Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach

Speech-based recognition and estimating severity of PTSD using machine learning

BACKGROUND

Traditional methodologies for diagnosing post-traumatic stress disorder (PTSD) primarily rely on interviews, incurring considerable costs and lacking objective indices. Integrating biomarkers and machine learning techniques into this diagnostic process has the potential to facilitate accurate PTSD assessment by clinicians.

METHODS

We assembled a dataset encompassing recordings from 76 individuals diagnosed with PTSD and 60 healthy controls. Leveraging the openSmile framework, we extracted acoustic features from these recordings and employed a random forest algorithm for feature selection. Subsequently, these selected features were utilized as inputs for six distinct classification models and a regression model.

RESULTS

Classification models employing a feature set of 18 elements yielded robust binary prediction outcomes for PTSD. Notably, the RF model achieved peak accuracy at 0.975 with the highest AUC of 1.0. In terms of the regression model, it exhibited significant predictive capability for PCL-5 scores (MSE = 0.90, MAE = 0.76, R2 = 0.10, p < 0.001). Noteworthy was the correlation coefficient of 0.33 (p < 0.05) between predicted and actual values.

LIMITATIONS

Firstly, the process of feature selection may compromise the stability of models, which leads to potentially overestimating results. Secondly, it is hard to elucidate the nature of biological mechanisms behind between PTSD patients and healthy individuals. Lastly, the regression model has a limited prediction for PTSD.

CONCLUSIONS

Distinct speech patterns differentiate PTSD patients and controls. Classification models accurately discern both groups. Regression model gauges PTSD severity, but further validation on larger datasets is needed.

The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

Application of machine learning techniques in the diagnostic approach of PTSD using MRI neuroimaging data: A systematic review

Background

At present, the diagnosis of post-traumatic stress disorder（PTSD） mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging（DTI）are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis.

Objectives

According to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls).

Methods

We searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors.

Results

All ML techniques have an diagnostic accuracy rate above 70%，and support vector machine（SVM） are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis.

Conclusion

In contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.

Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data

This study aimed to identify neural biomarkers for schizophrenia (SZ) and bipolar disorder (BP) by analyzing multimodal neuroimaging. Utilizing data from structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI), multiclass classification models were created for SZ, BP, and healthy controls (HC). A total of 113 participants (BP: 31, SZ: 39, and HC: 43) were recruited under strict enrollment control, from which 272, 200, and 1875 features were extracted from sMRI, DTI, and rs-fMRI data, respectively. A support vector machine (SVM) with recursive feature elimination (RFE) was employed to build the models using a one-against-one approach and leave-one-out cross-validation, achieving a classification accuracy of 70.8%. The most discriminative features were primarily from rs-fMRI, along with significant findings in sMRI and DTI. Key biomarkers identified included the increased thickness of the left cuneus cortex and decreased regional functional connectivity strength (rFCS) in the left supramarginal gyrus as shared indicators for BP and SZ. Additionally, decreased fractional anisotropy in the left superior fronto-occipital fasciculus was suggested as specific to BP, while decreased rFCS in the left inferior parietal area might serve as a specific biomarker for SZ. These findings underscore the potential of multimodal neuroimaging in distinguishing between BP and SZ and contribute to the understanding of their neural underpinnings.

Sensory alterations in post-traumatic stress disorder

PTSD is characterized by difficulties in accurately evaluating the threat value of sensory stimuli. While the role of canonical fear and threat neural circuitry in this ability has been well studied, recent lines of evidence suggest a need to include more emphasis on sensory processing in the conceptualization of PTSD symptomology. Specifically, studies have demonstrated a strong association between variability in sensory processing regions and the severity of PTSD symptoms. In this review, we summarize recent findings that underscore the importance of sensory processing in PTSD, in addition to the structural and functional characteristics of associated sensory brain regions. First, we discuss the link between PTSD and various behavioral aspects of sensory processing. This is followed by a discussion of recent findings that link PTSD to variability in the structure of both gray and white matter in sensory brain regions. We then delve into how brain activity (measured with task-based and resting-state functional imaging) in sensory regions informs our understanding of PTSD symptomology.

Neural correlates of PTSD in women with childhood sexual abuse with and without PTSD and response to paroxetine treatment: A placebo-controlled, double-blind trial

Objective

Childhood sexual abuse is the leading cause of posttraumatic stress disorder (PTSD) in women, and is a prominent cause of morbidity and loss of function for which limited treatments are available. Understanding the neurobiology of treatment response is important for developing new treatments. The purpose of this study was to assess neural correlates of personalized traumatic memories in women with childhood sexual abuse with and without PTSD, and to assess response to treatment.

