EEG alpha asymmetry as a gender-specific predictor of outcome to acute treatment with different antidepressant medications in the randomized iSPOT-D study

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.

Does 18 Hz deep TMS benefit a different subgroup of depressed patients relative to 10 Hz rTMS? The role of the individual alpha frequency

Both 10 Hz repetitive transcranial magnetic stimulation (rTMS) as well as 18 Hz deep TMS (dTMS) constitute effective, FDA-approved TMS treatment protocols for depression. However, not all patients experience sufficient symptom relief after either of these protocols. Biomarker-guided treatment stratification could aid in personalizing treatment and thereby enhancing improvement. An individual alpha frequency (iAF)-based EEG-biomarker, Brainmarker-I, can differentially stratify patients to depression treatments. For instance, an iAF close to 10 Hz was associated with better improvement to 10 Hz rTMS, possibly reflecting entrainment of endogenous oscillations to the stimulation frequency. Accordingly, we examined whether 18 Hz dTMS would result in better improvement in individuals whose iAF lies around 9 Hz, a harmonic frequency of 18 Hz. Curve fitting and regression analyses were conducted to assess the relation between iAF and improvement. For treatment stratification purposes, correlations with iAF-distance to 10 Hz compared 18 Hz dTMS (N = 114) to 10 Hz rTMS (N = 72). We found a robust quadratic effect, indicating that patients with an iAF around 9 Hz exhibited least symptom improvement (r2=0.126, p<.001). Improvement correlated positively with iAF-distance to 10 Hz (p=.003). A secondary analysis in 20 Hz figure-of-eight data confirmed this direction. A significant interaction of iAF-distance and stimulation frequency between 10 and 18 Hz datasets emerged (p=.026). These results question entrainment of endogenous oscillations by their harmonic frequency for 18 Hz, and suggest that 10 Hz and 18 Hz TMS target different subgroups of depression patients. This study adds to iAF stratification, augmenting Brainmarker-I with alternative TMS protocols (18 Hz/20 Hz) for patients with a slower iAF, thereby broadening clinical applicability and relevance of the biomarker.

Greater weekly physical activity linked to left resting frontal alpha asymmetry in women: A study on gender differences in highly active young adults

Physical activity, beneficial for physical and psychological health, may facilitate affective mechanisms of positive emotion and approach-motivation. Greater resting frontal alpha asymmetry (FAA), an index of greater relative left than right frontal cortical activity, is a neural correlate of affective mechanisms possibly associated with active lifestyles. This study sought to amplify limited literature on the relationship between physical (in)activity, FAA, and gender differences. College students (n = 70) self-reported physical activity (Total PA) and sedentary activity (Total Sitting) via the International Physical Activity Questionnaire-Short Form (IPAQ-SF), followed by a resting electroencephalography session to record FAA. A Total PA × gender interaction (β = 0.462, t = 3.163, p = 0.002) identified a positive relationship between Total PA and FAA in women (β = 0.434, t = 2.221, p = 0.030) and a negative relationship for men (β = -0.338, t = -2.300, p = 0.025). Total Sitting was positively linked to FAA (β = 0.288, t = 2.228, p = 0.029; no gender effect). Results suggest affective mechanisms reflected by FAA (e.g., positive emotion, approach-motivation) are associated with physical activity for women, indicating a possible mechanism of the psychological benefits linked with physically active lifestyles. A positive relationship between sedentary behavior and greater left FAA may also reflect motivated mechanisms of behavior that aid in minimizing energy expenditure, particularly within the context of our highly active sample.

A Deep Learning-Derived Transdiagnostic Signature Indexing Hypoarousal and Impulse Control: Implications for Treatment Prediction in Psychiatric Disorders

BACKGROUND

Psychiatric disorders are traditionally classified within diagnostic categories, but this approach has limitations. The Research Domain Criteria (RDoC) constitute a research classification system for psychiatric disorders based on dimensions within domains that cut across these psychiatric diagnoses. The overall aim of RDoC is to better understand mental illness in terms of dysfunction in fundamental neurobiological and behavioral systems, leading to better diagnosis, prevention, and treatment.

METHODS

A unique electroencephalographic feature, referred to as spindling excessive beta, has been studied in relation to impulse control and sleep as part of the arousal/regulatory system RDoC domain. Here, we studied electroencephalographic frontal beta activity as a potential transdiagnostic biomarker capable of diagnosing and predicting impulse control and sleep problems.

RESULTS

We showed in the first dataset (n = 3279) that the probability of having spindling excessive beta, classified by a deep learning algorithm, was associated with poor sleep maintenance and low daytime impulse control. Furthermore, in 2 additional, independent datasets (iSPOT-A [International Study to Predict Optimized Treatment in ADHD], n = 336; iSPOT-D [International Study to Predict Optimized Treatment in Depression], n = 1008), we revealed that conventional frontocentral beta power and/or spindling excessive beta probability, referred to as Brainmarker-III, is associated with a diagnosis of attention-deficit/hyperactivity disorder, with remission to methylphenidate in children with attention-deficit/hyperactivity disorder in a sex-specific manner, and with remission to antidepressant medication in adults with major depressive disorder in a drug-specific manner.

CONCLUSION

Our results demonstrate the value of the RDoC approach in psychiatry research for the discovery of biomarkers with diagnostic and treatment prediction capacities.

Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project)

Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD's clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool's recommendations. Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.

Predictive modeling of antidepressant efficacy based on cognitive neuropsychological theory

BACKGROUND

We aimed to develop a clinical predictive model based on the cognitive neuropsychological (CNP) theory and machine-learning to examine SSRI efficacy in the treatment of MDD.

