Disrupted cortical brain network in post-traumatic stress disorder patients: a resting-state electroencephalographic study

Can Electroencephalography-Based Neurofeedback Treat Post-traumatic Stress Disorder? A Meta-analysis Study

Post-traumatic stress disorder (PTSD) remains a significant clinical challenge with limited treatment options. Although electroencephalogram (EEG) neurofeedback has garnered attention as a prospective treatment modality for PTSD, no comprehensive meta-analysis has been conducted to assess its efficacy and compare different treatment protocols. This study aims to provide a multi-variable meta-regression analysis of EEG neurofeedback's impact on PTSD symptoms, while also assessing variables that may influence treatment outcomes. A systematic review was performed to identify controlled studies exploring for the efficacy of EEG neurofeedback on PTSD. The overall effectiveness was evaluated through meta-analysis, and a multi-variable meta-regression was employed to discern factors affecting the EEG neurofeedback efficacy. EEG neurofeedback demonstrated a statistically significant reduction in PTSD symptoms immediately post-intervention, with sustained effects observed at one-month and three-month follow-ups. A sub-analysis of sham-controlled studies confirmed that outcomes were not attributable to placebo effects. Sensitivity analysis revealed that excluding two outlying studies resolved heterogeneity entirely, and all subsequent analyses were conducted on the refined dataset. While initial analyses identified target frequency, target region, and feedback modality as significant moderators, meta-regressions controlling for publication year revealed that these effects were confounded by temporal trends in study design and methodological rigor. In contrast, variables related to treatment duration, such as session number, session length, or weeks of intervention, were not significant moderators. These findings suggest that EEG neurofeedback is robust across protocol variations and highlight the importance of methodological advancements in interpreting treatment efficacy. EEG neurofeedback emerges as a promising and robust treatment modality for PTSD, demonstrating sustained therapeutic effects across follow-up periods. Our findings confirm its efficacy beyond placebo effects and highlight the stability of outcomes across diverse neurofeedback protocols. This study underscores the importance of methodological rigor and standardization in EEG neurofeedback research and advocates for larger, well-controlled trials to refine and optimize treatment protocols. These findings reaffirm EEG neurofeedback's potential as a cost-effective and scalable intervention, while encouraging future research into alternative methodologies to enhance efficacy and expand accessibility.

Spatial-Frequency Characteristics of EEG Associated With the Mental Stress in Human-Machine Systems

Accurate assessment of user mental stress in human-machine system plays a crucial role in ensuring task performance and system safety. However, the underlying neural mechanisms of stress in human-machine tasks and assessment methods based on physiological indicators remain fundamental challenges. In this paper, we employ a virtual unmanned aerial vehicle (UAV) control experiment to explore the reorganization of functional brain network patterns under stress conditions. The results indicate enhanced functional connectivity in the frontal theta band and central beta band, as well as reduced functional connectivity in the left parieto-occipital alpha band, which is associated with increased mental stress. Evaluation of network metrics reveals that decreased global efficiency in the theta and beta bands is linked to elevated stress levels. Subsequently, inspired by the frequency-specific patterns in the stress brain network, a cross-band graph convolutional network (CBGCN) model is constructed for mental stress brain state recognition. The proposed method captures the spatial-frequency topological relationships of cross-band brain networks through multiple branches, with the aim of integrating complex dynamic patterns hidden in the brain network and learning discriminative cognitive features. Experimental results demonstrate that the neuro-inspired CBGCN model improves classification performance and enhances model interpretability. The study suggests that the proposed approach provides a potentially viable solution for recognizing stress states in human-machine system by using EEG signals.