Methods

Women with childhood sexual abuse with (N = 28) and without (N = 17) PTSD underwent brain imaging with High-Resolution Positron Emission Tomography scanning with radiolabeled water for brain blood flow measurements during exposure to personalized traumatic scripts and memory encoding tasks. Women with PTSD were randomized to paroxetine or placebo followed by three months of double-blind treatment and repeat imaging with the same protocol.

Results

Women with PTSD showed decreases in areas involved in the Default Mode Network (DMN), a network of brain areas usually active when the brain is at rest, hippocampus and visual processing areas with exposure to traumatic scripts at baseline while women without PTSD showed increased activation in superior frontal gyrus and other areas (p < 0.005). Treatment of women with PTSD with paroxetine resulted in increased anterior cingulate activation and brain areas involved in the DMN and visual processing with scripts compared to placebo (p < 0.005).

Conclusion

PTSD related to childhood sexual abuse in women is associated with alterations in brain areas involved in memory and the stress response and treatment with paroxetine results in modulation of these areas.

Altered effective connectivity from cerebellum to motor cortex in chronic low back pain: A multivariate pattern analysis and spectral dynamic causal modeling study

To explore the central processing mechanism of pain perception in chronic low back pain (cLBP) using multi-voxel pattern analysis (MVPA) based on the static and dynamic fractional amplitude of low-frequency fluctuations (fALFF) analysis, and spectral dynamic causal modeling (spDCM). Thirty-two patients with cLBP and 29 matched healthy controls (HCs) for the first cohort and 24 patients with cLBP and 22 HCs for the validation cohort underwent resting-state fMRI scan. The alterations in static and dynamic fALFF were as classification features to distinguish patients with cLBP from HCs. The brain regions gotten from the MVPA results were used for further spDCM analysis. We found that the most discriminative brain regions that contributed to the classification were the right supplementary motor area (SMA.R), left paracentral lobule (PCL.L), and bilateral cerebellar Crus II. The spDCM results displayed decreased excitatory influence from the bilateral cerebellar Crus II to PCL.L in patients with cLBP compared with HCs. Moreover, the conversion of effective connectivity from the bilateral cerebellar Crus II to SMA.R from excitatory influence to inhibitive influence, and the effective connectivity strength exhibited partially mediated effects on Chinese Short Form Oswestry Disability Index Questionnaire (C-SFODI) scores. Our findings suggest that the cerebellum and its weakened or inhibited connections to the motor cortex may be one of the underlying feedback pathways for pain perception in cLBP, and partially mediate the degree of dysfunction.

Systematic review of machine learning in PTSD studies for automated diagnosis evaluation

Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.

Altered Spontaneous Brain Activity and Its Predictive Role in Patients with Central Retinal Artery Occlusion Using fMRI and Machine Learning

Objective

To investigate spontaneous neuronal activity changes in patients with central retinal artery occlusion (CRAO) using the resting-state functional magnetic resonance imaging (fMRI) and detect whether these brain functional alterations can represent an objective biomarker of clinical response using a machine learning algorithm.

Methods

Eighteen patients with CRAO and eighteen healthy controls (HCs) were recruited. The regional homogeneity (ReHo) method of resting-state fMRI was conducted to evaluate the synchronous brain activity alterations between two groups. Differences of ReHo values between two groups were compared using the independent two-sample t-test. The support vector machine algorithm was to distinguish patients of CRAO from HCs based on the two groups' whole-brain ReHo patterns. The accuracy, sensitivity, and specificity for the classification were calculated. The classification performance was evaluated using the non-parametric permutation test.

Results

Compared to HCs, individuals with CRAO showed significantly lower ReHo in the right cerebellum and precuneus. Meanwhile, significant higher ReHo values were observed in the left superior temporal gyrus, postcentral gyrus, and precentral gyrus in the CRAO group compared to HCs. Furthermore, our results suggested that 77.78% individuals with CRAO could be successfully distinguished from HCs by the machine learning, with a sensitivity of 72.22% and a specificity of 83.33%, respectively. The area of receiver operating characteristic curve was calculated to be 0.85.

Conclusion

This study uncovered individuals with CRAO exhibited disturbed synchronous neuronal activities in multiple brain areas using neuroimaging techniques. The ReHo variability could distinguish individuals with CRAO from HCs with high accuracy.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

BACKGROUND

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

METHODS

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.

RESULTS

A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  - 2.75, p < .001, R²_adj = 0.40; r =  - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF₁₀ > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance.

