Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

Electrophysiological functional connectivity and complexity reflecting cognitive processing speed heterogeneity in young children with ADHD

Early intervention is imperative for young children with attention-deficit/hyperactivity disorder (ADHD) who manifest heterogeneous neurocognitive deficits. The study investigated the functional connectivity and complexity of brain activity among young children with ADHD exhibiting a fast cognitive processing speed (ADHD-F, n = 26), with ADHD exhibiting a slow cognitive processing speed (ADHD-S, n = 17), and typically developing children (n = 35) using wireless electroencephalography (EEG) during rest and task conditions. During rest, compared with the typically developing group, the ADHD-F group displayed lower long-range intra-hemispheric connectivity, while the ADHD-S group had lower frontal beta inter-hemispheric connectivity. During task performance, the ADHD-S group displayed lower frontal beta inter-hemispheric connectivity than the typically developing group. The ADHD-S group had lower frontal inter-hemispheric connectivity in broader frequency bands than the ADHD-F group, indicating ADHD heterogeneity in mental processing speed. Regarding complexity, the ADHD-S group tended to show lower frontal entropy estimators than the typically developing group during the task condition. These findings suggest that the EEG profile of brain connectivity and complexity can aid the early clinical diagnosis of ADHD, support subgrouping young children with ADHD based on cognitive processing speed heterogeneity, and may contain specific novel neural biomarkers for early intervention planning.

Abnormal Electroencephalogram Findings and Its Correlation With Clinical Features From Pediatric Patients in Psychiatric Clinic

Objective: We aimed to evaluate the occurrence of electroencephalogram (EEG) abnormalities in pediatric patients attending an outpatient psychiatry clinic at a tertiary center. We examined the rates of abnormalities and specific findings based on demographics, specific diagnoses, and clinical severity. Methods: This study included pediatric patients who underwent EEG at the outpatient psychiatry clinic. Patient demographics, psychiatric diagnosis, intellectual disability, intelligent quotient (IQ) score, family history of psychiatric disorders, and Clinical Global Impression-Severity (CGI-S) score were obtained through retrospective electronic health record analysis. The rate of EEG abnormalities was calculated, and specific abnormal findings were reviewed. Relationships between the rate of EEG abnormalities and diagnosis, severity, IQ, and age at EEG examination were analyzed. Results: Of 319 patients who underwent EEG, 21.3% (68 patients) of patients exhibited abnormalities, including background abnormalities (14.7%, 47 patients), interictal epileptiform discharges (IEDs) (10.3%, 33 patients), and a slow posterior dominant rhythm (3.8%, 10 patients). The frontal region was the most commonly affected area. Neurodevelopmental disorders (NDDs) had the most frequent abnormalities (29.8%), followed by anxiety (16.7%), sleep (14.3%), mood (11.7%), psychotic (5%), and conduct disorders (0%). Disease severity did not correlate with the rate of EEG abnormalities. Adjusted for age, sex, severity, and family history, patients with EEG abnormalities exhibited lower IQ scores. Conclusion: EEG abnormalities were common in pediatric patients with psychiatric disorders, with background abnormalities detected as frequently as IEDs. Disease severity was not associated with EEG abnormality, while IQ scores showed a negative correlation.

Clinical Implication of Maumgyeol Basic Biotypes-Electroencephalography- and Photoplethysmogram-Based Bwave State Inventory

OBJECTIVE

The development of individual subtypes based on biomarkers offers a cost-effective and timely avenue to comprehending individual differences pertaining to mental health, independent from individuals' subjective insights. Incorporating 2-channel electroencephalography (EEG) and photoplethysmogram (PPG), we sought to establish a subtype classification system with clinical relevance.

METHODS

One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were determined using the EEG and PPG data from 2,278 individuals without mental disorders, serving to classify subtypes in our sample of 199 participants. Multivariate analysis of variance was applied to examine psychological distinctions among these subtypes. K-means clustering was employed to verify the classification system.

RESULTS

The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive abilities were contingent upon brain subtypes, while mind subtypes exhibited significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. The synergistic assessment of both brain and mind subtypes was also explored.

CONCLUSION

Our subtype classification system offers a concise means to access individuals' mental health. The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.

Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies

Noninvasive brain stimulation (NIBS) treatments have gained considerable attention as potential therapeutic intervention for psychiatric disorders. The identification of reliable biomarkers for predicting clinical response to NIBS has been a major focus of research in recent years. Neuroimaging techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (MRI), have been used to identify potential biomarkers that could predict response to NIBS. However, identifying clinically actionable brain biomarkers requires robustness. In this systematic review, we aimed to summarize the current state of brain biomarker research for NIBS in depression, focusing only on well-powered studies (N ≥ 88) and/or studies that aimed at independently replicating previous findings, either successfully or unsuccessfully. A total of 220 studies were initially identified, of which 18 MRI studies and 18 EEG studies met the inclusion criteria. All focused on repetitive transcranial magnetic stimulation treatment in depression. After reviewing the included studies, we found the following MRI and EEG biomarkers to be most robust: 1) functional MRI-based functional connectivity between the dorsolateral prefrontal cortex and subgenual anterior cingulate cortex, 2) functional MRI-based network connectivity, 3) task-induced EEG frontal-midline theta, and 4) EEG individual alpha frequency. Future prospective studies should further investigate the clinical actionability of these specific EEG and MRI biomarkers to bring biomarkers closer to clinical reality.

Evidentiary Significance of Routine EEG in Refractory Cases: A Paradigm Shift in Psychiatry

Over the past decade, the Diagnostic and Statistical Manual's method of prescribing medications based on presenting symptoms has been challenged. The shift toward precision medicine began with the National Institute of Mental Health and culminated with the World Psychiatric Association's posit that a paradigm shift is needed. This study supports that shift by providing evidence explaining the high rate of psychiatric medication failure and suggests a possible first step toward precision medicine. A large psychiatric practice began collecting electroencephalograms (EEGs) for this study in 2012. The EEGs were analyzed by the same neurophysiologist (board certified in electroencephalography) on 1,233 patients. This study identified 4 EEG biomarkers accounting for medication failure in refractory patients: focal slowing, spindling excessive beta, encephalopathy, and isolated epileptiform discharges. Each EEG biomarker suggests underlying brain dysregulation, which may explain why prior medication attempts have failed. The EEG biomarkers cannot be identified based on current psychiatric assessment methods, and depending upon the localization, intensity, and duration, can all present as complex behavioral or psychiatric issues. The study highlights that the EEG biomarker identification approach can be a positive step toward personalized medicine in psychiatry, furthering the clinical thinking of "testing the organ we are trying to treat."

Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals' responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.

Associations between psychotropic drugs and rsEEG connectivity and network characteristics: a cross-sectional study in hospital-admitted psychiatric patients

Introduction

Resting-state EEG (rsEEG) characteristics, such as functional connectivity and network topology, are studied as potential biomarkers in psychiatric research. However, the presence of psychopharmacological treatment in study participants poses a potential confounding factor in biomarker research. To address this concern, our study aims to explore the impact of both single and multi-class psychotropic treatments on aforementioned rsEEG characteristics in a psychiatric population.