A biomarker of brain arousal mediates the intergenerational link between maternal and child post-traumatic stress disorder

This study examined whether there is a biological basis in the child's resting brain activity for the intergenerational link between maternal interpersonal violence-related posttraumatic stress disorder (IPV-PTSD) and child subclinical symptoms. We used high-density EEG recordings to investigate the resting brain activity in a sample of 57 children, 34 from mothers with IPV-PTSD, and 23 from mothers without PTSD. These children were part of a prospective, longitudinal study focusing on the offspring of mothers with and without IPV-PTSD, reporting how the severity of a mother's IPV-PTSD can impact her child's emotional regulation and risk for developing mental illness. However, we had not yet looked into potential EEG biomarkers during resting state that might mediate and/or moderate effects of maternal IPV-PTSD severity on child mental health, and in particular the risk for PTSD. The alpha band spectral power as well as the aperiodic exponent of the power spectrum (PLE; power-law exponent) were examined as mediators of maternal IPV-PTSD and child PTSD. While there was no difference in alpha spectral power between the two groups, PLE was significantly reduced in children of mothers with IPV-PTSD compared to control children, indicating cortical hyper-arousal. Interestingly, child PLE was negatively correlated with the severity of maternal IPV-PTSD, suggesting an intergenerational interaction. This interpretation was reinforced by a negative correlation between child PLE and child PTSD symptoms. Finally, causal analyses using structural equation modelling indicated that child PLE mediated the relationship between maternal PTSD severity and child PTSD. Our observations suggest that maternal IPV-PTSD has an intergenerational impact on the child neurobehavioral development through a correlated abnormal marker of brain arousal (i.e. child PLE). These findings are potentially relevant to psychotherapy research and to the development of more effective psycho-neurobehavioral therapies (i.e. neurofeedback) among affected individuals.

Altered long-range functional connectivity in PTSD: Role of the infraslow oscillations of cortical activity amplitude envelopes

OBJECTIVE

Coupling between the amplitude envelopes (AEs) of regional cortical activity reflects mechanisms that coordinate the excitability of large-scale cortical networks. We used resting-state MEG recordings to investigate the association between alterations in the coupling of cortical AEs and symptoms of post-traumatic stress disorder (PTSD).

METHODS

Participants (n = 96) were service members with combat exposure and various levels of post-traumatic stress severity (PTSS). We assessed the correlation between PTSS and (1) coupling of broadband cortical AEs of beta band activity, (2) coupling of the low- (<0.5 Hz) and high-frequency (>0.5 Hz) components of the AEs, and (3) their time-varying patterns.

RESULTS

PTSS was associated with widespread hypoconnectivity assessed from the broadband AE fluctuations, which correlated with subscores for the negative thoughts and feelings/emotional numbing (NTF/EN) and hyperarousal clusters of symptoms. Higher NTF/EN scores were also associated with smaller increases in resting-state functional connectivity (rsFC) with time during the recordings. The distinct patterns of rsFC in PTSD were primarily due to differences in the coupling of low-frequency (infraslow) fluctuations of the AEs of beta band activity.

CONCLUSIONS

Our findings implicate the mechanisms underlying the regulation/coupling of infraslow oscillations in the alterations of rsFC assessed from broadband AEs and in PTSD symptomatology.

SIGNIFICANCE

Altered coordination of infraslow amplitude fluctuations across large-scale cortical networks can contribute to network dysfunction and may provide a target for treatment in PTSD.

A prospective study on EEG default mode network associated with subsequent posttraumatic stress disorder following sexual assault

This study aimed to explore the predictors of posttraumatic stress disorder (PTSD) in women who have recently experienced sexual assault, by examining psychological and neurophysiological factors using a prospective design with resting-state electroencephalogram (EEG) functional connectivity. The study enrolled 33 women who had been recently traumatized by sexual assault and conducted assessments within a month of the trauma. These survivors were evaluated for PTSD three months later and were classified into two groups: PTSD positive (n = 12) and PTSD negative (n = 21). They were compared to two control groups comprising women who had not experienced any extremely traumatic events: 25 with depression and 25 healthy controls. The evaluation focused on resting-state EEG functional connectivity within default mode network (DMN) using small-worldness (SW), based on graph theory. We also assessed self-reported levels of depression, anxiety, anger, and executive functions. The findings indicated that survivors who developed PTSD three months post-trauma exhibited higher anxiety levels and reduced DMN SW in the beta 3 frequency, compared to those who did not develop PTSD. Contrary to expectations, survivors without PTSD showed decreased executive functioning and lower prefrontal centrality compared to those with PTSD. This study underscores the importance of early assessment and intervention for sexual assault survivors at risk of developing PTSD.