METHODS

Baseline assessments including clinical symptoms (HAMD, HAMA, BDI, and TEPS scores), negative biases (NEO-PI-R-N and NCPBQ scores), sociodemographic characteristics (social support and SES), and a 5-min eye-opening resting-state EEG were completed by 69 participants with first-episode major depressive disorder (MDD) and 36 healthy controls. The clinical symptoms and negative bias were again assessed after an 8-week treatment of depression with selective serotonin reuptake inhibitors (SSRIs). A multi-modality machine-learning model was developed to predict the effectiveness of SSRI antidepressants.

RESULTS

At baseline, we observed significant differences between MDD patients and healthy controls in terms of social support, clinical symptoms, and negative bias characteristics (p < 0.001). A negative association was found (p < 0.05) between neuroticism and alpha asymmetry in both the central and central-parietal areas, as well as between negative cognitive processing bias and alpha asymmetry in the parietal region. Compared to responders, non-responders exhibited less negative cognitive processing bias and greater alpha asymmetry in both central and central-parietal regions. Importantly, we developed a multi-modality machine-learning model with 83 % specificity using the above salient features.

CONCLUSIONS

Research results support the CNP theory of depression treatment. To some extent, the multimodal clinical model constructed based on the CNP theory effectively predicted the efficacy of this treatment in this population.

LIMITATIONS

Small sample and only focus on the mechanisms of delayed-onset SSRI treatment.

The role of the target language culture on Arabic learners' fondness for Arabic poetry

As an important carrier of culture, poetry plays a significant role in deepening language learners' understanding of the target language culture as well as enhancing their language skills; however, the effect of the target language culture on language learners' enjoyment of poetry remains unclear. The study served as an attempt to shed light on the point of whether the target language culture has different effects on high- and low-level Chinese Arabic learners' fondness for Arabic poetry with the use of pictures related to Arabic culture and those not related to Arabic culture. In the current study, 40 Arabic learners (20 high-level and 20 low-level) scored the Arabic poem line based on their fondness for it after viewing two kinds of picture with electroencephalogram (EEG) recording. Frontal alpha asymmetry index as a correlate of approach and avoidance related motivation measured by EEG power in the alpha band (8-13 Hz) was calculated for examining whether the behavioral results of Arabic learners' fondness for poetry are in line with the results of changes in the related EEG components. Behavioral results illustrated that low-level subjects showed significantly less liking for Arabic poetry after viewing pictures related to Arabic culture compared to those not related to Arabic culture. The high-level subjects did not show a significant difference in the level of liking for Arabic poetry between the two cases. FAA results demonstrated that low-level subjects presented a significant avoidance-related responses to Arabic poetry after viewing pictures related to Arabic culture in comparison to viewing pictures not related to Arabic culture; while the FAA values did not differ significantly between the two cases in high-level subjects, which is in line with behavioral results. The findings of this research can benefit teachers in motivating students to learn poetry in foreign language curriculum and also contribute to the literature on the effect of target language culture on language learners' enjoyment of poetry.

A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5-84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT - but in opposite direction for women (p = 0.002) relative to men (p = 0.018) - yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.

Electronic health records and stratified psychiatry: bridge to precision treatment?

The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR data's power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.

Caregiver-child interaction as an effective tool for identifying autism spectrum disorder: evidence from EEG analysis

BACKGROUND

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects individuals across their lifespan. Early diagnosis and intervention are crucial for improving outcomes. However, current diagnostic methods are often time-consuming, and costly, making them inaccessible to many families. In the current study, we aim to test caregiver-child interaction as a potential tool for screening children with ASD in clinic.

METHODS

We enrolled 85 preschool children (Mean age: 4.90 ± 0.65 years, 70.6% male), including ASD children with or without developmental delay (DD), and typical development (TD) children, along with their caregivers. ASD core symptoms were evaluated by Childhood Autism Rating Scale (CARS) and Autism Diagnostic Observation Schedule-Calibrated Severity Scores (ADOS-CSS). Behavioral indicators were derived from video encoding of caregiver-child interaction, including social involvement of children (SIC), interaction time (IT), response of children to social cues (RSC), time for caregiver initiated social interactions (GIS) and time for children initiated social interactions (CIS)). Power spectral density (PSD) values were calculated by EEG signals simultaneously recorded. Partial Pearson correlation analysis was used in both ASD groups to investigate the correlation among behavioral indicators scores and ASD symptom severity and PSD values. Receiver operating characteristic (ROC) analysis was used to describe the discrimination accuracy of behavioral indicators.

RESULTS

Compared to TD group, both ASD groups demonstrated significant lower scores of SIC, IT, RSC, CIS (all p values < 0.05), and significant higher time for GIS (all p values < 0.01). SIC scores negatively correlated with CARS (p = 0.006) and ADOS-CSS (p = 0.023) in the ASD with DD group. Compared to TD group, PSD values elevated in ASD groups (all p values < 0.05), and was associated with SIC (theta band: p = 0.005; alpha band: p = 0.003) but not IQ levels. SIC was effective in identifying both ASD groups (sensitivity/specificity: ASD children with DD, 76.5%/66.7%; ASD children without DD, 82.6%/82.2%).

CONCLUSION

Our results verified the behavioral paradigm of caregiver-child interaction as an efficient tool for early ASD screening.

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.

The Relationship Between Theta Power, Theta Asymmetry and the Effect of Escitalopram in the Treatment of Depression

Objective

Only about one-third of depressed patients respond to initial antidepressant treatment. Therefore, it is crucial to find effective predictors of antidepressants. The purpose of our study was to learn the relationship between EEG theta power, theta asymmetry, and the efficacy of escitalopram.

Methods

The study included 34 patients with depression. Before and after each patient's course of treatment, EEG data was gathered. Both the Hamilton Anxiety Scale (HAMA) and the 17-item Hamilton Depression Scale (HAMD-17) were evaluated simultaneously. The natural logarithm of right frontal theta power minus left frontal theta power was used to calculate inter-electrode theta asymmetry (AT).