CONCLUSIONS

Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Pre- and post-task resting-state differs in clinical populations

Resting-state functional connectivity has generated great hopes as a potential brain biomarker for improving prevention, diagnosis, and treatment in psychiatry. This neuroimaging protocol can routinely be performed by patients and does not depend on the specificities of a task. Thus, it seems ideal for big data approaches that require aggregating data across multiple studies and sites. However, technical variability, diverging data analysis approaches, and differences in data acquisition protocols introduce heterogeneity to the aggregated data. Besides these technical aspects, a prior task that changes the psychological state of participants might also contribute to heterogeneity. In healthy participants, studies have shown that behavioral tasks can influence resting-state measures, but such effects have not yet been reported in clinical populations. Here, we fill this knowledge gap by comparing resting-state functional connectivity before and after clinically relevant tasks in two clinical conditions, namely substance use disorders and phobias. The tasks consisted of viewing craving-inducing and spider anxiety provoking pictures that are frequently used in cue-reactivity studies and exposure therapy. We found distinct pre- vs post-task resting-state connectivity differences in each group, as well as decreased thalamo-cortical and increased intra-thalamic connectivity which might be associated with decreased vigilance in both groups. Our results confirm that resting-state measures can be strongly influenced by prior emotion-inducing tasks that need to be taken into account when pooling resting-state scans for clinical biomarker detection. This demands that resting-state datasets should include a complete description of the experimental design, especially when a task preceded data collection.

Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research

Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.

Functional and structural brain abnormalities in posttraumatic stress disorder: A multimodal meta-analysis of neuroimaging studies

BACKGROUND

Numerous resting-state functional and structural studies have revealed that many brain regions are involved in the pathogenesis of posttraumatic stress disorder (PTSD), but their findings have been inconsistent. Moreover, there has no study explored the functional and structural alterations across languages in PTSD.

METHODS

A meta-analysis of whole-brain on the amplitude of low-frequency fluctuation (ALFF) and voxel-based morphometry (VBM) studies that explored alterations in the spontaneous functional brain activity and grey matter volume (GMV) in PTSD patients across languages by using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software.

RESULTS

A total of 15 studies (19 datasets) comprising 577 PTSD patients and 499 HCs for ALFF, and 27 studies (31 datasets) comprising 539 PTSD patients and 693 HCs for VBM were included. Overall, PTSD patients across languages displayed decreased ALFF in the in the left amygdala. For VBM meta-analysis, PTSD patients across languages displayed reduced GMV in the bilateral anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC), striatum, insula, superior temporal gyrus, left postcentral gyrus, and occipital gyrus.

CONCLUSIONS

The multimodal meta-analysis suggest that PTSD patients showed similar pattern of aberrant resting-state functional brain activity and structure mainly in the amygdala, suggesting that structural deficits might underlie alterations in function. In addition, some regions exhibited only structural abnormalities in PTSD, including the ACC/mPFC, striatum, insula, primary visual, auditory and sensorimotor cortices. Moreover, consistent alterations in PTSD patients across languages may draw attention to the disparity in multi-cultural considerations in psychiatric research and further understanding the neurophysiopathology of PTSD.

Contrasting Associations Between Heart Rate Variability and Brainstem-Limbic Connectivity in Posttraumatic Stress Disorder and Its Dissociative Subtype: A Pilot Study

Background

Increasing evidence points toward the need to extend the neurobiological conceptualization of posttraumatic stress disorder (PTSD) to include evolutionarily conserved neurocircuitries centered on the brainstem and the midbrain. The reticular activating system (RAS) helps to shape the arousal state of the brain, acting as a bridge between brain and body. To modulate arousal, the RAS is closely tied to the autonomic nervous system (ANS). Individuals with PTSD often reveal altered arousal patterns, ranging from hyper- to blunted arousal states, as well as altered functional connectivity profiles of key arousal-related brain structures that receive direct projections from the RAS. Accordingly, the present study aims to explore resting state functional connectivity of the RAS and its interaction with the ANS in participants with PTSD and its dissociative subtype.

Methods

Individuals with PTSD (n = 57), its dissociative subtype (PTSD + DS, n = 32) and healthy controls (n = 40) underwent a 6-min resting functional magnetic resonance imaging and pulse data recording. Resting state functional connectivity (rsFC) of a central node of the RAS – the pedunculopontine nuclei (PPN) – was investigated along with its relation to ANS functioning as indexed by heart rate variability (HRV). HRV is a prominent marker indexing the flexibility of an organism to react adaptively to environmental needs, with higher HRV representing greater effective adaptation.

Results

Both PTSD and PTSD + DS demonstrated reduced HRV as compared to controls. HRV measures were then correlated with rsFC of the PPN. Critically, participants with PTSD and participants with PTSD + DS displayed inverse correlations between HRV and rsFC between the PPN and key limbic structures, including the amygdala. Whereas participants with PTSD displayed a positive relationship between HRV and PPN rsFC with the amygdala, participants with PTSD + DS demonstrated a negative relationship between HRV and PPN rsFC with the amygdala.