Methods

RsEEG was analyzed in a real-world cross-sectional sample of 900 hospital-admitted psychiatric patients. Patients were clustered into eight psychopharmacological groups: unmedicated, single-class treatment with antipsychotics (AP), antidepressants (AD) or benzodiazepines (BDZ), and multi-class combinations of these treatments. To assess the associations between psychotropic treatments and the macroscale rsEEG characteristics mentioned above, we employed a general linear model with post-hoc tests. Additionally, Spearman's rank correlation analyses were performed to explore potential dosage effects.

Results

Compared to unmedicated patients, single-class use of AD was associated with lower functional connectivity in the delta band, while AP was associated with lower functional connectivity in both the delta and alpha bands. Single-class use of BDZ was associated with widespread rsEEG differences, including lower functional connectivity across frequency bands and a different network topology within the beta band relative to unmedicated patients. All of the multi-class groups showed associations with functional connectivity or topology measures, but effects were most pronounced for concomitant use of all three classes of psychotropics. Differences were not only observed in comparison with unmedicated patients, but were also evident in comparisons between single-class, multi-class, and single/multi-class groups. Importantly, multi-class associations with rsEEG characteristics were found even in the absence of single-class associations, suggesting potential cumulative or interaction effects of different classes of psychotropics. Dosage correlations were only found for antipsychotics.

Conclusion

Our exploratory, cross-sectional study suggests small but significant associations between single and multi-class use of antidepressants, antipsychotics and benzodiazepines and macroscale rsEEG functional connectivity and network topology characteristics. These findings highlight the importance of considering the effects of specific psychotropics, as well as their interactions, when investigating rsEEG biomarkers in a medicated psychiatric population.

Resting-state EEG as a potential indicator to predict sleep quality in depressive patients

OBJECTIVES

To investigate the changes in sleep quality, heart rate variability (HRV) and resting-state electroencephalogram (rsEEG) in patients with major depressive disorder (MDD), and to explore whether HRV and rsEEG may be served as more convenient tools to assess sleep quality in MDD patients.

METHOD

We included a total of 91 subjects (46 healthy controls and 45 MDD patients) and compared their sleep quality, HRV and power spectra of rsEEG. Correlation analyses were conducted to discuss the relationship between HRV and seven factors of PSQI. Multiple linear regression model was used to examine whether absolute band power could predict sleep quality in MDD patients.

RESULTS

We found higher PSQI scores and lower levels of HRV in depressive individuals compared with healthy controls. In MDD patients, sleep latency was negatively correlated with RMSSD and HF. Delta, theta, and alpha band power of rsEEG were higher in MDD patients. Regression analyses showed delta band power of TP8, as well as theta, alpha band power of AF3 predicts PSQI score of MDD patients.

CONCLUSIONS

The findings of our study show that some aspects of sleep problems had negative correlations with parasympathetic activity and the regression model supports that the band power of rsEEG may be used as a potential indicator to evaluate the sleep quality in MDD patients.

SIGNIFICANCE

Cortical hyperarousal may be one of the reasons leading to poor sleep quality of MDD patients. And resting-state EEG can be used as a potential indicator for clinical assessment of MDD patients' sleep quality.

rTMS in mental health disorders

Transcranial magnetic stimulation (TMS) is an innovative and non-invasive technique used in the diagnosis and treatment of psychiatric and neurological disorders. Repetitive TMS (rTMS) can modulate neuronal activity, neuroplasticity and arousal of the waking and sleeping brain, and, more generally, overall mental health. Numerous studies have examined the predictors of the efficacy of rTMS on clinical outcome variables in various psychiatric disorders. These predictors often encompass the stimulated brain region's location, electroencephalogram (EEG) activity patterns, potential morphological and neurophysiological anomalies, and individual patient's response to treatment. Most commonly, rTMS is used in awake patients with depression, catatonia, and tinnitus. Interestingly, rTMS has also shown promise in inducing slow-wave oscillations in insomnia patients, opening avenues for future research into the potential beneficial effects of these oscillations on reports of non-restorative sleep. Furthermore, neurophysiological measures emerge as potential, disease-specific biomarkers, aiding in predicting treatment response and monitoring post-treatment changes. The study posits the convergence of neurophysiological biomarkers and individually tailored rTMS treatments as a gateway to a new era in psychiatric care. The potential of rTMS to induce slow-wave activity also surfaces as a significant contribution to personalized treatment approaches. Further investigations are called for to validate the imaging and electrophysiological biomarkers associated with rTMS. In conclusion, the potential for rTMS to significantly redefine treatment strategies through personalized approaches could enhance the outcomes in neuropsychiatric disorders.

Exploration of electroencephalogram response to MPH treatment in ADHD patients

Attention-deficit/hyperactivity disorder (ADHD) is known to be associated with several diagnostic resting-state electroencephalography (EEG) patterns, including the theta/beta ratio, but no objective predictive markers for each medication. In this study, we explored EEG markers with which the therapeutic efficacy of medications could be estimated at the 1st clinical visit. Thirty-two ADHD patients and thirty-one healthy subjects participated in this study. EEG was recorded during eyes-closed resting conditions, and ADHD symptoms were scored before and after the therapeutic intervention (8 ± 2 weeks). Although comparing EEG patterns between ADHD patients and healthy subjects showed significant differences, EEG dynamics, e.g., theta/beta ratio, in ADHD patients before and after MPH treatment were not significantly different despite improvements in ADHD symptoms. We demonstrated that MPH good responders and poor responders, defined by the efficacy of MPH, had significantly different theta band power in right temporal areas, alpha in left occipital and frontal areas, and beta in left frontal areas. Moreover, we showed that MPH good responders had significant improvements toward normalization in several coherence measures after MPH treatment. Our study implies the possibility of these EEG indices as predictive markers for ADHD therapeutic efficacy.

For Which Children with ADHD is TBR Neurofeedback Effective? Comorbidity as a Moderator

We examined psychiatric comorbidities moderation of a 2-site double-blind randomized clinical trial of theta/beta-ratio (TBR) neurofeedback (NF) for attention deficit hyperactivity disorder (ADHD). Seven-to-ten-year-olds with ADHD received either NF (n = 84) or Control (n = 58) for 38 treatments. Outcome was change in parent-/teacher-rated inattention from baseline to end-of-treatment (acute effect), and 13-month-follow-up. Seventy percent had at least one comorbidity: oppositional defiant disorder (ODD) (50%), specific phobias (27%), generalized anxiety (23%), separation anxiety (16%). Comorbidities were grouped into anxiety alone (20%), ODD alone (23%), neither (30%), or both (27%). Comorbidity (p = 0.043) moderated acute effect; those with anxiety-alone responded better to Control than to TBR NF (d = - 0.79, CI - 1.55- - 0.04), and the other groups showed a slightly better response to TBR NF than to Control (d = 0.22 ~ 0.31, CI - 0.3-0.98). At 13-months, ODD-alone group responded better to NF than Control (d = 0.74, CI 0.05-1.43). TBR NF is not indicated for ADHD with comorbid anxiety but may benefit ADHD with ODD.Clinical Trials Identifier: NCT02251743, date of registration: 09/17/2014.

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.