Results

First, our study found no statistically significant difference between intra-electrode theta power and inter-electrode AT before and after treatment (P ≥ 0.05). When we later looked at the data regarding treatment effects, the findings revealed that patients (n = 9) who did not respond to treatment had lower baseline theta power at C4 [6.190 (2.000, 12.990) vs 15.800 (7.255, 22.330), z = -2.166, P = 0.030]. The two groups had no difference in other electrodes (P ≥ 0.05). The AT of C3/C4 in non-responders (n = 9) was lower [0.012 (0.795) vs 0.733 (0.539), t = -3.224, P = 0.005]. However, there was no difference in inter-electrode AT between the two groups in F3/F4 and F7/F8 (P ≥ 0.05). We finally show that the theta power at C4 was negatively correlated with HAMD scores before treatment (r = -0.346, P = 0.045).

Conclusion

Our findings determined that increased theta power and positive asymmetry in the right frontal-central area correlate with favourable escitalopram treatment, providing a basis for finding predictive markers for antidepressants.

Rostral Anterior Cingulate Cortex Oscillatory Power Indexes Treatment-Resistance to Multiple Therapies in Major Depressive Disorder

INTRODUCTION

High rostral anterior cingulate cortex (rACC) activity is proposed as a nonspecific prognostic marker for treatment response in major depressive disorder, independent of treatment modality. However, other studies report a negative association between baseline high rACC activation and treatment response. Interestingly, these contradictory findings were also found when focusing on oscillatory markers, specifically rACC-theta power. An explanation could be that rACC-theta activity dynamically changes according to number of previous treatment attempts and thus is mediated by level of treatment-resistance.

METHODS

Primarily, we analyzed differences in rACC- and frontal-theta activity in large national cross-sectional samples representing various levels of treatment-resistance and resistance to multimodal treatments in depressed patients (psychotherapy [n = 175], antidepressant medication [AD; n = 106], repetitive transcranial magnetic stimulation [rTMS; n = 196], and electroconvulsive therapy [ECT; n = 41]), and the respective difference between remitters and non-remitters. For exploratory purposes, we also investigated other frequency bands (delta, alpha, beta, gamma).

RESULTS

rACC-theta activity was higher (p < 0.001) in the more resistant rTMS and ECT patients relative to the less resistant psychotherapy and AD patients (psychotherapy-rTMS: d = 0.315; AD-rTMS: d = 0.320; psychotherapy-ECT: d = 1.031; AD-ECT: d = 1.034), with no difference between psychotherapy and AD patients. This association was even more pronounced after controlling for frontal-theta. Post hoc analyses also yielded effects for delta, beta, and gamma bands.

CONCLUSION

Our findings suggest that by factoring in degree of treatment-resistance during interpretation of the rACC-theta biomarker, its usefulness in treatment selection and prognosis could potentially be improved substantially in future real-world practice. Future research should however also investigate specificity of the theta band.

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication

Importance

Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging.

Objective

To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications.

Design, Setting, and Participants

This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022.

Exposures

In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment.

Main Outcomes and Measures

The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity.

Results

The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response.

Conclusions and Relevance

In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.

Deep Learning in the Identification of Electroencephalogram Sources Associated with Sexual Orientation

INTRODUCTION

It is unclear if sexual orientation is a biological trait that has neurofunctional footprints. With deep learning, the power to classify biological datasets without an a priori selection of features has increased by magnitudes. The aim of this study was to correctly classify resting-state electroencephalogram (EEG) data from males with different sexual orientation using deep learning and to explore techniques to identify the learned distinguishing features.

METHODS

Three cohorts (homosexual men, heterosexual men, and a mixed sex cohort), one pretrained network on sex classification, and one newly trained network for sexual orientation classification were used to classify sex. Further, Grad-CAM methodology and source localization were used to identify the spatiotemporal patterns that were used for differentiation by the networks.

RESULTS

Using a pretrained network for classification of males and females, no differences existed between classification of homosexual and heterosexual males. The newly trained network was able, however, to correctly classify the cohorts with a total accuracy of 83%. The retrograde activation using Grad-CAM technology yielded distinctive functional EEG patterns in the Brodmann area 40 and 1 when combined with Fourier analysis and a source localization.

DISCUSSION

This study shows that electrophysiological trait markers of male sexual orientation can be identified using deep learning. These patterns are different from the differentiating signatures of males and females in a resting-state EEG.

Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.

Orbitofrontal cortex-hippocampus potentiation mediates relief for depression: A randomized double-blind trial and TMS-EEG study

It has been 15 years since repetitive transcranial magnetic stimulation (rTMS) targeting the dorsolateral prefrontal cortex (DLPFC) was approved by the FDA for clinical depression treatment. Yet, the underlying mechanisms for rTMS-induced depression relief are not fully elucidated. This study analyzes TMS-electroencephalogram (EEG) data from 64 healthy control (HC) subjects and 53 patients with major depressive disorder (MDD) before and after rTMS treatment. Prior to treatment, patients with MDD have lower activity in the DLPFC, the hippocampus (HPC), the orbitofrontal cortex (OFC), and DLPFC-OFC connectivity compared with HCs. Following active rTMS treatment, patients with MDD show a significant increase in the DLPFC, HPC, and OFC. Notably, the increase in HPC activity is specifically associated with amelioration of depressive symptoms but not anxiety or sleep quality. The orbitofrontal-hippocampal pathway plays a crucial role in mediating depression relief following rTMS treatment. These findings suggest potential alternative targets for brain stimulation therapy against depression (chictr.org.cn: ChiCTR2100052007).