Conclusion

The present exploratory investigation reveals contrasting patterns of arousal-related circuitry among participants with PTSD and PTSD + DS, providing a neurobiological lens to interpret hyper- and more blunted arousal states in PTSD and PTSD + DS, respectively.

Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM)

Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found that sole decision by the ML algorithms is not accurate enough to implement the treatment procedure. Thus, an intelligent decision is required further by the radiologists after evaluating the ML outcomes. The current research is based on the critical ML, where radiologists' critical thinking ability, IQ (intelligence quotient), and experience in radiology have been examined to understand how these factors affect the accuracy of neuroimaging discrimination. A primary quantitative survey has been carried out, and the data were analysed in IBM SPSS. The results showed that experience in works has a positive impact on neuroimaging discrimination accuracy. IQ and trained ML are also responsible for improving the accuracy as well. Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.

A Pilot Randomized Controlled Trial of Goal Management Training in Canadian Military Members, Veterans, and Public Safety Personnel Experiencing Post-Traumatic Stress Symptoms

Post-traumatic stress disorder (PTSD) is a severe psychiatric illness that disproportionately affects military personnel, veterans, and public safety personnel (PSP). Evidence demonstrates that PTSD is significantly associated with difficulties with emotion regulation (ER) and difficulties with cognitive functioning, including difficulties with attention, working memory, and executive functioning. A wide body of evidence suggests a dynamic interplay among cognitive dysfunction, difficulties with ER, and symptoms of PTSD, where numerous studies have identified overlapping patterns of alterations in activation among neuroanatomical regions and neural circuitry. Little work has examined interventions that may target these symptoms collectively. The primary objective of this pilot randomized controlled trial (RCT) with a parallel experimental design was to assess the effectiveness of goal management training (GMT), a cognitive remediation intervention, in reducing difficulties with cognitive functioning, and to determine its effects on PTSD symptoms and symptoms associated with PTSD, including difficulties with ER, dissociation, and functioning among military personnel, veterans, and PSP. Forty-two military personnel, veterans, and PSP between the ages of 18 and 70 with symptoms of PTSD were recruited across Ontario, Canada between October 2017 and August 2019. Participants were randomized to either the waitlist (WL) (n = 18) or the GMT (n = 22) condition. Participants in both conditions received self-report measures and a comprehensive neuropsychological assessment at baseline, post-intervention, and 3-month follow-up. Following their completion of the 3-month follow-up, participants in the WL condition were given the opportunity to participate in GMT. Assessors and participants were blind to intervention allocation during the initial assessment. A series of 2 (time) × 2 (group) ANOVAs were conducted to assess the differences between the WL and GMT conditions from pre- to post-intervention for the self-report and neuropsychological measures. The results demonstrated significant improvements in measures of executive functioning (e.g., verbal fluency, planning, impulsivity, cognitive shifting, and discrimination of targets) and trending improvements in short-term declarative memory for participants in the GMT condition. Participants in the GMT condition also demonstrated significant improvements from pre- to post-testing in measures of subjective cognition, functioning, PTSD symptom severity, difficulties with ER, dissociative symptom severity, and depression and anxiety symptoms. No adverse effects were reported as a result of participating in GMT. The results of this pilot RCT show promise that GMT may be a useful intervention to improve symptoms of cognitive dysfunction, symptoms of PTSD, and symptoms associated with PTSD within military personnel, veterans, and PSP. Future work is needed to address the small sample size and the durability of these findings.

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.

Trauma-related dissociation and the autonomic nervous system: a systematic literature review of psychophysiological correlates of dissociative experiencing in PTSD patients

Background: Neurophysiological models link dissociation (e.g. feeling detached during or after a traumatic event) to hypoarousal. It is currently assumed that the initial passive reaction to a threat may coincide with a blunted autonomic response, which constitutes the dissociative subtype of post-traumatic stress disorder (PTSD). Objective: Within this systematic review we summarize research which evaluates autonomic nervous system activation (e.g. heart rate, blood pressure) and dissociation in PTSD patients to discern the validity of current neurophysiological models of trauma-related hypoarousal. Method: Of 553 screened articles, 28 studies (N = 1300 subjects) investigating the physiological response to stress provocation or trauma-related interventions were included in the final analysis. Results: No clear trend exists across all measured physiological markers in trauma-related dissociation. Extracted results are inconsistent, in part due to high heterogeneity in experimental methodology. Conclusion: The current review is unable to provide robust evidence that peri- and post-traumatic dissociation are associated with hypoarousal, questioning the validity of distinct psychophysiological profiles in PTSD.

A systematic review of the neural correlates of sexual minority stress: towards an intersectional minority mosaic framework with implications for a future research agenda

BACKGROUND

Systemic oppression, particularly towards sexual minorities, continues to be deeply rooted in the bedrock of many societies globally. Experiences with minority stressors (e.g. discrimination, hate-crimes, internalized homonegativity, rejection sensitivity, and microaggressions or everyday indignities) have been consistently linked to adverse mental health outcomes. Elucidating the neural adaptations associated with minority stress exposure will be critical for furthering our understanding of how sexual minorities become disproportionately affected by mental health burdens.

