude Envelope Correlation method.

RESULTS

Relative to controls, rMDD showed increased posterior cingulate cortex (PCC) - sgPFC connectivity in the beta-3 (25-30 Hz) band. As hypothesized, PCC - sgPFC connectivity was positively associated with rumination for rMDD, even after controlling for depression and anxiety.

LIMITATIONS

The absence of an MDD patient group and the relatively small sample size can limit the generalizability of the results.

CONCLUSIONS

EEG resting state PCC - sgPFC functional connectivity is significantly elevated in rMDD and is associated with rumination, suggesting that EEG PCC - sgPFC connectivity may be useful as a neural marker to identify individuals at risk for depression.

Transcranial alternating current stimulation (tACS): from basic mechanisms towards first applications in psychiatry

Transcranial alternating current stimulation (tACS) is a unique form of non-invasive brain stimulation. Sinusoidal alternating electric currents are delivered to the scalp to affect mostly cortical neurons. tACS is supposed to modulate brain function and, in turn, cognitive processes by entraining brain oscillations and inducing long-term synaptic plasticity. Therefore, tACS has been investigated in cognitive neuroscience, but only recently, it has been also introduced in psychiatric clinical trials. This review describes current concepts and first findings of applying tACS as a potential therapeutic tool in the field of psychiatry. The current understanding of its mechanisms of action is explained, bridging cellular neuronal activity and the brain network mechanism. Revisiting the relevance of altered brain oscillations found in six major psychiatric disorders, putative targets for the management of mental disorders using tACS are discussed. A systematic literature search on PubMed was conducted to report findings of the clinical studies applying tACS in patients with psychiatric conditions. In conclusion, the initial results may support the feasibility of tACS in clinical psychiatric populations without serious adverse events. Moreover, these results showed the ability of tACS to reset disturbed brain oscillations, and thus to improve behavioural outcomes. In addition to its potential therapeutic role, the reactivity of the brain circuits to tACS could serve as a possible tool to determine the diagnosis, classification or prognosis of psychiatric disorders. Future double-blind randomised controlled trials are necessary to answer currently unresolved questions. They may aim to detect response predictors and control for various confounding factors.

EEG Correlates of Cognitive Functions and Neuropsychiatric Disorders: A Review of Oscillatory Activity and Neural Synchrony Abnormalities

Background:

A large body of evidence suggested that disruption of neural rhythms and synchronization of brain oscillations are correlated with a variety of cognitive and perceptual processes. Cognitive deficits are common features of psychiatric disorders that complicate treatment of the motivational, affective and emotional symptoms.

Objective:

Electrophysiological correlates of cognitive functions will contribute to understanding of neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and developing novel targets for the treatment of cognitive impairments.

Methods:

This review includes a description of brain oscillations in Alzheimer’s disease, bipolar disorder, attention-deficit/hyperactivity disorder, major depression, obsessive compulsive disorders, anxiety disorders, schizophrenia and autism.

Results:

The review clearly shows that the reviewed neuropsychiatric diseases are associated with fundamental changes in both spectral power and coherence of EEG oscillations.

Conclusion:

In this article, we examined the nature of brain oscillations, the association of brain rhythms with cognitive functions and the relationship between EEG oscillations and neuropsychiatric diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric disorders.

Current Updates on Newer Forms of Transcranial Magnetic Stimulation in Major Depression

Repetitive transcranial magnetic stimulation (rTMS) is an FDA-approved technique for treating medication-resistant depression. Conventional rTMS includes high frequency (HF) to left dorsolateral prefrontal cortex (DLPFC) and low frequency to right DLPFC. However, not all depressed patients could benefit from standard rTMS protocols. Meta-analytical evidence indicated that there was an average response rate of 29.3% for patients receiving the most commonly adopted HF rTMS to the left DLPFC. Hence, newer forms of rTMS paradigms are warranted to improve antidepressant response and remission rate in patients with depression, especially those who are refractory to adequate antidepressant trials. In the current chapter, we review newer forms of rTMS paradigms and the content will cover standard theta burst stimulation (TBS), prolonged iTBS (piTBS), accelerated rTMS (aTMS), deep TMS (dTMS), priming TMS (pTMS), synchronized TMS (sTMS), and magnetic seizure therapy (MST).

Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression

To examine the electrophysiological underpinnings of the functional networks involved in music listening, previous approaches based on spatial independent component analysis (ICA) have recently been used to ongoing electroencephalography (EEG) and magnetoencephalography (MEG). However, those studies focused on healthy subjects, and failed to examine the group-level comparisons during music listening. Here, we combined group-level spatial Fourier ICA with acoustic feature extraction, to enable group comparisons in frequency-specific brain networks of musical feature processing. It was then applied to healthy subjects and subjects with major depressive disorder (MDD). The music-induced oscillatory brain patterns were determined by permutation correlation analysis between individual time courses of Fourier-ICA components and musical features. We found that (1) three components, including a beta sensorimotor network, a beta auditory network and an alpha medial visual network, were involved in music processing among most healthy subjects; and that (2) one alpha lateral component located in the left angular gyrus was engaged in music perception in most individuals with MDD. The proposed method allowed the statistical group comparison, and we found that: (1) the alpha lateral component was activated more strongly in healthy subjects than in the MDD individuals, and that (2) the derived frequency-dependent networks of musical feature processing seemed to be altered in MDD participants compared to healthy subjects. The proposed pipeline appears to be valuable for studying disrupted brain oscillations in psychiatric disorders during naturalistic paradigms.

Depression evaluation based on prefrontal EEG signals in resting state using fuzzy measure entropy

OBJECTIVE

Depression is a mental disorder that causes emotional changes and even suicide. However, there is still a lack of objective physiological data to support current clinical depression diagnosis. Accurate computer-aided diagnosis systems are becoming more and more crucial and urgent for future depression diagnosis. The purpose of this study is to analyze the electroencephalogram (EEG) regularity of depression using fuzzy measure entropy (FMEn), and thus to explore its role in the computer-aided diagnosis of depression.

APPROACH

Three-channel EEG signals among 35 subjects (divided into two groups according to the severity of the disease) were recorded in this study. First, the frontal delta, theta, alpha and beta frequency bands were extracted after preprocessing, and the sample entropy (SEn) and the FMEn were calculated. Then, the difference between the two groups and the correlation between the entropy values and the Hamilton Depression Rating Scale scores were analyzed using statistical analysis. Finally, the results of FMEn were compared with those of SEn.

MAIN RESULTS

A better statistically significant difference between the two groups using FMEn was revealed, with p < 0.01 in the theta and alpha bands. In terms of SEn, only SEn_Fp2 in the delta band, SEn_Fp2 in the theta band and SEn_Fp1 in the alpha band performed better, showing significant differences with p = 0.0006, p = 0.002 and p = 0.0114.

SIGNIFICANCE

These findings suggest that frontal EEG signal complexity analysis with depression using FMEn might be more sensitive than that using SEn. FMEn could be considered as a promising biomarker for future clinical depression detection.

Predicting Depression Symptoms in Families at Risk for Depression: Interrelations of Posterior EEG Alpha and Religion/Spirituality

BACKGROUND

Posterior EEG alpha has been identified as a putative biomarker of clinical outcomes in major depression (MDD). Separately, personal importance of religion and spirituality (R/S) has been shown to provide protective benefits for individuals at high familial risk for MDD. This study directly explored the joint value of posterior alpha and R/S on predicting clinical health outcomes of depression.

METHODS

Using a mixed-effects model approach, we obtained virtual estimates of R/S at age 21 using longitudinal data collected at 5 timepoints spanning 25 years. Current source density and frequency principal component analysis was used to quantify posterior alpha in 72-channel resting EEG (eyes open/closed). Depression severity was measured between 5 and 10 years after EEG collection using PHQ-9 and IDAS-GD scales.

RESULTS

Greater R/S (p = .008, η²_p = 0.076) and higher alpha (p = .02, η²_p = 0.056) were separately associated with fewer symptoms across scales. However, an interaction between alpha and R/S (p = .02, η²_p = 0.062) was observed, where greater R/S predicted fewer symptoms with low alpha but high alpha predicted fewer symptoms with lower R/S.

LIMITATIONS

Small-to-medium effect sizes and homogeneity of sample demographics caution overall interpretation and generalizability.

CONCLUSIONS

Findings revealed a complementary role of R/S and alpha in that either variable exerted protective effects only if the other was present at low levels. These findings confirm the relevance of R/S importance and alpha oscillations as predictors of depression symptom severity. More research is needed on the neurobiological mechanism underlying the protective effects of R/S importance for MDD.