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

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

Functional role of frontal electroencephalogram alpha asymmetry in the resting state in patients with depression: A review

Depression is a psychological disorder that affects the general public worldwide. It is particularly important to make an objective and accurate diagnosis of depression, and the measurement methods of brain activity have gradually received increasing attention. Resting electroencephalogram (EEG) alpha asymmetry in patients with depression shows changes in activation of the alpha frequency band of the left and right frontal cortices. In this paper, we review the findings of the relationship between frontal EEG alpha asymmetry in the resting state and depression. Based on worldwide studies, we found the following: (1) Compared with individuals without depression, those with depression showed greater right frontal EEG alpha asymmetry in the resting state. However, the pattern of frontal EEG alpha asymmetry in the resting state in depressive individuals seemed to disappear with age; (2) Compared with individuals without maternal depression, those with maternal depression showed greater right frontal EEG alpha asymmetry in the resting state, which indicated that genetic or experience-based influences have an impact on frontal EEG alpha asymmetry at rest; and (3) Frontal EEG alpha asymmetry in the resting state was stable, and little or no change occurred after antidepressant treatment. Finally, we concluded that the contrasting results may be due to differences in methodology, clinical characteristics, and participant characteristics.

Electrophysiological biomarkers and age characterize phenotypic heterogeneity among individuals with major depressive disorder

Introduction: Despite the high need for effective treatments for major depressive disorder (MDD), the development of novel medicines is hampered by clinical, genetic and biological heterogeneity, unclear links between symptoms and neural dysfunction, and tenuous biomarkers for clinical trial contexts of use. Methods: In this study, we examined the International Study to Predict Optimized Treatment in Depression (iSPOT-D) clinical trial database for new relationships between auditory event-related potential (ERP) responses, demographic features, and clinical symptoms and behavior, to inform strategies for biomarker-driven patient stratification that could be used to optimize future clinical trial design and drug development strategy in MDD. Results: We replicate findings from previous analyses of the classic auditory oddball task in the iSPOT-D sample showing smaller than typical N1 and P300 response amplitudes and longer P300 latencies for target and standard stimuli in patients with MDD, suggesting altered bottom-up sensory and top-down attentional processes. We further demonstrate that age is an important contributor to clinical group differences, affecting both topographic distribution of the clinically informative ERP responses and the types of the stimuli sensitive to group differences. In addition, the observed brain-behavior associations indicate that levels of anxiety and stress are major contributing factors to atypical sensory and attentional processing among patients with MDD, particularly in the older subgroups. Discussion: Our novel findings support the possibility of accelerated cognitive aging in patients with MDD and identify the frontal P300 latency as an additional candidate biomarker of MDD. These results from a large, well-phenotyped sample support the view that heterogeneity of the clinical population with MDD can be systematically characterized based on age and neural biomarkers of sensory and attentional processing, informing patient stratification strategies in the design of clinical trials.

The effect of ketamine and D-cycloserine on the high frequency resting EEG spectrum in humans

RATIONALE

Preclinical studies indicate that high-frequency oscillations, above 100 Hz (HFO:100-170 Hz), are a potential translatable biomarker for pharmacological studies, with the rapid acting antidepressant ketamine increasing both gamma (40-100 Hz) and HFO.

OBJECTIVES

To assess the effect of the uncompetitive NMDA antagonist ketamine, and of D-cycloserine (DCS), which acts at the glycine site on NMDA receptors on HFO in humans.

METHODS

We carried out a partially double-blind, 4-way crossover study in 24 healthy male volunteers. Each participant received an oral tablet and an intravenous infusion on each of four study days. The oral treatment was either DCS (250 mg or 1000 mg) or placebo. The infusion contained 0.5 mg/kg ketamine or saline placebo. The four study conditions were therefore placebo-placebo, 250 mg DCS-placebo, 1000 mg DCS-placebo, or placebo-ketamine.

RESULTS

Compared with placebo, frontal midline HFO magnitude was increased by ketamine (p = 0.00014) and 1000 mg DCS (p = 0.013). Frontal gamma magnitude was also increased by both these treatments. However, at a midline parietal location, only HFO were increased by DCS, and not gamma, whilst ketamine increased both gamma and HFO at this location. Ketamine induced psychomimetic effects, as measured by the PSI scale, whereas DCS did not increase the total PSI score. The perceptual distortion subscale scores correlated with the posterior low gamma to frontal high beta ratio.

CONCLUSIONS

Our results suggest that, at high doses, a partial NMDA agonist (DCS) has similar effects on fast neural oscillations as an NMDA antagonist (ketamine). As HFO were induced without psychomimetic effects, they may prove a useful drug development target.

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.

Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety

Anxiety affects approximately 5-10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4-7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8-12 Hz) or beta (13-30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.

Hemispheric Utilization of Alpha Oscillatory Dynamics as a Unique Biomarker of Neural Compensation in Females with Fragile X Syndrome

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by a trinucleotide expansion on the FMR1 gene and characterized by intellectual disability, sensory hypersensitivity, executive function difficulties, and social anxiety. Recently, efforts to define neural biomarkers for FXS have highlighted disruptions to power in the alpha frequency band; however the dynamic mechanisms supporting these findings are poorly understood. The current study aimed to explore the temporal and hemispheric dynamics supporting alpha phenotypes in FXS and their relationship with neural phenotypes related to auditory processing using electroencephalography during an auditory evoked task. Adolescents and adults (N = 36) with FXS and age/sex matched typically developing controls (N = 40) completed an auditory chirp task. Frontal alpha power in the prestimulus period was decomposed into "bursts" using percentile thresholding, then assessed for number of bursts per second (burst count) and burst length. Data were compared across left and right hemispheres to assess lateralization of neural activity. Individuals with FXS showed more differences in alpha power compared to TDC primarily in the right hemisphere. Notably, alpha hemisphere outcomes in males with FXS were driven by the number of times they entered a dynamically relevant period of alpha (burst count) rather than length of time spent in alpha. Females with FXS showed reduced burst counts but remained in sustained high alpha states for longer periods of time. Length of time spent in alpha may reflect a modulatory or compensatory mechanism capable of recovering sensory processing abilities in females with FXS resulting in a less severe clinical presentation. Right hemisphere abnormalities may impact sensory processing differences between males and females with FXS. The relationship between alpha burst length, count, sex, and hemisphere may shed light on underlying mechanisms for previously observed alpha power abnormalities in FXS and their variation by sex.