Following PRISMA-guidelines, we systematically reviewed published neuroimaging studies that compared neural dynamics among sexual minority and heterosexual populations, aggregating information pertaining to any measurement of minority stress and relevant clinical phenomena.

RESULTS

Only 1 of 13 studies eligible for inclusion examined minority stress directly, where all other studies focused on investigating the neurobiological basis of sexual orientation. In our narrative synthesis, we highlight important themes that suggest minority stress exposure may be associated with decreased activation and functional connectivity within the default-mode network (related to the sense-of-self and social cognition), and summarize preliminary evidence related to aberrant neural dynamics within the salience network (involved in threat detection and fear processing) and the central executive network (involved in executive functioning and emotion regulation). Importantly, this parallels neural adaptations commonly observed among individuals with posttraumatic stress disorder (PTSD) in the aftermath of trauma and supports the inclusion of insidious forms of trauma related to minority stress within models of PTSD.

CONCLUSIONS

Taken together, minority stress may have several shared neuropsychological pathways with PTSD and stress-related disorders. Here, we outline a detailed research agenda that provides an overview of literature linking sexual minority stress to PTSD and insidious trauma, moral affect (including shame and guilt), and mental health risk/resiliency, in addition to racial, ethnic, and gender related minority stress. Finally, we propose a novel minority mosaic framework designed to inform future directions of minority stress neuroimaging research from an intersectional lens.

Contributions of diagnostic, cognitive, and somatovisceral information to the prediction of fear ratings in spider phobic and non-spider-fearful individuals

BACKGROUND

Physiological responding is a key characteristic of fear responses. Yet, it is unknown whether the time-consuming measurement of somatovisceral responses ameliorates the prediction of individual fear responses beyond the accuracy reached by the consideration of diagnostic (e.g., phobic vs. non phobic) and cognitive (e.g., risk estimation) factors, which can be more easily assessed.

METHOD

We applied a machine learning approach to data of an experiment, in which spider phobic and non-spider fearful participants (diagnostic factor) faced pictures of spiders. For each experimental trial, participants specified their personal risk of encountering the spider (cognitive factor), as well as their subjective fear (outcome variable) on quasi-continuous scales, while diverse somatovisceral responses were registered (heart rate, electrodermal activity, respiration, facial muscle activity).

RESULTS

The machine-learning analyses revealed that fear ratings were predominantly predictable by the diagnostic factor. Yet, when allowing for learning of individual patterns in the data, somatovisceral responses contributed additional information on the fear ratings, yielding a prediction accuracy of 81% explained variance. Moreover, heart rate prior to picture onset, but not heart rate reactivity increased predictive power.

LIMITATIONS

Fear was solely assessed by verbal reports, only 27 females were considered, and no generalization to other anxiety disorders is possible.

CONCLUSIONS

After training the algorithm to learn about individual-specific responding, somatovisceral patterns can be successfully exploited. Our findings further point to the possibility that the expectancy-related autonomic state throughout the experiment predisposes an individual to experience specific levels of fear, with less influence of the actual visual stimulations.

Distinct patterns of resting-state connectivity in U.S. service members with mild traumatic brain injury versus posttraumatic stress disorder

Mild traumatic brain injury (mTBI) is highly prevalent in military populations, with many service members suffering from long-term symptoms. Posttraumatic stress disorder (PTSD) often co-occurs with mTBI and predicts worse clinical outcomes. Functional neuroimaging research suggests there are both overlapping and distinct patterns of resting-state functional connectivity (rsFC) in mTBI versus PTSD. However, few studies have directly compared rsFC of cortical networks in military service members with these two conditions. In the present study, U.S. service members (n = 137; ages 19-59; 120 male) underwent resting-state fMRI scans. Participants were divided into three study groups: mTBI only, PTSD only, and orthopedically injured (OI) controls. Analyses investigated group differences in rsFC for cortical networks: default mode (DMN), frontoparietal (FPN), salience, somatosensory, motor, auditory, and visual. Analyses were family-wise error (FWE) cluster-corrected and Bonferroni-corrected for number of network seeds regions at the whole brain level (p_FWE < 0.002). Both mTBI and PTSD groups had reduced rsFC for DMN and FPN regions compared with OI controls. These group differences were largely driven by diminished connectivity in the PTSD group. rsFC with the middle frontal gyrus of the FPN was increased in mTBI, but decreased in PTSD. Overall, these results suggest that PTSD symptoms may have a more consistent signal than mTBI. Our novel findings of opposite patterns of connectivity with lateral prefrontal cortex highlight a potential biomarker that could be used to differentiate between these conditions.