Riemannian classifier enhances the accuracy of machine-learning-based diagnosis of PTSD using resting EEG

Recently, objective and automated methods for the diagnosis of post-traumatic stress disorder (PTSD) have attracted increasing attention. However, previous studies on machine-learning-based diagnosis of PTSD with resting-state electroencephalogram (EEG) have reported poor accuracies of as low as 60%. Here, a Riemannian geometry-based classifier, the Fisher geodesic minimum distance to the mean (FgMDM), was employed for PTSD classification for the first time. Eyes-closed resting-state EEG data of 39 healthy individuals and 42 PTSD patients were used for the analysis. EEG source activities in 148 cortical regions were parcellated based on the Destrieux atlas, and their covariances were evaluated for each individual. Thirty epochs of preprocessed EEG were employed to calculate source activities. In addition, the FgMDM approach was applied to each EEG source covariance to construct the classifier. For a comparison, linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) classifiers employing source band powers and network features as feature candidates were also tested. The FgMDM classifier showed an average classification accuracy of 75.240.80%. In contrast, the maximum accuracies of LDA, SVM, and RF classifiers were 66.54 ± 2.99%, 61.11 ± 2.98%, and 60.99 ± 2.19%, respectively. Our study demonstrated that the diagnostic accuracy of PTSD with resting-state EEG could be significantly improved by employing the FgMDM framework, which is a type of Riemannian geometry-based classifier.

Functional connectivity of major depression disorder using ongoing EEG during music perception

OBJECTIVE

The functional connectivity (FC) of major depression disorder (MDD) has not been well studied under naturalistic and continuous stimuli conditions. In this study, we investigated the frequency-specific FC of MDD patients exposed to conditions of music perception using ongoing electroencephalogram (EEG).

METHODS

First, we applied the phase lag index (PLI) method to calculate the connectivity matrices and graph theory-based methods to measure the topology of brain networks across different frequency bands. Then, classification methods were adopted to identify the most discriminate frequency band for the diagnosis of MDD.

RESULTS

During music perception, MDD patients exhibited a decreased connectivity pattern in the delta band but an increased connectivity pattern in the beta band. Healthy people showed a left hemisphere-dominant phenomenon, but MDD patients did not show such a lateralized effect. Support vector machine (SVM) achieved the best classification performance in the beta frequency band with an accuracy of 89.7%, sensitivity of 89.4% and specificity of 89.9%.

CONCLUSIONS

MDD patients exhibited an altered FC in delta and beta bands, and the beta band showed a superiority in the diagnosis of MDD.

SIGNIFICANCE

Our study provided a promising reference for the diagnosis of MDD, and revealed a new perspective for understanding the topology of MDD brain networks during music perception.

Experimental evaluation of methods for real-time EEG phase-specific transcranial magnetic stimulation

OBJECTIVE

Real-time approaches for transcranial magnetic stimulation (TMS) based on a specific EEG phase are a promising avenue for more precise neuromodulation interventions. However, optimal approaches to reliably extract the EEG phase in a frequency band of interest to inform TMS are still to be identified. Here, we implement a new real-time phase detection method for closed-loop EEG-TMS for robust phase extraction. We compare this algorithm with state-of-the-art methods and evaluate its performance both in silico and experimentally.

APPROACH

We propose a new robust algorithm (Educated Temporal Prediction) for delivering real-time EEG phase-specific stimulation based on short prerecorded EEG training data. This method estimates the interpeak period from a training period and applies a bias correction to predict future peaks. We compare the accuracy and computation speed of the ETP algorithm with two existing methods (Fourier based, Autoregressive Prediction) using prerecorded resting EEG data and real-time experiments.

MAIN RESULTS

We found that Educated Temporal Prediction performs with higher accuracy than Fourier-based or Autoregressive methods both in silico and in vivo while being computationally more efficient. Further, we document the dependency of the EEG signal-to-noise ratio (SNR) on algorithm accuracy across all algorithms.

SIGNIFICANCE

Our results give important insights for real-time EEG-TMS technical development as well as experimental design. Due to its robustness and computational efficiency, our method can find broad use in experimental research or clinical applications. Through open sharing of code for all three methods, we enable broad access of TMS-EEG real-time algorithms to the community.