Reducing Stress with Yoga: A Systematic Review Based on Multimodal Biosignals

Stress is an enormous concern in our culture because it is the root cause of many health issues. Yoga asanas and mindfulness-based practices are becoming increasingly popular for stress management; nevertheless, the biological effect of these practices on stress reactivity is still a research domain. The purpose of this review is to emphasize various biosignals that reflect stress reduction through various yoga-based practices. A comprehensive synthesis of numerous prior investigations in the existing literature was conducted. These investigations undertook a thorough examination of numerous biosignals. Various features are extracted from these signals, which are further explored to reflect the effectiveness of yoga practice in stress reduction. The multifaceted character of stress and the extensive research undertaken in this field indicate that the proposed approach would rely on multiple modalities. The notable growth of the body of literature pertaining to prospective yoga processes is deserving of attention; nonetheless, there exists a scarcity of research undertaken on these mechanisms. Hence, it is recommended that future studies adopt more stringent yoga methods and ensure the incorporation of suitable participant cohorts.

Early- and late-phase changes of brain activity and early-phase neuromodulation in the posttraumatic stress disorder rat model

Posttraumatic stress disorder (PTSD) is a complex syndrome that may occur after life-threatening events. Fear memory abnormalities may play vital roles in the pathogenesis of PTSD. Previous work has found that fear memories are not rigid; the retrieval of fear memories may change over time. Furthermore, prior studies suggest that theta wave (4 Hz) activity is highly correlated with fear expression in an animal model. However, the relationship between pathological fear memory and potential brain wave features in PTSD remains largely uncharacterized. Here, we hypothesized that after traumatic stress exposure, the longitudinal dynamics of abnormal fears in PTSD animal models could be reflected by the measurement of local field potentials (LFPs). Using a well-established modified single-prolonged stress and footshock (SPS & FS) PTSD rat model, animals were restrained for 2 h and subsequently subjected to 20 min of forced swimming, then exposed to diethyl ether until they lost consciousness and placed in a conditioning chamber for fear conditioning. To characterize the temporal changes, we characterized freezing behavior brain wave features during the conditioning chamber re-exposure in the early (10 and 30 min; 2, 4, and 6 h) and late (day 1, 3, 7, and 14) phases after traumatic stress exposure. Our results indicate that SPS & FS rats showed co-morbid PTSD phenotypes including significantly higher levels of anxiety-, depression-, and anhedonia-like behaviors, and impaired fear extinction. Delta wave (0.5-4 Hz) suppression in the medial prefrontal cortex, amygdala, and ventral hippocampus occurred 10 and 30 min after traumatic stress, followed by continuous delta wave activity from 2 h to day 14, correlating with fear levels. tDCS reduced delta activity and alleviated PTSD-like phenotypes in the SPS & FS group. In this study, profiling abnormal fears with brain wave correlates may improve our understanding of time-dependent pathological fear memory retrieval in PTSD and facilitate the development of effective intervention strategies.

Can RNA Affect Memory Modulation? Implications for PTSD Understanding and Treatment

Memories are a central aspect of our lives, but the mechanisms underlying their formation, consolidation, retrieval, and extinction remain poorly understood. In this review, we explore the molecular mechanisms of memory modulation and investigate the effects of RNA on these processes. Specifically, we examine the effects of time and location on gene expression alterations. We then discuss the potential for harnessing these alterations to modulate memories, particularly fear memories, to alleviate post-traumatic stress disorder (PTSD) symptoms. The current state of research suggests that transcriptional changes play a major role in memory modulation and targeting them through microRNAs may hold promise as a novel approach for treating memory-related disorders such as PTSD.