EEG-Update

Durch die rasante Entwicklung digitaler Computertechniken und neuer Analysemethoden hat sich ein neuer Ansatz zur Analyse der Hirnströme (quantitatives EEG) ergeben, die in verschiedenen klinischen Bereichen der Neurologie und Psychiatrie bereits Ergebnisse zeigen. Die neuen Möglichkeiten der Analyse des EEG durch Einsatz künstlicher Intelligenz (Deep Learning) und großer Datenmengen (Big Data) sowie telemedizinischer Datenübermittlung und Interaktion wird den Einsatz der Methode vermutlich in den nächsten Jahren erweitern.

The novel frontal alpha asymmetry factor and its association with depression, anxiety, and personality traits

Frontal alpha asymmetry (FAA) is widely examined in EEG research, yet a procedural consensus on its assessment is lacking. In this study, we tested a latent factorial approach to measure FAA. We assessed resting-state FAA at broad, low, and high alpha bands (8-13; 8-10.5; and 11-13 Hz) using mastoids as reference electrodes and Current Source Density (CSD) transformation (N = 139 non-clinical participants). From mastoid-referenced data, we extracted a frontal alpha asymmetry factor (FAAf) and a parietal factor (PAAf) subjecting all asymmetry indices to a varimax-rotated, principal component analysis. We explored split-half reliability and discriminant validity of the mastoid factors and the mastoid and CSD raw asymmetry indices (F3/4, F7/8, P3/4, and P7/8). Both factor and raw scores reached an excellent split-half reliability (>.99), but only the FAAf reached the maximum discriminant validity from parietal scores. Next, we explored the correlations of latent factor and raw FAA scores with symptoms of depression, anxiety, and personality traits to determine which associations were driven by FAA after variance from parietal activity was removed. After correcting for false discovery rate, only FAAf at the low alpha band was negatively associated with depression symptoms (a latent CES-D factor) and significantly diverged from PAAf's association with depression symptoms. With respect to personality traits, only CSD-transformed F7/8 was positively correlated with Conscientiousness and significantly diverged from the correlations between Conscientiousness and P3/4 and P7/8. Overall, the latent factor approach shows promise for isolating functionally distinct resting-state EEG signatures, although further research is needed to examine construct validity.

Frontal alpha asymmetry: A potential biomarker of approach-withdrawal motivation towards pain

Pain-related catastrophising is a maladaptive coping strategy known to have a strong influence on clinical pain outcomes and treatment efficacy. Notwithstanding, little is known about its neurophysiological correlates. There is evidence to suggest catastrophising is associated with resting-state EEG frontal alpha asymmetry (FAA) patterns reflective of greater relative right frontal activity, which is known to be linked to withdrawal motivation and avoidance of aversive stimuli. The present study aims to investigate whether such a relationship occurs in the situational context of experimental pain. A placebo intervention was also included to evaluate effects of a potential pain-relieving intervention on FAA. 35 participants, including both chronic pain patients and healthy subjects, completed the Pain Catastrophising Scale (PCS) questionnaire followed by EEG recordings during cold pressor test (CPT)-induced tonic pain with or without prior application of placebo cream. There was a negative correlation between FAA and PCS-subscale helplessness scores, but not rumination or magnification, during the pre-placebo CPT condition. Moreover, FAA scores were shown to increase significantly in response to pain, indicative of greater relative left frontal activity that relates to approach-oriented behaviours. Placebo treatment elicited a decrease in FAA in low helplessness scorers, but no significant effects in individuals scoring above the mean on PCS-helplessness. These findings suggest that, during painful events, FAA may reflect the motivational drive to obtain reward of pain relief, which may be diminished in individuals who are prone to feel helpless about their pain. This study provides valuable insights into biomarkers of pain-related catastrophising and prospects of identifying promising targets of brain-based therapies for chronic pain management.

Frontal Alpha Asymmetry in Children with Trauma Exposure

The purpose of the current study was to investigate differences in frontal alpha asymmetry (FAA) between children (5-17 years) with or without histories of trauma exposure. EEG data were obtained from 165 children who participated in the Healthy Brain Network Initiative during rest with eyes open and closed. FAA during resting-state electroencephalography was significantly more negative in the trauma-exposed group, suggesting greater left lateralized FAA and avoidance-oriented motivation. Moreover, alpha suppression (difference in alpha amplitude between eyes open and eyes closed conditions) was marginally greater in the trauma-exposed group. The results suggest that early exposure to trauma may be associated with trait-level avoidance of environmental stimuli, which ultimately may be predictive of psychopathology, including posttraumatic stress disorder (PTSD). Study findings thus provide preliminary evidence of brain-based mechanisms that may confer risk for PTSD in the wake of early trauma exposure.

A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study

The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.

Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review

Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.

A comparison of nomothetic and individualized alpha frequency approaches to measuring frontal EEG alpha asymmetry

Frontal alpha asymmetry (FAA) is considered to be a reliable marker of affective processing and psychopathology. Traditionally, the magnitude of alpha is calculated by taking the average over a nomothetic fixed frequency window (e.g., 8 to 13 Hz). Alternatively, methods have been proposed to extract individualized alpha frequency (IAF) peaks and windows in hopes of improving the reliability and validity of signal detection. However, no study has compared the nomothetic to IAF approaches to examine the reliability and validity of resting FAA in a large well-characterized data set. In this study, we assessed the psychometric performance of the standard fixed window approach, a PZ-alpha based IAF approach and a global-alpha based IAF windows detection approach on a previously collected EEG data set (8 recordings per subject collected on four occasions across two weeks). Our results revealed that resting FAA calculated with these three different methods are highly correlated at all frontal regions (mean r = .98). The stability across the 8 recordings over the two weeks also showed no substantial difference between approaches as indicated by intraclass correlations. Moreover, internal-consistency reliability, validity with respect to measures of emotion and emotion-related psychopathology and state-trait Structure equation model (SEM) fitting were evaluated and yielded no significant differences across methods. Our results supported the overall reliability and validity of two different IAF approaches to assessing resting FAA but fail to find any incremental advantage over nomothetic approaches to defining alpha bands. Guidelines for methods selection for future research are provided.