Electrophysiological and Clinical Predictors of Methylphenidate, Guanfacine, and Combined Treatment Outcomes in Children With Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

The combination of d-methylphenidate and guanfacine (an α-2A agonist) has emerged as a potential alternative to either monotherapy in children with attention-deficit/hyperactivity disorder (ADHD), but it is unclear what predicts response to these treatments. This study is the first to investigate pretreatment clinical and electroencephalography (EEG) profiles as predictors of treatment outcome in children randomized to these different medications.

METHOD

A total of 181 children with ADHD (aged 7-14 years; 123 boys) completed an 8-week randomized, double-blind, comparative study with d-methylphenidate, guanfacine, or combined treatments. Pretreatment assessments included ratings on ADHD, anxiety, and oppositional behavior. EEG activity from cortical sources localized within midfrontal and midoccipital regions was measured during a spatial working memory task with encoding, maintenance, and retrieval phases. Analyses tested whether pretreatment clinical and EEG measures predicted treatment-related change in ADHD severity.

RESULTS

Higher pretreatment hyperactivity-impulsivity and oppositional symptoms and lower anxiety predicted greater ADHD improvements across all medication groups. Pretreatment event-related midfrontal beta power predicted treatment outcome with combined and monotherapy treatments, albeit in different directions. Weaker beta modulations predicted improvements with combined treatment, whereas stronger modulation during encoding and retrieval predicted improvements with d-methylphenidate and guanfacine, respectively. A multivariate model including EEG and clinical measures explained twice as much variance in ADHD improvement with guanfacine and combined treatment (R2= 0.34-0.41) as clinical measures alone (R2 = 0.14-.21).

CONCLUSION

We identified treatment-specific and shared predictors of response to different pharmacotherapies in children with ADHD. If replicated, these findings would suggest that aggregating information from clinical and brain measures may aid personalized treatment decisions in ADHD.

CLINICAL TRIAL REGISTRATION INFORMATION

Single Versus Combination Medication Treatment for Children With Attention Deficit Hyperactivity Disorder; https://clinicaltrials.gov; NCT00429273.

Analysis two types of K complexes on the human EEG based on classical continuous wavelet transform

In our work, we compare EEG time-frequency features for two types of K-complexes detected in volunteers performing the monotonous psychomotor test with their eyes closed. Type I K-complexes preceded spontaneous awakenings, while after type II K-complexes, subjects continued to sleep at least for 10 s after. The total number of K-complexes in the group of 18 volunteers was 646, of which of which type I K-complexes was 150 and type II K-complexes was 496. Time-frequency analysis was performed using continuous wavelet transform. EEG wavelet spectral power was averaged upon several brain zones for each of the classical frequency ranges (slow wave, δ, θ, α, β₁, β₂, γ bands). The low-frequency oscillatory activity ( δ-band) preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere. Statistically significant differences were obtained by averaging over the left and right hemispheres, as well as projections of the motor area of the brain, p<0.05. The maximal differences between the types I and II of K-complexes were demonstrated in δ-, θ-bands in the occipital and posterior temporal regions. The high amplitude of the motor cortex projection response in β₂-band, [20;30] Hz, related to the sensory-motor modality of task in monotonous psychomotor test. The δ-oscillatory activity preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere may be due to the important role of the left hemisphere in spontaneous awakening from sleep during monotonous work, which is an interesting issue for future research.

Brainmarker-I Differentially Predicts Remission to Various Attention-Deficit/Hyperactivity Disorder Treatments: A Discovery, Transfer, and Blinded Validation Study

BACKGROUND

Attention-deficit/hyperactivity disorder is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates.

METHODS

The biomarker in this study was developed in a heterogeneous clinical sample (N = 4249) and first applied to two large transfer datasets, a priori stratifying young males (<18 years) with a higher individual alpha peak frequency (iAPF) to methylphenidate (N = 336) and those with a lower iAPF to multimodal neurofeedback complemented with sleep coaching (N = 136). Blinded, out-of-sample validations were conducted in two independent samples. In addition, the association between iAPF and response to guanfacine and atomoxetine was explored.

RESULTS

Retrospective stratification in the transfer datasets resulted in a predicted gain in normalized remission of 17% to 30%. Blinded out-of-sample validations for methylphenidate (n = 41) and multimodal neurofeedback (n = 71) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively.

CONCLUSIONS

This study introduces a clinically interpretable and actionable biomarker based on the iAPF assessed during resting-state electroencephalography. Our findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.

Vigilance described by the time-on-task effect in EEG activity during a cued Go/NoGo task

Vigilance refers to the ability to maintain attention and to remain alert to stimuli in prolonged and monotonous tasks. Vigilance decrement describes the decline in performance in the course of such sustained attention tasks. Time-related alterations in attention have been found to be associated with changes in EEG. We investigated these time-on-task effects on the basis of changes in the conventional EEG spectral bands with the aim of finding a compound measure of vigilance. 148 healthy adults performed a cued Go/NoGo task that lasted approximately 21 min. Behavioural performance was examined by comparing the number of errors in the first and last quarters of the task using paired t-test. EEG data were epoched per trial, and time-on-task effects were modelled by using multiple linear regression, with frequency spectra band power values as independent variables and trial number as the dependent variable. Behavioural performance decreased in terms of omission errors only. Performance of the models, expressed by predicted R-squared, was between 0.10 and 0.27, depending on the particular task condition. The time-on-task EEG spectral changes were characterized by broad changes in the alpha and frontal changes in the beta and gamma bands. We were able to identify a set of EEG spectral features that predict time-on-task. Our output is considered to be a measure of vigilance, reflecting the allocation of mental resources for the maintenance of attention.

A Predictive Biomarker Model Using Quantitative Electroencephalography in Adolescent Major Depressive Disorder

Background: With evolving understanding of psychiatric diagnosis and treatment, demand for biomarkers for psychiatric disorders in children and adolescents has grown dramatically. This study utilized quantitative electroencephalography (qEEG) to develop a predictive model for adolescent major depressive disorder (MDD). We hypothesized that youth with MDD compared to healthy controls (HCs) could be differentiated using a singular logistic regression model that utilized qEEG data alone. Methods: qEEG data and psychometric measures were obtained in adolescents aged 14-17 years with MDD (n = 35) and age- and gender-matched HCs (n = 14). qEEG in four frequency bands (alpha, beta, theta, and delta) was collected and coherence, cross-correlation, and power data streams obtained. A two-stage analytical framework was then used to develop the final logistic regression model, which was then evaluated using a receiver-operating characteristic curve (ROC) analysis. Results: Within the initial analysis, six qEEG dyads (all coherence) had significant predictive values. Within the final biomarkers, just four predictors, including F3-C3 (R frontal) alpha coherence, P3-O1 (R parietal) theta coherence, CZ-PZ (central) beta coherence, and P8-O2 (L parietal occipital) theta power were used in the final model, which yielded an ROC area of 0.8226. Conclusions: We replicated our previous findings of qEEG differences between adolescents and HCs and successfully developed a single-value predictive model with a robust ROC area. Furthermore, the brain areas involved in behavioral disinhibition and resting state/default mode networks were again shown to be involved in the observed differences. Thus, qEEG appears to be a potential low-cost and effective intermediate biomarker for MDD in youth.