Mismatch between Tissue Partial Oxygen Pressure and Near-Infrared Spectroscopy Neuromonitoring of Tissue Respiration in Acute Brain Trauma: The Rationale for Implementing a Multimodal Monitoring Strategy

The brain tissue partial oxygen pressure (PbtO₂) and near-infrared spectroscopy (NIRS) neuromonitoring are frequently compared in the management of acute moderate and severe traumatic brain injury patients; however, the relationship between their respective output parameters flows from the complex pathogenesis of tissue respiration after brain trauma. NIRS neuromonitoring overcomes certain limitations related to the heterogeneity of the pathology across the brain that cannot be adequately addressed by local-sample invasive neuromonitoring (e.g., PbtO₂ neuromonitoring, microdialysis), and it allows clinicians to assess parameters that cannot otherwise be scanned. The anatomical co-registration of an NIRS signal with axial imaging (e.g., computerized tomography scan) enhances the optical signal, which can be changed by the anatomy of the lesions and the significance of the radiological assessment. These arguments led us to conclude that rather than aiming to substitute PbtO₂ with tissue saturation, multiple types of NIRS should be included via multimodal systemic- and neuro-monitoring, whose values then are incorporated into biosignatures linked to patient status and prognosis. Discussion on the abnormalities in tissue respiration due to brain trauma and how they affect the PbtO₂ and NIRS neuromonitoring is given.

Fear-induced brain activations distinguish anxious and trauma-exposed brains

Translational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning

BACKGROUND

Recent research has identified a number of pre-traumatic, peri-traumatic and post-traumatic psychological and ecological factors that put an individual at increased risk for developing PTSD following a life-threatening event. While these factors have been found to be associated with PTSD in univariate analyses, the complex interactions of these risk factors and how they contribute to individual trajectories of the illness are not yet well understood. In this study, we examine the impact of prior trauma, psychopathology, sociodemographic characteristics, community and environmental information, on PTSD onset in a nationally representative sample of adults in the United States, using machine learning methods to establish the relative contributions of each variable.

METHODS

Individual risk factors identified in Waves 1 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were combined with community-level data for the years concurrent to the NESARC Wave 1 (n = 43,093) and 2 (n = 34,653) surveys. Machine learning feature selection and classification analyses were used at the national level to create models using individual- and community-level variables that would best predict the new onset of PTSD at Wave 2.

RESULTS

Our classification algorithms yielded 89.7 to 95.6% accuracy for predicting new onset of PTSD at Wave 2. A prior diagnosis of DSM-IV-TR Borderline Personality Disorder, Major Depressive Disorder or Anxiety Disorder conferred the greatest relative influence in new diagnosis of PTSD. Distal risk factors such as prior psychiatric diagnosis accounted for significantly greater relative risk than proximal factors (such as adverse event exposure).

CONCLUSIONS

Our findings show that a machine learning classification approach can successfully integrate large numbers of known risk factors for PTSD into stronger models that account for high-dimensional interactions and collinearity between variables. We discuss the implications of these findings as pertaining to the targeted mobilization emergency mental health resources. These findings also inform the creation of a more comprehensive risk assessment profile to the likelihood of developing PTSD following an extremely adverse event.

Hidden wounds of violence: Abnormal motor oscillatory brain activity is related to posttraumatic stress symptoms

Victims of urban violence are at risk of developing Posttraumatic Stress Disorder (PTSD), one of the most debilitating consequences of violence. Considering that PTSD may be associated with inefficient selection of defensive responses, it is important to understand the relation between motor processing and PTSD. The present study aims to investigate the extent to which the severity of posttraumatic stress symptoms (PTSS) is related to motor preparation against visual threat cues in victims of urban violence. Participants performed a choice reaction time task while ignoring a picture that could be threating or neutral. The EEG indices extracted were the motor-related amplitude asymmetry (MRAA) in the alpha frequency range, and the lateralized readiness potential (LRP). We observed a linear relation between longer LRP latency and a slower reaction time, selectively during threat processing (compared to neutral) in low PTSS, but not in high PTSS participants. Alpha MRAA suppression and the PTSS were also linearly related: the smaller the alpha MRAA suppression in the threat condition relative to neutral, the greater the PTSS. These results provide evidence that threatening cues affect motor processing that is modulated by the severity of PTSS in victims of urban violence.

Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity

The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last two decades, the application of neuroimaging techniques in acupuncture research provided visualized evidence for acupuncture promoting neuroplasticity. Recently, the integration of machine learning (ML) and neuroimaging techniques becomes a focus in neuroscience and brings a new and promising approach to understand the facilitation of acupuncture on neuroplasticity at the individual level. This review is aimed at providing an overview of this rapidly growing field by introducing the commonly used ML algorithms in neuroimaging studies briefly and analyzing the characteristics of the acupuncture studies based on ML and neuroimaging, so as to provide references for future research.