Impaired functional cortical networks in the theta frequency band of patients with post-traumatic stress disorder during auditory-cognitive processing

Impaired cognitive function related to intrusive memories of traumatic experiences is the most noticeable characteristic of post-traumatic stress disorder (PTSD); nevertheless, the brain mechanism involved in the cognitive processing is still elusive. To improve the understanding of the neuropathology in PTSD patients, we investigated functional cortical networks that are based on graph theory, by using electroencephalogram (EEG). EEG signals, elicited by an auditory oddball paradigm, were recorded from 53 PTSD patients and 39 healthy controls (HCs). Source signals in 68 regions of interests were estimated using EEG data for each subject using minimum-norm estimation. Then, using source signals of each subject, time-frequency analysis was conducted, and a functional connectivity matrix was constructed using the imaginary part of coherence, which was used to evaluate three global-level (strength, clustering coefficient, and path length) and two nodal-level (strength and clustering coefficients) network indices in four frequency bands (theta, alpha, low-beta, and high-beta). The relationships between the network indices and symptoms were evaluated using Pearson's correlation. Compared with HCs, PTSD patients showed significantly reduced spectral powers around P300 periods and significantly altered network indices (diminished strength and clustering coefficient, and prolonged path length) in theta frequency band. In addition, the nodal strengths and nodal clustering coefficients in theta band of PTSD patients were significantly reduced, compared with those of HCs, and the reduced nodal clustering coefficients in parieto-temporo-occipital regions had negative correlations with the symptom scores (Impact of Event Scale-Revises, Beck Depression Inventory, and Beck Anxiety Inventory). The characterization of this disrupted pattern improves the understanding of the neuropathophysiology underlying the impaired cognitive function in PTSD patients.

Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder

Electroencephalography (EEG)-based diagnosis of psychiatric diseases using machine-learning approaches has made possible the objective diagnosis of various psychiatric diseases. The objective of this study was to improve the performance of a resting-state EEG-based computer-aided diagnosis (CAD) system to diagnose post-traumatic stress disorder (PTSD), by optimizing the frequency bands used to extract EEG features. We used eyes-closed resting-state EEG data recorded from 77 PTSD patients and 58 healthy controls (HC). Source-level power spectrum densities (PSDs) of the resting-state EEG data were extracted from 6 frequency bands (delta, theta, alpha, low-beta, high-beta, and gamma), and the PSD features of each frequency band and their combinations were independently used to discriminate PTSD and HC. The classification performance was evaluated using support vector machine with leave-one-out cross validation. The PSD features extracted from slower-frequency bands (delta and theta) showed significantly higher classification performance than those of relatively higher-frequency bands. The best classification performance was achieved when using delta PSD features (86.61%), which was significantly higher than that reported in a recent study by about 13%. The PSD features selected to obtain better classification performances could be explained from a neurophysiological point of view, demonstrating the promising potential to develop a clinically reliable EEG-based CAD system for PTSD diagnosis.

Predictability of Seasonal Mood Fluctuations Based on Self-Report Questionnaires and EEG Biomarkers in a Non-clinical Sample

Induced by decreasing light, people affected by seasonal mood fluctuations may suffer from low energy, have low interest in activities, experience changes in weight, insomnia, difficulties in concentration, depression, and suicidal thoughts. Few studies have been conducted in search for biological predictors of seasonal mood fluctuations in the brain, such as EEG oscillations. A sample of 64 participants was examined with questionnaires and electroencephalography in summer. In winter, a follow-up survey was recorded and participants were grouped into those with at least mild (N = 18) and at least moderate (N = 11) mood decline and those without self-reported depressive symptoms both in summer and in winter (N = 46). A support vector machine was trained to predict mood decline by either EEG biomarkers alone, questionnaire data from baseline alone, or a combination of the two. Leave-one-out-cross validation with lasso regularization was used with logistic regression to fit a model. The accuracy for classification for at least mild/moderate mood decline was 77/82% for questionnaire data, 72/82% for EEG alone, and 81/86% for EEG combined with questionnaire data. Self-report data was more conclusive than EEG biomarkers recorded in summer for prediction of worsening of depressive symptoms in winter but it is advantageous to combine EEG with psychological assessment to boost predictive performance.