The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database

In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment response. At the same time neuroscience is becoming more data-driven, made possible by computational advances. In support of biomarker development and methodologies such as training Artificial Intelligent (AI) networks we present the extensive Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG database. This clinical lifespan database (5-89 years) contains resting-state, raw EEG-data complemented with relevant clinical and demographic data of a heterogenous collection of 1274 psychiatric patients collected between 2001 to 2021. Main indications included are Major Depressive Disorder (MDD; N = 426), attention deficit hyperactivity disorder (ADHD; N = 271), Subjective Memory Complaints (SMC: N = 119) and obsessive-compulsive disorder (OCD; N = 75). Demographic-, personality- and day of measurement data are included in the database. Thirty percent of clinical and treatment outcome data will remain blinded for prospective validation and replication purposes. The TDBRAIN database and code are available on the Brainclinics Foundation website at www.brainclinics.com/resources and on Synapse at www.synapse.org/TDBRAIN .

A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

Electrophysiological correlates and predictors of the antidepressant response to repeated ketamine infusions in treatment-resistant depression

BACKGROUND

Sub-anesthetic ketamine doses rapidly reduce depressive symptoms, although additional investigations of the underlying neural mechanisms and the prediction of response outcomes are needed. Electroencephalographic (EEG)-derived measures have shown promise in predicting antidepressant response to a variety of treatments, and are sensitive to ketamine administration. This study examined their utility in characterizing changes in depressive symptoms following single and repeated ketamine infusions.

METHODS

Recordings were obtained from patients with treatment-resistant major depressive disorder (MDD) (N = 24) enrolled in a multi-phase clinical ketamine trial. During the randomized, double-blind, crossover phase (Phase 1), patients received intravenous ketamine (0.5 mg/kg) and midazolam (30 μg/kg), at least 1 week apart. For each medication, three resting, eyes-closed recordings were obtained per session (pre-infusion, immediately post-infusion, 2 h post-infusion), and changes in power (delta, theta1/2/total, alpha1/2/total, beta, gamma), alpha asymmetry, theta cordance, and theta source-localized anterior cingulate cortex activity were quantified. The relationships between ketamine-induced changes with early (Phase 1) and sustained (Phases 2,3: open-label repeated infusions) decreases in depressive symptoms (Montgomery-Åsberg Depression Rating Score, MADRS) and suicidal ideation (MADRS item 10) were examined.

RESULTS

Both medications decreased alpha and theta immediately post-infusion, however, only midazolam increased delta (post-infusion), and only ketamine increased gamma (immediately post- and 2 h post-infusion). Regional- and frequency-specific ketamine-induced EEG changes were related to and predictive of decreases in depressive symptoms (theta, gamma) and suicidal ideation (alpha). Early and sustained treatment responders differed at baseline in surface-level and source-localized theta.

CONCLUSIONS

Ketamine exerts frequency-specific changes on EEG-derived measures, which are related to depressive symptom decreases in treatment-resistant MDD and provide information regarding early and sustained individual response to ketamine.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov: Action of Ketamine in Treatment-Resistant Depression, NCT01945047.

Therapeutic Garden With Contemplative Features Induces Desirable Changes in Mood and Brain Activity in Depressed Adults

The therapeutic values of contact with nature have been increasingly recognized. A growing body of evidence suggests that a unique subcategory of "contemplative landscapes" is particularly therapeutic. Previous studies predominantly focused on observational designs in non-clinical populations. It is not known if these effects can be extrapolated to populations suffering from depression, and experimental designs need to be utilized to establish causality. We examined the effects of in-situ passive exposure to three urban spaces on brain activity, namely a Therapeutic Garden with high Contemplative Landscape scores (TG), Residential Green (RG) and Busy Downtown (BD), and self-reported momentary mood in adults aged 21-74 (n = 92), including 24 clinically depressed and 68 healthy participants. Portable, multimodal electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) systems were used to record brain activity, and a Profile of Mood States (POMS) questionnaire was used to record mood before and after exposure. We tested the interactions between the site, time and group for the mood, and between site and group for the neuroelectric oscillations and brain hemodynamics. Self-reported pre- post-mood was significant only at the TG (p = 0.032) in both groups. The lowest Total Mood Disturbance (TMD) was reported at TG and the highest in BD (p = 0.026). Results from fNIRS indicated marginally significant lower oxy-Hb in the frontal region at TG as compared to BD (p = 0.054) across both groups. The marginally significant effect of site and group was also observed (p = 0.062), with the Clinical group showing much lower oxy-Hb at TG than Healthy. The opposite pattern was observed at BD. EEG results showed differences between Healthy and Clinical groups in the Frontal Alpha Asymmetry (FAA) pattern across the sites (p = 0.04), with more frontal alpha right in the Clinical sample and more left lateralization in the Healthy sample at TG. Temporal Beta Asymmetry (TBA) analyses suggested that patients displayed lower bottom-up attention than Healthy participants across all sites (p = 0.039). The results suggest that both healthy and depressed adults benefitted from exposure to TG, with possibly different pathways of mood improvement. Visiting therapeutic nature with contemplative features may provide valuable support for the treatment of depression in clinical populations and a self-care intervention in non-clinical populations.