Structural and functional brain alterations in psychiatric patients with dissociative experiences: A systematic review of magnetic resonance imaging studies

INTRODUCTION

There is currently no general agreement on how to best conceptualize dissociative symptoms and whether they share similar neural underpinnings across dissociative disorders. Neuroimaging data could help elucidate these questions.

OBJECTIVES

The objective of this review is to summarize empirical evidence for neural aberrations observed in patients suffering from dissociative symptoms.

METHODS

A systematic literature review was conducted including patient cohorts diagnosed with primary dissociative disorders, post-traumatic stress disorder (PTSD), or borderline personality disorder.

RESULTS

Results from MRI studies reporting structural (gray matter and white matter) and functional (during resting-state and task-related activation) brain aberrations were extracted and integrated. In total, 33 articles were included of which 10 pertained to voxel-based morphology, 2 to diffusion tensor imaging, 10 to resting-state fMRI, and 11 to task-related fMRI. Overall findings indicated aberrations spread across diverse brain regions, especially in the temporal and frontal cortices. Patients with dissociative identity disorder and with dissociative PTSD showed more overlap in brain activation than each group showed with depersonalization/derealization disorder.

CONCLUSION

In conjunction, the results indicate that dissociative processing cannot be localized to a few distinctive brain regions but rather corresponds to differential neural signatures depending on the symptom constellation.

Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample

BACKGROUND

The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The recent application of machine learning techniques to develop neuroimaging-based biomarkers has yielded promising preliminary results. However, much of the work in this domain has not met best practice standards from the field of machine learning. This is especially true for studies of anxiety, creating uncertainty about the potential for anxiety biomarker development.

METHODS

We applied machine learning tools to predict trait anxiety from neuroimaging measurements in humans. Using publicly available data from the Brain Genomics Superstruct Project, we compared a suite of neuroimaging-based machine learning models predicting anxiety within a discovery sample (n = 531, 307 women) via k-fold cross-validation, and we tested the final model (a stacked model incorporating region-to-region functional connectivity, amygdala seed-to-voxel connectivity, and volumetric and cortical thickness data) in a held-out, unseen test sample (n = 348, 209 women).

RESULTS

Though the best model was able to predict anxiety within the discovery sample (cross-validated R2 of .06, permutation test p < .001), the generalization test within the holdout sample failed (R2 of -.04, permutation test p > .05).

CONCLUSIONS

In this study, we did not find evidence of a generalizable anxiety biomarker. However, we encourage other researchers to investigate this topic, utilizing large samples and proper methodology, to clarify the potential of neuroimaging-based anxiety biomarkers.

The hijacked self: Disrupted functional connectivity between the periaqueductal gray and the default mode network in posttraumatic stress disorder using dynamic causal modeling

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Posttraumatic stress disorder (PTSD) shows altered effective connectivity dynamics.

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Modeling between the periaqueductal gray (PAG) and the default mode network (DMN).

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In PTSD, stronger excitatory effective connectivity from the PAG towards the DMN.

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Trauma-related/neutral stimulus modulations to effective connectivity are compared.

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In PTSD, trauma-related stimulus modulations differ significantly to the controls.

Self-related processes define assorted self-relevant or social-cognitive functions that allow us to gather insight and to draw inferences related to our own mental conditions. Self-related processes are mediated by the default mode network (DMN), which, critically, shows altered functionality in individuals with posttraumatic stress disorder (PTSD). In PTSD, the midbrain periaqueductal gray (PAG) demonstrates stronger functional connectivity with the DMN [i.e., precuneus (PCN), medial prefrontal cortex (mPFC)] as compared to healthy individuals during subliminal, trauma-related stimulus processing. Here, we analyzed the directed functional connectivity between the PAG and the PCN, as well as between the PAG and the mPFC to more explicitly characterize the functional connectivity we have observed previously on the corresponding sample and paradigm. We evaluated three models varying with regard to context-dependent modulatory directions (i.e., bi-directional, bottom-up, top-down) among individuals with PTSD (n = 26) and healthy participants (n = 20), where Bayesian model selection was used to identify the most optimal model for each group. We then compared the effective connectivity strength for each parameter across the models and between our groups using Bayesian model averaging. Bi-directional models were found to be favoured across both groups. In PTSD, we revealed the PAG to show stronger excitatory effective connectivity to the PCN, as well as to the mPFC as compared to controls. In PTSD, we further demonstrated that PAG-mediated effective connectivity to the PCN, as well as to the mPFC were modulated more strongly during subliminal, trauma-related stimulus conditions as compared to controls. Clinical disturbances towards self-related processes are reported widely by participants with PTSD during trauma-related stimulus processing, where altered functional connectivity directed by the PAG to the DMN may elucidate experiential links between self- and trauma-related processing in traumatized individuals.