Using mobile brain/body imaging to advance research in arts, health, and related therapeutics

The uses of mobile brain/body imaging (MoBI) are expanding and allow for more direct study of the neurophysiological signals associated with behavior in psychotherapeutic encounters. Neuroaesthetics is concerned with the cognitive and neural basis of art appreciation, and scientific correlations are being made in the field that might help to clarify theories claimed in the creative arts therapies. Yet, most neuroaesthetics studies are confined to the laboratory and do not propose a translation for research methods and clinical applications. The creative arts therapies have a long history of clinical success with various patient populations and will benefit from increased scientific explanation to support intervention strategies. Examining the brain dynamics and motor behaviors that are associated with the higher complex processes involved in artistic expression offers MoBI as a promising instrumentation to move forward in linking ideas from neuroaesthetics to the creative arts therapies. Tracking brain dynamics in association with behavioral change allows for more objective and quantitative physiological monitors to evaluate, and together with subjective patient reports provides insight into the psychological mechanisms of change in treatment. We outline a framework that shows how MoBI can be used to study the effectiveness of creative arts therapy interventions motivated by the 4E approach to cognition with a focus on visual art therapy. The article illuminates how a new partnership among the fields of art therapy, neuroscience, and neuroaesthetics might work together within the 4E/MoBI framework in efforts to advance transdisciplinary research for clinical health populations.

Inflated prediction accuracy of neuropsychiatric biomarkers caused by data leakage in feature selection

In recent years, machine learning techniques have been frequently applied to uncovering neuropsychiatric biomarkers with the aim of accurately diagnosing neuropsychiatric diseases and predicting treatment prognosis. However, many studies did not perform cross validation (CV) when using machine learning techniques, or others performed CV in an incorrect manner, leading to significantly biased results due to overfitting problem. The aim of this study is to investigate the impact of CV on the prediction performance of neuropsychiatric biomarkers, in particular, for feature selection performed with high-dimensional features. To this end, we evaluated prediction performances using both simulation data and actual electroencephalography (EEG) data. The overall prediction accuracies of the feature selection method performed outside of CV were considerably higher than those of the feature selection method performed within CV for both the simulation and actual EEG data. The differences between the prediction accuracies of the two feature selection approaches can be thought of as the amount of overfitting due to selection bias. Our results indicate the importance of correctly using CV to avoid biased results of prediction performance of neuropsychiatric biomarkers.

Predicting PTSD severity using longitudinal magnetoencephalography with a multi-step learning framework

Objective The present study explores the effectiveness of incorporating temporal information in predicting Post-Traumatic Stress Disorder (PTSD) severity using magnetoencephalography (MEG) imaging data. The main objective was to assess the relationship between longitudinal MEG functional connectome data, measured across a variety of neural oscillatory frequencies and collected at two-timepoints (Phase I & II), against PTSD severity captured at the later time point. Approach We used an in-house developed informatics solution, featuring a two-step process featuring pre-learn feature selection (CV-SVR-rRF-FS, cross-validation with support vector regression and recursive random forest feature selection) and deep learning (long-short term memory recurrent neural network, LSTM-RNN) techniques. Main results The pre-learn step selected a small number of functional connections (or edges) from Phase I MEG data associated with Phase II PTSD severity, indexed using the PTSD CheckList (PCL) score. This strategy identified the functional edges affected by traumatic exposure and indexed disease severity, either permanently or evolving dynamically over time, for optimal predictive performance. Using the selected functional edges, LSTM modelling was used to incorporate the Phase II MEG data into longitudinal regression models. Single timepoint (Phase I and Phase II MEG data) SVR models were generated for comparison. Assessed with holdout test data, alpha and high gamma bands showed enhanced predictive performance with the longitudinal models comparing to the Phase I single timepoint models. The best predictive performance was observed for lower frequency ranges compared to the higher frequencies (low gamma), for both model types. Significance This study identified the neural oscillatory signatures that benefited from additional temporal information when estimating the outcome of PTSD severity using MEG functional connectome data. Crucially, this approach can similarly be applied to any other mental health challenge, using this effective informatics foundation for longitudinal tracking of pathological brain states and predicting outcome with a MEG-based neurophysiology imaging system.