Changes in Electric Brain Response to Affective Stimuli in the First Week of Antidepressant Treatment: An Exploratory Study

INTRODUCTION

Asymmetrical alpha and frontal theta activity have been discussed as neurobiological markers for antidepressant treatment response. While most studies focus on resting-state EEG, there is evidence that task-related activity assessed at multiple time points might be superior in detecting subtle early differences.

METHODS

This was a naturalistic study design assessing participants in a psychiatric in- and outpatient hospital setting. We investigated stimulus-related EEG asymmetry (frontal and occipital alpha-1 and alpha-2) and power (frontal midline theta) assessed at baseline and 1 week after initiation of pharmacological depression treatment while presenting affective stimuli. We then compared week 4 responders and nonresponders to antidepressant treatment.

RESULTS

Follow-up analyses of a significant group × emotion × time interaction (p < 0.04) for alpha-1 asymmetry showed that responders differed significantly at baseline in their asymmetry scores in response to sad compared to happy faces with a change in this pattern 1 week later. Nonresponders did not show this pattern. No significant results were found for alpha-2, occipital alpha-1, and occipital alpha-2 asymmetry or frontal midline theta power.

DISCUSSION

Our study addresses the gap in comparisons of task-related EEG activity changes measured at two time points and supports the potential value of this approach in detecting early differences in responders versus nonresponders to pharmacological treatment. Important limitations include the small sample size and the noncontrolled study design.

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.

Intrinsic Brain Activity in Temporal Lobe Epilepsy With and Without Depression: Insights From EEG Microstates

Background: Depression is the most common psychiatric comorbidity of temporal lobe epilepsy (TLE). In the recent years, studies have focused on the common pathogenesis of TLE and depression. However, few of the studies focused on the dynamic characteristics of TLE with depression. We tested the hypotheses that there exist abnormalities in microstates in patients with TLE with depression.

Methods: Participants were classified into patients with TLE with depression (PDS) (n = 19) and patients with TLE without depression (nPDS) (n = 19) based upon the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V). Microstate analysis was applied based on 256-channel electroencephalography (EEG) to detect the dynamic changes in whole brain. The coverage (proportion of time spent in each state), frequency of occurrence, and duration (average time of each state) were calculated.

Results: Patients with PDS showed a shorter mean microstate duration with higher mean occurrence per second compared to patients with nPDS. There was no difference between the two groups in the coverage of microstate A–D.

Conclusion: This is the first study to present the temporal fluctuations of EEG topography in comorbid depression in TLE using EEG microstate analysis. The temporal characteristics of the four canonical EEG microstates were significantly altered in patients with TLE suffer from comorbid depression.

Revisiting Hemispheric Asymmetry in Mood Regulation: Implications for rTMS for Major Depressive Disorder

Hemispheric differences in emotional processing have been observed for over half a century, leading to multiple theories classifying differing roles for the right and left hemisphere in emotional processing. Conventional acceptance of these theories has had lasting clinical implications for the treatment of mood disorders. The theory that the left hemisphere is broadly associated with positively valenced emotions, while the right hemisphere is broadly associated with negatively valenced emotions, drove the initial application of repetitive transcranial magnetic stimulation (rTMS) for the treatment of major depressive disorder (MDD). Subsequent rTMS research has led to improved response rates while adhering to the same initial paradigm of administering excitatory rTMS to the left prefrontal cortex (PFC) and inhibitory rTMS to the right PFC. However, accumulating evidence points to greater similarities in emotional regulation between the hemispheres than previously theorized, with potential implications for how rTMS for MDD may be delivered and optimized in the near future. This review will catalog the range of measurement modalities that have been used to explore and describe hemispheric differences, and highlight evidence that updates and advances knowledge of TMS targeting and parameter selection. Future directions for research are proposed that may advance precision medicine and improve efficacy of TMS for MDD.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

Evaluating the evidence for sex differences: a scoping review of human neuroimaging in psychopharmacology research

Although sex differences in psychiatric disorders abound, few neuropsychopharmacology (NPP) studies consider sex as a biological variable (SABV). We conducted a scoping review of this literature in humans by systematically searching PubMed to identify peer-reviewed journal articles published before March 2020 that (1) studied FDA-approved medications used to treat psychiatric disorders (or related symptoms) and (2) adequately evaluated sex differences using in vivo neuroimaging methodologies. Of the 251 NPP studies that included both sexes and considered SABV in analyses, 80% used methodologies that eliminated the effect of sex (e.g., by including sex as a covariate to control for its effect). Only 20% (50 studies) adequately evaluated sex differences either by testing for an interaction involving sex or by stratifying analyses by sex. Of these 50 studies, 72% found statistically significant sex differences in at least one outcome. Sex differences in neural and behavioral outcomes were studied more often in drugs indicated for conditions with known sex differences. Likewise, the majority of studies conducted in those drug classes noted sex differences: antidepressants (13 of 16), antipsychotics (10 of 12), sedative-hypnotics (6 of 10), and stimulants (6 of 10). In contrast, only two studies of mood stabilizers evaluated SABV, with one noting a sex difference. By mapping this literature, we bring into sharp relief how few studies adequately evaluate sex differences in NPP studies. Currently, all NIH-funded studies are required to consider SABV. We urge scientific journals, peer reviewers, and regulatory agencies to require researchers to consider SABV in their research. Continuing to ignore SABV in NPP research has ramifications both in terms of rigor and reproducibility of research, potentially leading to costly consequences and unrealized benefits.

Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System

Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate real-world experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22-80 years old, 64.3% female), and an open-source automatic method to preprocess it. We then examined associations between well-being levels and the alpha center of gravity (CoG) as well as trait EEG asymmetries, in the frontal and temporoparietal (TP) areas. Robust linear regression models did not reveal an association between well-being and alpha (8-13 Hz) asymmetry in the frontal regions, nor with the CoG. However, well-being was associated with alpha asymmetry in the TP areas (i.e., corresponding to relatively less left than right TP cortical activity as well-being levels increased). This effect was driven by oscillatory activity in lower alpha frequencies (8-10.5 Hz), reinforcing the importance of dissociating sub-components of the alpha band when investigating alpha asymmetries. Age was correlated with both well-being and alpha asymmetry scores, but gender was not. Finally, EEG asymmetries in the other frequency bands were not associated with well-being, supporting the specific role of alpha asymmetries with the brain mechanisms underlying well-being levels. Interpretations, limitations, and recommendations for future studies are discussed. This paper presents novel methodological, experimental, and theoretical findings that help advance human neurophysiological monitoring techniques using wearable neurotechnologies and increase the feasibility of their implementation into real-world applications.

Quantitative Electroencephalogram Standardization: A Sex- and Age-Differentiated Normative Database

We describe the utility of a standardized index (Z-score) in quantitative EEG (QEEG) capable of when referenced to a resting-state, sex- and age-differentiated QEEG normative database (ISB-NormDB). Our ISB-NormDB comprises data for 1,289 subjects (553 males, 736 females) ages 4.5 to 81 years that met strict normative data criteria. A de-noising process allowed stratification based on QEEG variability between normal healthy men and women at various age ranges. The ISB-NormDB data set that is stratified by sex provides a unique, highly accurate ISB-NormDB model (ISB-NormDB: ISB-NormDB-Male, ISB-NormDB-Female). To evaluate the trends and accuracy of the ISB-NormDB, we used actual data to compare Z-scores obtained through the ISB-NormDB with those obtained through a traditional QEEG normative database to confirm that basic trends are maintained in most bands and are sensitive to abnormal test data. Finally, we demonstrate the value of our standardized index of QEEG, and highlight it's capacity to minimize the confounding variables of sex and age in any analysis.

To spindle or not to spindle: A replication study into spindling excessive beta as a transdiagnostic EEG feature associated with impulse control

BACKGROUND

Frontocentral Spindling Excessive Beta (SEB), a spindle-like beta-activity observed in the electroencephalogram (EEG), has been transdiagnostically associated with more problems with impulse control and sleep maintenance. The current study aims to replicate and elaborate on these findings.

METHODS

Participants reporting sleep problems (n = 31) or Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms (n = 48) were included. Baseline ADHD-Rating Scale (ADHD-RS), Pittsburgh Sleep Quality Index (PSQI), Holland Sleep Disorder Questionnaire (HSDQ), and EEG were assessed. Analyses were confined to adults with frontocentral SEB.

RESULTS

Main effects of SEB showed more impulse control problems (d = 0.87) and false positive errors (d = 0.55) in participants with SEB. No significant associations with sleep or interactions with Sample were observed.

DISCUSSION

This study partially replicates an earlier study and demonstrates that participants exhibiting SEB report more impulse control problems, independent of diagnosis. Future studies should focus on automating SEB classification and further investigate the transdiagnostic nature of SEB.

NeuroPharm study: EEG wakefulness regulation as a biomarker in MDD

While several electroencephalogram (EEG)-based biomarkers have been proposed as diagnostic or predictive tools in major depressive disorder (MDD), there is a clear lack of replication studies in this field. Markers that link clinical features such as disturbed wakefulness regulation in MDD with neurophysiological patterns are particularly promising candidates for e.g., EEG-informed choices of antidepressive treatment. We investigate if we in an independent MDD sample can replicate abnormal findings of EEG-vigilance regulation during rest and as a predictor for antidepressive treatment response. EEG-resting state was recorded in 91 patients and 35 healthy controls from the NeuroPharm trial. EEG-vigilance was assessed using the Vigilance Algorithm Leipzig (VIGALL). We compared the vigilance regulation during rest between patients and healthy controls and between remitters/responders and non-remitters/non-responders after eight weeks of SSRI/SNRI treatment using two different sets of response criteria (NeuroPharm and iSPOT-D). We replicated previous findings showing hyperstable EEG-wakefulness regulation in patients in comparison to healthy subjects. Responders defined by the iSPOT-D criteria showed a higher propensity toward low vigilance stages in comparison to patients with no response at pretreatment, however, this did not apply when using the NeuroPharm criteria. EEG-wakefulness regulation patterns normalized toward patterns of healthy controls after 8 weeks of treatment. This replication study supports the diagnostic value of EEG-vigilance regulation and its usefulness as a biomarker for the choice of treatment in MDD.

Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study

Several electroencephalogram (EEG) biomarkers for prediction of drug response in major depressive disorder (MDD) have been proposed, but validations in larger independent datasets are missing. In the current study, we investigated the prognostic value of previously suggested EEG biomarkers. We gathered data that matched prior studies in terms of EEG methodology, clinical criteria for MDD, and statistical approach as closely as possible. The NeuroPharm study is a non-randomized and open label prospective clinical trial. One hundred antidepressant free patients with MDD were enrolled in the study and 79 (57 female) were included in the per-protocol analysis. The biomarkers candidates for cross-validation were derived from prior studies such as iSPOT-D and EMBARC and include frontal and occipital alpha power and asymmetry and delta and theta activity at anterior cingulate cortex (ACC). The alpha asymmetry, reported in two out of six prior studies, could be partially validated. We found that in female patients, larger right than left frontal alpha power prior to drug treatment was associated with better clinical outcome 8 weeks later. Moreover, female non-responder had higher central left alpha power relative to the right. In contrast to prior reports, we found that lower theta activity at ACC was present in remitters and was associated with greater improvement at week 8. We provide evidence that in women with MDD, alpha asymmetry seems to be the most promising EEG biomarker for prediction of treatment response. Registration number: NCT02869035.

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

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