Treatment biomarkers for ADHD: Taking stock and moving forward

The development of treatment biomarkers for psychiatric disorders has been challenging, particularly for heterogeneous neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD). Promising findings are also rarely translated into clinical practice, especially with regard to treatment decisions and development of novel treatments. Despite this slow progress, the available neuroimaging, electrophysiological (EEG) and genetic literature provides a solid foundation for biomarker discovery. This article gives an updated review of promising treatment biomarkers for ADHD which may enhance personalized medicine and novel treatment development. The available literature points to promising pre-treatment profiles predicting efficacy of various pharmacological and non-pharmacological treatments for ADHD. These candidate predictive biomarkers, particularly those based on low-cost and non-invasive EEG assessments, show promise for the future stratification of patients to specific treatments. Studies with repeated biomarker assessments further show that different treatments produce distinct changes in brain profiles, which track treatment-related clinical improvements. These candidate monitoring/response biomarkers may aid future monitoring of treatment effects and point to mechanistic targets for novel treatments, such as neurotherapies. Nevertheless, existing research does not support any immediate clinical applications of treatment biomarkers for ADHD. Key barriers are the paucity of replications and external validations, the use of small and homogeneous samples of predominantly White children, and practical limitations, including the cost and technical requirements of biomarker assessments and their unknown feasibility and acceptability for people with ADHD. We conclude with a discussion of future directions and methodological changes to promote clinical translation and enhance personalized treatment decisions for diverse groups of individuals with ADHD.

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Antecedent

The event-related potential (ERP) components P300 and mismatch negativity (MMN) have been linked to cognitive deficits in patients with schizophrenia. The diagnosis of schizophrenia could be improved by applying machine learning procedures to these objective neurophysiological biomarkers. Several studies have attempted to achieve this goal, but no study has examined Multiple Kernel Learning (MKL) classifiers. This algorithm finds optimally a combination of kernel functions, integrating them in a meaningful manner, and thus could improve diagnosis.

Objective

This study aimed to examine the efficacy of the MKL classifier and the Boruta feature selection method for schizophrenia patients (SZ) and healthy controls (HC) single-subject classification.

Methods

A cohort of 54 SZ and 54 HC participants were studied. Three sets of features related to ERP signals were calculated as follows: peak related features, peak to peak related features, and signal related features. The Boruta algorithm was used to evaluate the impact of feature selection on classification performance. An MKL algorithm was applied to address schizophrenia detection.

Results

A classification accuracy of 83% using the whole dataset, and 86% after applying Boruta feature selection was obtained. The variables that contributed most to the classification were mainly related to the latency and amplitude of the auditory P300 paradigm.

Conclusion

This study showed that MKL can be useful in distinguishing between schizophrenic patients and controls when using ERP measures. Moreover, the use of the Boruta algorithm provides an improvement in classification accuracy and computational cost.

Task-Rate-Related Neural Dynamics Using Wireless EEG to Assist Diagnosis and Intervention Planning for Preschoolers with ADHD Exhibiting Heterogeneous Cognitive Proficiency

This study used a wireless EEG system to investigate neural dynamics in preschoolers with ADHD who exhibited varying cognitive proficiency pertaining to working memory and processing speed abilities. Preschoolers with ADHD exhibiting high cognitive proficiency (ADHD-H, n = 24), those with ADHD exhibiting low cognitive proficiency (ADHD-L, n = 18), and preschoolers with typical development (TD, n = 31) underwent the Conners’ Kiddie Continuous Performance Test and wireless EEG recording under different conditions (rest, slow-rate, and fast-rate task). In the slow-rate task condition, compared with the TD group, the ADHD-H group manifested higher delta and lower beta power in the central region, while the ADHD-L group manifested higher parietal delta power. In the fast-rate task condition, in the parietal region, ADHD-L manifested higher delta power than those in the other two groups (ADHD-H and TD); additionally, ADHD-L manifested higher theta as well as lower alpha and beta power than those with ADHD-H. Unlike those in the TD group, the delta power of both ADHD groups was enhanced in shifting from rest to task conditions. These findings suggest that task-rate-related neural dynamics contain specific neural biomarkers to assist clinical planning for ADHD in preschoolers with heterogeneous cognitive proficiency. The novel wireless EEG system used was convenient and highly suitable for clinical application.

Characteristics of single-channel electroencephalogram in depression during conversation with noise reduction technology

Background

Previous studies have attempted to characterize depression using electroencephalography (EEG), but results have been inconsistent. New noise reduction technology allows EEG acquisition during conversation.

Methods

We recorded EEG from 40 patients with depression as they engaged in conversation using a single-channel EEG device while conducting real-time noise reduction and compared them to those of 40 healthy subjects. Differences in EEG between patients and controls, as well as differences in patients’ depression severity, were examined using the ratio of the power spectrum at each frequency. In addition, the effects of medications were examined in a similar way.

Results

In comparing healthy controls and depression patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In the patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. In addition, significant differences were observed at multiple frequencies when comparing patients who did and did not take antidepressants, antipsychotics, and/or benzodiazepines. However, the power spectra still remained significantly different between non-medicated patients and healthy individuals.

Limitations

The small sample size may have caused Type II error. Patients’ demographic characteristics varied. Moreover, most patients were taking various medications, and cannot be compared to the non-medicated control group.

Conclusion

A study with a larger sample size should be conducted to gauge reproducibility, but the methods used in this study could be useful in clinical practice as a biomarker of depression.

Isolated epileptiform activity in children and adolescents: prevalence, relevance, and implications for treatment

In the field of psychiatry diagnoses are primarily based on the report of symptoms from either the patient, parents, or both, and a psychiatrist's observations. A psychiatric diagnosis is currently the most widely used basis for medication selection and the brain is seldom investigated directly as a source of those symptoms. This study addresses the request from the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC) for scientific research into neurological abnormalities that can be linked to psychiatric symptoms for the purpose of predicting medication response. One such neurological abnormality that has been the focus of many studies over the last three decades is isolated epileptiform discharges (IEDs) in children and adolescents without seizures. We conducted a systematic review of the literature to determine prevalence rates of IEDs within diagnostic categories. We then compared the prevalence of IEDs in the selected literature to our IRB-approved data archive. Our study found a consistent high prevalence of IEDs specifically for ADHD (majority > 25%) and ASD (majority > 59%), and consistent low prevalence rates were found for Depression (3%). If children and adolescents have failed multiple medication attempts, and more than one-third of them have IEDs, then an EEG would be justified within the RDoC paradigm.

Investigating the Effects of Auditory and Vibrotactile Rhythmic Sensory Stimulation on Depression: An EEG Pilot Study

Background

Major depressive disorder (MDD) is a persistent psychiatric condition and one of the leading causes of global disease burden. In a previous study, we investigated the effects of a five-week intervention consisting of rhythmic gamma frequency (30-70 Hz) vibroacoustic stimulation in 20 patients formally diagnosed with MDD. In that study, the findings suggested a significant clinical improvement in depression symptoms as measured using the Montgomery-Asberg Depression Rating Scale (MADRS), with 37% of participants meeting the criteria for clinical response. The goal of the present research was to examine possible changes from baseline to posttreatment in resting-state electroencephalography (EEG) recordings using the same treatment protocol and to characterize basic changes in EEG related to treatment response.