Associations between resting-state functional connectivity and treatment response in a randomized clinical trial for posttraumatic stress disorder

BACKGROUND

Alterations in resting-state functional connectivity (rsFC) have been reported in posttraumatic stress disorder (PTSD). Here, we examined pre- and post-treatment rsFC during a randomized clinical trial to characterize alterations and examine predictors of treatment response.

METHODS

Sixty-four combat veterans with PTSD were randomly assigned to prolonged exposure (PE) plus placebo, sertraline plus enhanced medication management, or PE plus sertraline. Symptom assessment and resting-state functional magnetic resonance imaging (fMRI) scans occurred before and after treatment. Twenty-nine trauma-exposed combat veterans without PTSD served as a control group at intake. Seed-based and region of interest (ROI)-to-ROI connectivities, as well as an exploratory connectome-based approach were used to analyze rsFC patterns. Based on previously reported findings, analyses focused on Salience Network (SN) and Default-Mode Network (DMN).

RESULTS

At intake, patients with PTSD showed greater DMN-dorsal attention network (DAN) connectivity (between ventromedial prefrontal cortex and superior parietal lobule; family-wise error corrected p = .011), greater SN-DAN connectivity (between insula and middle frontal gyrus; corrected p = .003), and a negative correlation between re-experiencing symptoms and within-DMN connectivity (between posterior cingulate cortex (PCC) and middle temporal gyrus; corrected p < .001). We also found preliminary evidence for associations between rsFC and treatment response. Specifically, high responders (≥50% PTSD symptom improvement), compared with low responders, had greater SN-DMN segregation (i.e., less pre-treatment amygdala-PCC connectivity; p = .011) and lower pre-treatment global centrality (p = .042).

CONCLUSIONS

Our findings suggest neural abnormalities in PTSD and may inform future research examining neural biomarkers of PTSD treatment response.

Neural correlates of script-driven imagery in adolescents with interpersonal traumatic experiences: A pilot study

In adults, trauma imagery has proven to be a useful tool to assess the neural mechanisms of psychological trauma processing. In adolescents, heterogeneous results could be found for other tasks, however, a trauma imagery paradigm has not been evaluated. For this purpose, we investigated a trauma imagery paradigm with control scripts to assess neural correlates of traumatic experiences in youth. 15 adolescents, who had experienced a traumatic interpersonal event in the past and have developed clinically relevant symptoms, underwent an fMRI scan while listening to their individual trauma- versus two control scripts (positive/negative). We analysed a parametric contrast of the imagery phases (trauma > negative > positive) which revealed activity in the thalamus, dorsal anterior cingulate cortex, cuneus, dorsomedial prefrontal cortex and amygdala. Additionally, amygdala-activity correlated positively with depression-symptom-severity. Our data provide evidence for the feasibility of fMRI during a trauma imagery task in adolescents to investigate networks previously related to hyperarousal in adults with PTSD. Further, we demonstrate the specificity of the activated networks for trauma imagery as compared to imagery of other emotional situations. The task might be particularly useful to evaluate neural correlates of treatment in adolescents when hyperarousal is a target symptom.

Rumination in posttraumatic stress disorder: A systematic review

Initial models and empirical investigations of rumination in the clinical literature were predominantly in the domain of depression. However, rumination is now well-established as a transdiagnostic cognitive process, including in the context of posttraumatic stress. To clarify the current understanding of rumination in posttraumatic stress, we conducted a systematic review of the empirical literature on rumination in posttraumatic stress disorder (PTSD). Six sub-groups of studies on this topic were identified; these addressed: (i) the frequency and nature of rumination, (ii) cross-sectional relationships between rumination and PTSD symptoms, (iii) the capacity of rumination to predict PTSD longitudinally, (iv) other processes associated with rumination, (v) neurobiological correlates of rumination, and (vi) whether treating PTSD reduces rumination. This review synthesizes these domains of research and identifies key methodological limitations which limit causal inferences, and points to important areas of future research to advance knowledge on rumination in PTSD.