Materials and methods

The study sample consisted of 19 individuals aged 18-70 years with a clinical diagnosis of MDD. The participants were assessed before and after a five-week treatment period, which consisted of listening to an instrumental musical track on a vibroacoustic device, delivering auditory and vibrotactile stimulus in the gamma-band range (30-70 Hz, with particular emphasis on 40 Hz). The primary outcome measure was the change in Montgomery-Asberg Depression Rating Scale (MADRS) from baseline to posttreatment and resting-state EEG.

Results

Analysis comparing MADRS score at baseline and post-intervention indicated a significant change in the severity of depression symptoms after five weeks (t = 3.9923, df = 18, p = 0.0009). The clinical response rate was 36.85%. Resting-state EEG power analysis revealed a significant increase in occipital alpha power (t = -2.149, df = 18, p = 0.04548), as well as an increase in the prefrontal gamma power of the responders (t = 2.8079, df = 13.431, p = 0.01442).

Conclusions

The results indicate that improvements in MADRS scores after rhythmic sensory stimulation (RSS) were accompanied by an increase in alpha power in the occipital region and an increase in gamma in the prefrontal region, thus suggesting treatment effects on cortical activity in depression. The results of this pilot study will help inform subsequent controlled studies evaluating whether treatment response to vibroacoustic stimulation constitutes a real and replicable reduction of depressive symptoms and to characterize the underlying mechanisms.

Stratified psychiatry: Tomorrow's precision psychiatry?

Here we review the paradigm-change from one-size-fits-all psychiatry to more personalized-psychiatry, where we distinguish between 'precision psychiatry' and 'stratified psychiatry'. Using examples in Depression and ADHD we argue that stratified psychiatry, using biomarkers to facilitate patients to best 'on-label' treatments, is a more realistic future for implementing biomarkers in clinical practice.

Acute electroencephalography responses during incremental exercise in those with mental illness

INTRODUCTION

Depression is a mental illness (MI) characterized by a process of behavioral withdrawal whereby people experience symptoms including sadness, anhedonia, demotivation, sleep and appetite change, and cognitive disturbances. Frontal alpha asymmetry (FAA) differs in depressive populations and may signify affective responses, with left FAA corresponding to such aversive or withdrawal type behavior. On an acute basis, exercise is known to positively alter affect and improve depressive symptoms and this has been measured in conjunction with left FAA as a post-exercise measure. It is not yet known if these affective electroencephalography (EEG) responses to exercise occur during exercise or only after completion of an exercise bout. This study therefore aimed to measure EEG responses during exercise in those with MI.

MATERIALS AND METHODS

Thirty one participants were allocated into one of two groups; those undergoing management of a mental health disorder (MI; N = 19); or reporting as apparently healthy (AH; N = 12). EEG responses at rest and during incremental exercise were measured at the prefrontal cortex (PFC) and the motor cortex (MC). EEG data at PFC left side (F3, F7, FP1), PFC right side (F4, F8, FP2), and MC (C3, Cz, and C4) were analyzed in line with oxygen uptake at rest, 50% of ventilatory threshold (VT) (50% VT) and at VT.

RESULTS

EEG responses increased with exercise across intensity from rest to 50% VT and to VT in all bandwidths (P < 0.05) for both groups. There were no significant differences in alpha activity responses between groups. Gamma responses in the PFC were significantly higher in MI on the left side compared to AH (P < 0.05).

CONCLUSION

Alpha activity responses were no different between groups at rest or any exercise intensity. Therefore the alpha activity response previously shown post-exercise was not found during exercise. However, increased PFC gamma activity in the MI group adds to the body of evidence showing increased gamma can differentiate between those with and without MI.

Effect of transcranial magnetic stimulation as an enhancer of cognitive stimulation sessions on mild cognitive impairment: Preliminary results

Mild cognitive impairment (MCI) is a state between normal cognition and dementia. Currently, there is little evidence of repetitive Transcranial Magnetic Stimulation (rTMS) as an enhancing tool for Cognitive Stimulation (CS) on MCI. The importance of this study consists in its assessment of the enhancing effect of rTMS on CS in 22 MCI patients randomized and divided into two group: active (AG) and sham (SG). Diagnoses and assessments were determined during 30 sessions over a 10-week period by Montreal Cognitive Assessment (MoCA) and Neuropsí test. Results were statistically significant in the intergroup analysis with MoCA and intragroup only for AG.

Neural Dynamics for Facilitating ADHD Diagnosis in Preschoolers: Central and Parietal Delta Synchronization in the Kiddie Continuous Performance Test

The present study aimed to characterize children at risk of attention-deficit/hyperactivity disorder (ADHD) during preschool age and provide early intervention. The continuous performance test (CPT) and electroencephalography (EEG) can contribute additional valuable information to facilitate diagnosis. This study measured brain dynamics at slow and fast task rates in the CPT using a wireless wearable EEG and identified correlations between the EEG and CPT data in preschool children with ADHD. Forty-nine preschool children participated in this study, of which 29 were diagnosed with ADHD and 20 exhibited typical development (TD). The Conners Kiddie Continuous Performance Test (K-CPT) and wireless wearable EEG recordings were employed simultaneously. Significant differences were observed between the groups with ADHD and TD in task-related EEG spectral powers (central as well as parietal delta, P < 0.01), which were distinct only in the slow-rate task condition. A shift from resting to the CPT task condition induced overall alpha powers decrease in the ADHD group. In the task condition, the delta powers were positively correlated with the CPT perseveration scores, whereas the alpha powers were negatively correlated with specific CPT scores mainly on perseveration and detectability (P < 0.05). These results, which complement the findings of other sparse studies that have investigated within-task-related brain dynamics, particularly in preschool children, can assist specialists working in early intervention to plan training and educational programs for preschoolers with ADHD.

Feasibility of Repeated Assessment of Cognitive Function in Older Adults Using a Wireless, Mobile, Dry-EEG Headset and Tablet-Based Games

Access to affordable, objective and scalable biomarkers of brain function is needed to transform the healthcare burden of neuropsychiatric and neurodegenerative disease. Electroencephalography (EEG) recordings, both resting and in combination with targeted cognitive tasks, have demonstrated utility in tracking disease state and therapy response in a range of conditions from schizophrenia to Alzheimer's disease. But conventional methods of recording this data involve burdensome clinic visits, and behavioural tasks that are not effective in frequent repeated use. This paper aims to evaluate the technical and human-factors feasibility of gathering large-scale EEG using novel technology in the home environment with healthy adult users. In a large field study, 89 healthy adults aged 40-79 years volunteered to use the system at home for 12 weeks, 5 times/week, for 30 min/session. A 16-channel, dry-sensor, portable wireless headset recorded EEG while users played gamified cognitive and passive tasks through a tablet application, including tests of decision making, executive function and memory. Data was uploaded to cloud servers and remotely monitored via web-based dashboards. Seventy-eight participants completed the study, and high levels of adherence were maintained throughout across all age groups, with mean compliance over the 12-week period of 82% (4.1 sessions per week). Reported ease of use was also high with mean System Usability Scale scores of 78.7. Behavioural response measures (reaction time and accuracy) and EEG components elicited by gamified stimuli (P300, ERN, Pe and changes in power spectral density) were extracted from the data collected in home, across a wide range of ages, including older adult participants. Findings replicated well-known patterns of age-related change and demonstrated the feasibility of using low-burden, large-scale, longitudinal EEG measurement in community-based cohorts. This technology enables clinically relevant data to be recorded outside the lab/clinic, from which metrics underlying cognitive ageing could be extracted, opening the door to potential new ways of developing digital cognitive biomarkers for disorders affecting the brain.

[EEG predictors of therapeutic response in psychiatry]

The literature review provides data on one of the types of biomarkers - EEG predictors of the therapeutic response of patients with different types of mental pathology. It has been shown that the quantitative parameters of the electroencephalogram (EEG) recorded before the start of the treatment course reflect not only the current functional state of the patient's brain, but also its adaptive resources in terms of the possibility and magnitude of response to therapy. The identified EEG predictors of the therapeutic response in patients with depression, schizophrenia and some other mental disorders have a sufficiently high prognostic ability, sensitivity and specificity in determining responders and non-responders, make it possible to carry out a quantitative prediction of the patient's condition after a course of treatment, and also to assist the clinician in choosing medications for optimal therapy.

Deep learning applied to electroencephalogram data in mental disorders: A systematic review

In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictive purposes in conducting research on mental disorders. EEG-studies on psychiatric diseases based on the ICD-10 or DSM-V classification that used either convolutional neural networks (CNNs) or long -short-term-memory (LSTMs) networks for classification were searched and examined for the quality of the information they contained in three domains: clinical, EEG-data processing, and deep learning. Although we found that the description of EEG acquisition and pre-processing was sufficient in most of the studies, we found, that many of them lacked a systematic characterization of clinical features. Furthermore, many studies used misguided model selection procedures or flawed testing. It is recommended that the study of psychiatric disorders using DL in the future must improve the quality of clinical data and follow state of the art model selection and testing procedures so as to achieve a higher research standard and head toward a clinical significance.

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.

Treatment effect variability in brain stimulation across psychiatric disorders: A meta-analysis of variance

Noninvasive brain stimulation methods such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are promising add-on treatments for a number of psychiatric conditions. Yet, some of the initial excitement is wearing off. Randomized controlled trials (RCT) have found inconsistent results. This inconsistency is suspected to be the consequence of variation in treatment effects and solvable by identifying responders in RCTs and individualizing treatment. However, is there enough evidence from RCTs that patients respond differently to treatment? This question can be addressed by comparing the variability in the active stimulation group with the variability in the sham group. We searched MEDLINE/PubMed and included all double-blinded, sham-controlled RCTs and crossover trials that used TMS or tDCS in adults with a unipolar or bipolar depression, bipolar disorder, schizophrenia spectrum disorder, or obsessive compulsive disorder. In accordance with the PRISMA guidelines to ensure data quality and validity, we extracted a measure of variability of the primary outcome. A total of 130 studies with 5748 patients were considered in the analysis. We calculated variance-weighted variability ratios for each comparison of active stimulation vs sham and entered them into a random-effects model. We hypothesized that treatment effect variability in TMS or tDCS would be reflected by increased variability after active compared with sham stimulation, or in other words, a variability ratio greater than one. Across diagnoses, we found only a minimal increase in variability after active stimulation compared with sham that did not reach statistical significance (variability ratio = 1.03; 95% CI, 0.97, 1.08, P = 0.358). In conclusion, this study found little evidence for treatment effect variability in brain stimulation, suggesting that the need for personalized or stratified medicine is still an open question.

Electroencephalographic Parameters Differentiating Melancholic Depression, Non-melancholic Depression, and Healthy Controls. A Systematic Review

Introduction: The objective of this systematic review was to investigate whether electroencephalographic parameters can serve as a tool to distinguish between melancholic depression, non-melancholic depression, and healthy controls in adults. Methods: A systematic review comprising an extensive literature search conducted in PubMed, Embase, Google Scholar, and PsycINFO in August 2020 with monthly updates until November 1st, 2020. In addition, we performed a citation search and scanned reference lists. Clinical trials that performed an EEG-based examination on an adult patient group diagnosed with melancholic unipolar depression and compared with a control group of non-melancholic unipolar depression and/or healthy controls were eligible. Risk of bias was assessed by the Strengthening of Reporting of Observational Studies in Epidemiology (STROBE) checklist. Results: A total of 24 studies, all case-control design, met the inclusion criteria and could be divided into three subgroups: Resting state studies (n = 5), sleep EEG studies (n = 10), and event-related potentials (ERP) studies (n = 9). Within each subgroup, studies were characterized by marked variability on almost all levels, preventing pooling of data, and many studies were subject to weighty methodological problems. However, the main part of the studies identified one or several EEG parameters that differentiated the groups. Conclusions: Multiple EEG modalities showed an ability to distinguish melancholic patients from non-melancholic patients and/or healthy controls. The considerable heterogeneity across studies and the frequent methodological difficulties at the individual study level were the main limitations to this work. Also, the underlying premise of shifting diagnostic paradigms may have resulted in an inhomogeneous patient population. Systematic Review Registration: Registered in the PROSPERO registry on August 8th, 2020, registration number CRD42020197472.

Using EEG to Predict Clinical Response to Electroconvulsive Therapy in Patients With Major Depression: A Comprehensive Review

Introduction: An important approach to improve the therapeutic effect of electroconvulsive therapy (ECT) may be to early characterize patients who are more likely to respond. Our objective was to explore whether baseline electroencephalography (EEG) settings before the beginning of ECT treatment can predict future clinical response to ECT in patients with depressive disorder. Methods: We conducted a systematic search in the MEDLINE, EMBASE, PsycINFO, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL) databases to identify studies using EEG in adults with depressive disorder treated by ECT. To investigate the predictive value of baseline EEG on clinical outcomes of ECT, we extracted from the retrieved studies and qualitatively described the association between the baseline EEG markers characteristics and the rates of future responders and/or remitters to ECT. Results: The primary search yielded 2,531 potentially relevant citations, and 12 articles were selected according to inclusion criteria. Most of the studies were prospective studies with small sample size. Sociodemographic and clinical characteristics of patients, ECT settings, EEG settings, and outcomes were heterogeneous. Event-related potential (ERP) paradigms were used in three studies, polysomnography was used in three studies, and the six other studies used EEG to measure cerebral connectivity and activity. Conclusions: P300 amplitude, coherence, and connectivity measures were correlated with remission in patients with depression treated by ECT. Sleep EEG recordings seemed not to be correlated with remission after ECT. Further prospective studies with large sample size are needed to determine optimal EEG parameters associated with clinical response to ECT in depressive disorder. Systematic Review Registration: PROSPERO CRD42020181978.