Nervous and Endocrine System Dysfunction in Posttraumatic Stress Disorder: An Overview and Consideration of Sex as a Biological Variable

Decades of research into the biological mechanisms of posttraumatic stress disorder (PTSD) suggests that chronic activation of the stress response leads to long-lasting changes in the structure and function of the nervous and endocrine systems. While the prevalence of PTSD is twice as high in females as males, little is known about how sex differences in neuroendocrine systems may contribute to PTSD. In response to the paucity of research on sex-related mechanisms, the National Institutes of Health created a policy that asks researchers to consider sex as a biological variable. This review provides an overview of the current understanding of nervous and endocrine dysfunction in PTSD (e.g., neural circuitry, autonomic arousal, hormonal response), highlighting areas where the influence of sex has been characterized and where further research is needed. We also provide recommendations for using the sex-as-a-biological-variable policy to address specific gaps in PTSD neuroscience research.

An Electroencephalography Connectomic Profile of Posttraumatic Stress Disorder

OBJECTIVE

The authors sought to identify brain regions whose frequency-specific, orthogonalized resting-state EEG power envelope connectivity differs between combat veterans with posttraumatic stress disorder (PTSD) and healthy combat-exposed veterans, and to determine the behavioral correlates of connectomic differences.

METHODS

The authors first conducted a connectivity method validation study in healthy control subjects (N=36). They then conducted a two-site case-control study of veterans with and without PTSD who were deployed to Iraq and/or Afghanistan. Healthy individuals (N=95) and those meeting full or subthreshold criteria for PTSD (N=106) underwent 64-channel resting EEG (eyes open and closed), which was then source-localized and orthogonalized to mitigate effects of volume conduction. Correlation coefficients between band-limited source-space power envelopes of different regions of interest were then calculated and corrected for multiple comparisons. Post hoc correlations of connectomic abnormalities with clinical features and performance on cognitive tasks were conducted to investigate the relevance of the dysconnectivity findings.

RESULTS

Seventy-four brain region connections were significantly reduced in PTSD (all in the eyes-open condition and predominantly using the theta carrier frequency). Underconnectivity of the orbital and anterior middle frontal gyri were most prominent. Performance differences in the digit span task mapped onto connectivity between 25 of the 74 brain region pairs, including within-network connections in the dorsal attention, frontoparietal control, and ventral attention networks.

CONCLUSIONS

Robust PTSD-related abnormalities were evident in theta-band source-space orthogonalized power envelope connectivity, which furthermore related to cognitive deficits in these patients. These findings establish a clinically relevant connectomic profile of PTSD using a tool that facilitates the lower-cost clinical translation of network connectivity research.

Neurocognitive Mechanism of Human Resilience: A Conceptual Framework and Empirical Review

Resilience is an innate human capacity that holds the key to uncovering why some people rebound after trauma and others never recover. Various theories have debated the mechanisms underlying resilience at the psychological level but have not yet incorporated neurocognitive concepts/findings. In this paper, we put forward the idea that cognitive flexibility moderates how well people adapt to adverse experiences, by shifting attention resources between cognition–emotion regulation and pain perception. We begin with a consensus on definitions and highlight the role of cognitive appraisals in mediating this process. Shared concepts among appraisal theories suggest that cognition–emotion, as well as pain perception, are cognitive mechanisms that underlie how people respond to adversity. Frontal brain circuitry sub-serves control of cognition and emotion, connecting the experience of physical pain. This suggests a substantial overlap between these phenomena. Empirical studies from brain imaging support this notion. We end with a discussion of how the role of the frontal brain network in regulating human resilience, including how the frontal brain network interacts with cognition–emotion–pain perception, can account for cognitive theories and why cognitive flexibilities’ role in these processes can create practical applications, analogous to the resilience process, for the recovery of neural plasticity.