Major Depression and Brain Asymmetry in a Decision-Making Task with Negative and Positive Feedback

Depressed patients are characterized by hypoactivity of the left and hyperactivity of the right frontal areas during the resting state. Depression is also associated with impaired decision-making, which reflects multiple cognitive, affective, and attentional processes, some of which may be lateralized. The aim of this study was to investigate brain asymmetry during a decision-making task performed in negative and positive feedback conditions in patients with Major Depressive Disorder (MDD) in comparison to healthy control participants. The electroencephalogram (EEG) was recorded from 60 MDD patients and 60 healthy participants while performing a multi-stage decision-making task. Frontal, central, and parietal alpha asymmetry were analyzed with EEGlab/ERPlab software. Evoked potential responses (ERPs) showed general lateralization suggestive of an initial right dominance developing into a more complex pattern of asymmetry across different scalp areas as information was processed. The MDD group showed impaired mood prior to performance, and decreased confidence during performance in comparison to the control group. The resting state frontal alpha asymmetry showed lateralization in the healthy group only. Task-induced alpha power and ERP P100 and P300 amplitudes were more informative biomarkers of depression during decision making. Asymmetry coefficients based on task alpha power and ERP amplitudes showed consistency in the dynamical changes during the decision-making stages. Depression was characterized by a lack of left dominance during the resting state and left hypoactivity during the task baseline and subsequent decision-making process. Findings add to understanding of the functional significance of lateralized brain processes in depression.

Individual alpha frequency proximity associated with repetitive transcranial magnetic stimulation outcome: An independent replication study from the ICON-DB consortium

OBJECTIVE

The aim of the current study was to attempt to replicate the finding that the individual alpha frequency (IAF) as well as the absolute difference between IAF and 10 Hz stimulation frequency (IAF-prox) is related to treatment outcome.

METHODS

Correlations were performed to investigate the relationship between IAF-prox and percentage symptom improvement in a sample of 153 patients with major depressive disorder treated with 10 Hz (N = 59) to the left dorsolateral prefrontal cortex (DLPFC) or 1 Hz (N = 94) to the right DLPFC repetitive Transcranial Magnetic Stimulation (rTMS).

RESULTS

There was a significant negative correlation between IAF-prox and the percentage of symptom improvement only for the 10 Hz group. Curve fitting models revealed that there was a quadratic association between IAF and treatment response in the 10 Hz group, with a peak at 10 Hz IAF.

CONCLUSION

The main result of Corlier and colleagues was replicated, and the findings suggest that the distance between 10 Hz stimulation frequency and the IAF may influence clinical outcome in a non-linear manner.

SIGNIFICANCE

rTMS is often administered at a frequency of 10 Hz, which is the center of the EEG alpha frequency band. The results can make a significant contribution to optimizing the clinical application of rTMS.

Predicting Sex From EEG: Validity and Generalizability of Deep-Learning-Based Interpretable Classifier

Explainable artificial intelligence holds a great promise for neuroscience and plays an important role in the hypothesis generation process. We follow-up a recent machine learning-oriented study that constructed a deep convolutional neural network to automatically identify biological sex from EEG recordings in healthy individuals and highlighted the discriminative role of beta-band power. If generalizing, this finding would be relevant not only theoretically by pointing to some specific neurobiological sexual dimorphisms, but potentially also as a relevant confound in quantitative EEG diagnostic practice. To put this finding to test, we assess whether the automatic identification of biological sex generalizes to another dataset, particularly in the presence of a psychiatric disease, by testing the hypothesis of higher beta power in women compared to men on 134 patients suffering from Major Depressive Disorder. Moreover, we construct ROC curves and compare the performance of the classifiers in determining sex both before and after the antidepressant treatment. We replicate the observation of a significant difference in beta-band power between men and women, providing classification accuracy of nearly 77%. The difference was consistent across the majority of electrodes, however multivariate classification models did not generally improve the performance. Similar results were observed also after the antidepressant treatment (classification accuracy above 70%), further supporting the robustness of the initial finding.

A novel quantitative electroencephalography subtype with high alpha power in ADHD: ADHD or misdiagnosed ADHD?

This study investigated quantitative electroencephalography (QEEG) subtypes as auxiliary tools to assess Attention Deficit Hyperactivity Disorder (ADHD). A total of 74 subjects (58 male and 16 female) were assessed using the Korean version of the Diagnostic Interview Schedule for Children Version IV and were assigned to one of three groups: ADHD, ADHD-Not Otherwise specified (NOS), and Neurotypical (NT). We measured absolute and relative EEG power in 19 channels and conducted an auditory continuous performance test. We analyzed QEEG according to the frequency range: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), and beta (13.5–30 Hz). The subjects were then grouped by Ward’s method of cluster analysis using the squared Euclidian distance to measure dissimilarities. We discovered four QEEG clusters, which were characterized by: (a) elevated delta power with less theta activity, (b) elevated slow alpha relative power, (c) elevated theta with deficiencies of alpha and beta relative power, and (d) elevated fast alpha and beta absolute power. The largest proportion of participants in clusters (a) and (c) were from the ADHD group (48% and 47%, respectively). Conversely, group (b) mostly consisted of the participants from the NOS group (59%), while group (d) had the largest proportion of participants from the NT group (62%). These results indicate that children with ADHD does not neurophysiologically constitute a homogenous group. We also identified a new subtype with increased alpha power in addition to those commonly reported in ADHD. Given the QEEG characteristics with increased alpha power, we should consider the possibility that this subtype may be caused by childhood depression. In conclusion, we believe that these QEEG subtypes of ADHD are expected to provide valuable information for accurately diagnosing ADHD.

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.

Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging-like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice.

OBJECTIVE

To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment.

DESIGN, SETTING, AND PARTICIPANTS

In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019.

INTERVENTIONS

Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks.

MAIN OUTCOMES AND MEASURES

Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit.

RESULTS

Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions-most prominently parietal-for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points.

CONCLUSIONS AND RELEVANCE

These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01407094.

Resting-state EEG beta band power predicts quality of life outcomes in patients with depressive disorders: A longitudinal investigation

BACKGROUND

Quality of life is severely impaired in patients with depressive disorders. Previous studies have focused on biomarkers predicting depressive symptomatology; however, studies investigating biomarkers predicting quality of life outcomes are limited. Improving quality of life is important because it is related not only to mental health but also to physical health. We need to develop a biomarker related to quality of life as a therapeutic target for patients with depressive disorders. Resting-state electroencephalography (EEG) is easy to record in clinical settings. The index of bandwidth spectral power predicts treatment response in depressive disorders and thus may be a candidate biomarker. However, no longitudinal studies have investigated whether EEG-recorded power could predict quality of life outcomes in patients with depressive disorders.

METHODS

The resting-state EEG-recorded bandwidth spectral power at baseline and the World Health Organization Quality of Life (QOL)-26 scores at 3-year follow-up were measured in 44 patients with depressive disorders.

RESULTS

The high beta band power (20-30 Hz) at baseline significantly predicted QOL at the 3-year follow-up after considering depressive symptoms and medication effects in a longitudinal investigation in patients with depressive disorders (β = 0.38, p = 0.01).

LIMITATIONS

We did not have healthy subjects as a comparison group in this study.

CONCLUSIONS

Our findings suggest that resting-state beta activity has the potential to be a useful biomarker for predicting future quality of life outcomes in patients with depressive disorders.

Neurocognitive reorganization of emotional processing following a socio-cognitive intervention in Colombian ex-combatants

Ex-combatants often exhibit atypical Emotional Processing (EP) such as reduced emphatic levels and higher aggressive attitudes. Social Cognitive Training (SCT) addressing socio-emotional components powerfully improve social interaction among Colombian ex-combatants. However, with narrow neural evidence, this study offers a new testimony. A sample of 28 ex-combatants from Colombian illegal armed groups took part in this study, split into 15 for SCT and 13 for the conventional program offered by the Governmental Reintegration Route. All of them were assessed before and after the intervention with a protocol that included an EP task synchronized with electroencephalographic recordings. We drew behavioral scores and brain connectivity (Coherency) metrics from task performance. Behavioral scores yielded no significant effects. Increased post-intervention connectivity in the delta band was observed during negative emotional processing only SCT group. Positive emotions exposed distinctive gamma band connectivity that differentiate groups. These results suggest that SCT can trigger covert neurofunctional reorganization in ex-combatants embarked on the reintegration process even when overt behavioral improvements are not yet apparent. Such covert functional changes may be the neural signature of compensatory mechanisms necessary to reshape behaviors adaptively. This novel framework may inspire cutting-edge translational research at the crossing of neuroscience, sociology, and public policy-making.

EEG Microstate Differences in Medicated vs. Medication-Naïve First-Episode Psychosis Patients

There has been considerable interest in the role of synchronous brain activity abnormalities in the pathophysiology of psychotic disorders and their relevance for treatment; one index of such activity are EEG resting-state microstates. These reflect electric field configurations of the brain that persist over 60-120 ms time periods. A set of quasi-stable microstates classes A, B, C, and D have been repeatedly identified across healthy participants. Changes in microstate parameters coverage, duration and occurrence have been found in medication-naïve as well as medicated patients with psychotic disorders compared to healthy controls. However, to date, only two studies have directly compared antipsychotic medication effects on EEG microstates either pre- vs. post-treatment or between medicated and unmedicated chronic schizophrenia patients. The aim of this study was therefore to directly compare EEG resting-state microstates between medicated and medication-naïve (untreated) first-episode (FEP) psychosis patients (mFEP vs. uFEP). We used 19-channel clinical EEG recordings to compare temporal parameters of four prototypical microstate classes (A-D) within an overall sample of 47 patients (mFEP n = 17; uFEP n = 30). The results demonstrated significant decreases of microstate class A and significant increases of microstate class B in mFEP compared to uFEP. No significant differences between groups were found for microstate classes C and D. Further studies are needed to replicate these results in longitudinal designs that assess antipsychotic medication effects on neural networks at the onset of the disorder and over time during illness progression. As treatment response and compliance in FEP patients are relatively low, such studies could contribute to better understand treatment outcomes and ultimately improve treatment strategies.

Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence-Preliminary Data

This study investigated the magneto- and electroencephalography (MEG and EEG, respectively) resting state to identify the deviations closely associated with the deficit syndrome (DS) in schizophrenia patients. Ten subjects in each group (control, DS, and nondeficit schizophrenia [NDS]) were included. Subjects underwent MEG-EEG recordings during a resting state condition. MEG coherence source imaging (CSI) in source space and spectral analysis in sensor space were performed. Significant differences were found between the 2 patient groups: (1) MEG and EEG spectral analysis showed significantly higher power at low frequencies (delta band) at sensor space in DS compared with NDS patients; (2) source analysis revealed larger power in the DS compared with NDS group at low frequencies in the frontal region; (3) NDS patients showed significantly higher MEG signal relative power in beta bands in sensor space compared with DS patients; (4) both DS and NDS patients showed higher EEG absolute power at higher beta band compared to controls; and (5) patients with DS were found to have a significantly higher MEG CSI than controls in the beta frequency band. These data support the observation of increased power in the low-frequency EEG/MEG rhythms associated with the DS. Increased power in the beta rhythms was more associated with the NDS.

Higuchi's fractal dimension, but not frontal or posterior alpha asymmetry, predicts PID-5 anxiousness more than depressivity

Depression is a major cause of health disability. EEG measures may provide one or more economical biomarkers for the diagnosis of depression. Here we compared frontal alpha asymmetry (FAA), posterior alpha asymmetry (PAA), and Higuchi's fractal dimension (HFD) for their capacity to predict PID-5 depressivity and for the specificity of these predictions relative to PID-5 anxiousness. University students provided 8 or 10 minutes of resting EEG and PID-5 depressivity and PID-5 anxiousness questionnaire scores. FAA and PAA had no significant correlations with the measures at any electrode pair. There were distinct frontal and posterior factors underlying HFD that correlated significantly with anxiousness and with each other. Posterior HFD also correlated significantly with depressivity, though this was weaker than the correlation with anxiousness. The portion of depressivity variance accounted for by posterior HFD was not unique but shared with anxiousness. Inclusion of anxiety disorder patients into the sample rendered the frontal factor somewhat more predictive than the posterior one but generally strengthened the prior conclusions. Contrary to our predictions, none of our measures specifically predicted depressivity. Previous reports of links with depression may involve confounds with concurrent anxiety. Indeed, HFD may be a better measure of anxiety than depression; and its previous linkage to depression may be due to a confound between the two, given the high incidence of depression in cases of severe anxiety.

Brain oscillation-synchronized stimulation of the left dorsolateral prefrontal cortex in depression using real-time EEG-triggered TMS

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC) is an effective treatment for major depressive disorder (MDD), but response rates are low and effect sizes small. Synchronizing TMS pulses with instantaneous brain oscillations can reduce variability and increase efficacy of TMS-induced plasticity.

OBJECTIVE

To study whether brain oscillation-synchronized rTMS is feasible, safe and has neuromodulatory effects when targeting the DLPFC of patients with MDD.

METHODS

Using real-time EEG-triggered TMS we conducted a pseudo-randomized controlled single-session crossover trial of brain oscillation-synchronized rTMS of left DLPFC in 17 adult patients with antidepressant-resistant MDD. Stimulation conditions in separate sessions were: (1) rTMS triggered at the negative EEG peak of instantaneous alpha oscillations (alpha-synchronized rTMS), (2) a variation of intermittent theta-burst stimulation (modified iTBS), and (3) a random alpha phase control condition.

RESULTS

Triggering TMS at the negative peak of instantaneous alpha oscillations by real-time analysis of the electrode F5 EEG signal was successful in 15 subjects. Two subjects reported mild transient discomfort at the site of stimulation during stimulation; no serious adverse events were reported. Alpha-synchronized rTMS, but not modified iTBS or the random alpha phase control condition, reduced resting-state alpha activity in left DLPFC and increased TMS-induced beta oscillations over frontocentral channels.

CONCLUSIONS

Alpha-synchronized rTMS of left DLPFC is feasible, safe and has specific single-session neuromodulatory effects in patients with antidepressant-resistant MDD. Future studies need to further elucidate the mechanisms, optimize the parameters and investigate the therapeutic potential and efficacy of brain oscillation-synchronized rTMS in MDD.