The Electrical Aftermath: Brain Signals of Posttraumatic Stress Disorder Filtered Through a Clinical Lens

This review aims to identify patterns of electrical signals identified using electroencephalography (EEG) linked to posttraumatic stress disorder (PTSD) diagnosis and symptom dimensions. We filter EEG findings through a clinical lens, evaluating nuances in findings according to study criteria and participant characteristics. Within the EEG frequency domain, greater right than left parietal asymmetry in alpha band power is the most promising marker of PTSD symptoms and is linked to exaggerated physiological arousal that may impair filtering of environmental distractors. The most consistent findings within the EEG time domain focused on event related potentials (ERPs) include: 1) exaggerated frontocentral responses (contingent negative variation, mismatch negativity, and P3a amplitudes) to task-irrelevant distractors, and 2) attenuated parietal responses (P3b amplitudes) to task-relevant target stimuli. These findings suggest that some individuals with PTSD suffer from attention dysregulation, which could contribute to problems concentrating on daily tasks and goals in lieu of threatening distractors. Future research investigating the utility of alpha asymmetry and frontoparietal ERPs as diagnostic and predictive biomarkers or intervention targets are recommended.

Brain Networks Reorganization During Maturation and Healthy Aging-Emphases for Resilience

Maturation and aging are important life periods that are linked to drastic brain reorganization processes which are essential for mental health. However, the development of generalized theories for delimiting physiological and pathological brain remodeling through life periods linked to healthy states and resilience on one side or mental dysfunction on the other remains a challenge. Furthermore, important processes of preservation and compensation of brain function occur continuously in the cerebral brain networks and drive physiological responses to life events. Here, we review research on brain reorganization processes across the lifespan, demonstrating brain circuits remodeling at the structural and functional level that support mental health and are parallelized by physiological trajectories during maturation and healthy aging. We show evidence that aberrations leading to mental disorders result from the specific alterations of cerebral networks and their pathological dynamics leading to distinct excitability patterns. We discuss how these series of large-scale responses of brain circuits can be viewed as protective or malfunctioning mechanisms for the maintenance of mental health and resilience.

Increased Frontal Gamma and Posterior Delta Powers as Potential Neurophysiological Correlates Differentiating Posttraumatic Stress Disorder from Anxiety Disorders

OBJECTIVE

Posttraumatic stress disorder (PTSD) is distinct from anxiety disorders in its etiology and clinical symptomatology, and was reclassified into trauma- and stressor-related disorders in DSM-5. This study aimed to find neurophysiological correlates differentiating PTSD from anxiety disorders using resting-state quantitative electroencephalography (qEEG).

METHODS

Thirty-six patients with either PTSD or acute stress disorder and 79 patients with anxiety disorder were included in the analysis. qEEG data of absolute and relative powers and patients' medication status on the day of qEEG examination were obtained. Electrodes were grouped into frontal, central, and posterior regions to analyze for regional differences. General linear models were utilized to test for group differences in absolute and relative powers while controlling for medications.

RESULTS

PTSD patients differed from those with anxiety disorders in overall absolute powers [F(5,327)=2.601, p=0.025]. Specifically, overall absolute delta powers [F(1,331)=4.363, p=0.037], and overall relative gamma powers [F(1,331)=3.965, p=0.047] were increased in PTSD group compared to anxiety disorder group. Post hoc analysis regarding brain regions showed that the increase in absolute delta powers were localized to the posterior region [F(1,107)=4.001, p=0.048]. Additionally, frontal absolute gamma powers [F(1,107)=4.138, p=0.044] were increased in PTSD group compared to anxiety disorder group.

CONCLUSION

Our study suggests increased overall absolute delta powers and relative gamma powers as potential markers that could differentiate PTSD from anxiety disorders. Moreover, increased frontal absolute gamma and posterior delta powers might pose as novel markers of PTSD, which may reflect its distinct symptomatology.

Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study

Background

Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD).

Method

Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales.

Results

At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales.

Conclusion

Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD.

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We investigated the altered function of electroencephalogram-based cortical network for major depressive disorder (MDD).

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MDD showed altered cortical brain network at both global and nodal level network in alpha frequency band.

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Abnormal network indices in MDD were significantly correlated with depression and anxiety symptom scale scores.

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Disrupted cortical networks band might reflect altered neural pathway during emotional processing in patients with MDD.