PTSD-related neuroimaging abnormalities in brain function, structure, and biochemistry

Although approximately 90% of the U.S. population will experience a traumatic event within their lifetime, only a fraction of those traumatized individuals will develop posttraumatic stress disorder (PTSD). In fact, approximately 7 out of 100 people in the U.S. will be afflicted by this debilitating condition, which suggests there is substantial inter-individual variability in susceptibility to PTSD. This uncertainty regarding who is susceptible to PTSD necessitates a thorough understanding of the neurobiological processes that underlie PTSD development in order to build effective predictive models for the disorder. In turn, these predictive models may lead to the development of improved diagnostic markers, early intervention techniques, and targeted treatment approaches for PTSD. Prior research has characterized a fear learning and memory network, centered on the prefrontal cortex, hippocampus, and amygdala, that plays a key role in the pathology of PTSD. Importantly, changes in the function, structure, and biochemistry of this network appear to underlie the cognitive-affective dysfunction observed in PTSD. The current review discusses prior research that has demonstrated alterations in brain function, structure, and biochemistry associated with PTSD. Further, the potential for future research to address current gaps in our understanding of the neural processes that underlie the development of PTSD is discussed. Specifically, this review emphasizes the need for multimodal neuroimaging research and investigations into the acute effects of posttraumatic stress. The present review provides a framework to move the field towards a comprehensive neurobiological model of PTSD.

Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning

Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central executive network (CEN), and the salience network (SN) have been shown to be aberrant in patients with posttraumatic stress disorder (PTSD). The purpose of the current study was to a) compare ICN functional connectivity between PTSD, dissociative subtype PTSD (PTSD+DS) and healthy individuals; and b) to examine the use of multivariate machine learning algorithms in classifying PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our neuroimaging dataset consisted of resting-state fMRI scans from 186 participants [PTSD (n = 81); PTSD + DS (n = 49); and healthy controls (n = 56)]. We performed group-level independent component analyses to evaluate functional connectivity differences within each ICN. Multiclass Gaussian Process Classification algorithms within PRoNTo software were then used to predict the diagnosis of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. When comparing the functional connectivity of ICNs between PTSD, PTSD+DS and healthy controls, we found differential patterns of connectivity to brain regions involved in emotion regulation, in addition to limbic structures and areas involved in self-referential processing, interoception, bodily self-consciousness, and depersonalization/derealization. Machine learning algorithms were able to predict with high accuracy the classification of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our results suggest that alterations within intrinsic connectivity networks may underlie unique psychopathology and symptom presentation among PTSD subtypes. Furthermore, the current findings substantiate the use of machine learning algorithms for classifying subtypes of PTSD illness based on ICNs.

Diagnostic and Predictive Neuroimaging Biomarkers for Posttraumatic Stress Disorder

BACKGROUND

Comorbidity between posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) has been commonly overlooked by studies examining resting-state functional connectivity patterns in PTSD. The current study used a data-driven approach to identify resting-state functional connectivity biomarkers to 1) differentiate individuals with PTSD (with or without MDD) from trauma-exposed healthy control subjects (TEHCs), 2) compare individuals with PTSD alone with those with comorbid PTSD+MDD, and 3) explore the clinical utility of the identified biomarkers by testing their associations with clinical symptoms and treatment response.

METHODS

Resting-state magnetic resonance images were obtained from 51 individuals with PTSD alone, 52 individuals with PTSD+MDD, and 76 TEHCs. Of the 103 individuals with PTSD, 55 were enrolled in prolonged exposure treatment. A support vector machine model was used to identify resting-state functional connectivity biomarkers differentiating individuals with PTSD (with or without MDD) from TEHCs and differentiating individuals with PTSD alone from those with PTSD+MDD. The associations between the identified features and symptomatology were tested with Pearson correlations.

RESULTS

The support vector machine model achieved 70.6% accuracy in discriminating between individuals with PTSD and TEHCs and achieved 76.7% accuracy in discriminating between individuals with PTSD alone and those with PTSD+MDD for out-of-sample prediction. Within-network connectivity in the executive control network, prefrontal network, and salience network discriminated individuals with PTSD from TEHCs. The basal ganglia network played an important role in differentiating individuals with PTSD alone from those with PTSD+MDD. PTSD scores were inversely correlated with within-executive control network connectivity (p < .001), and executive control network connectivity was positively correlated with treatment response (p < .001).

CONCLUSIONS

Results suggest that unique brain-based abnormalities differentiate individuals with PTSD from TEHCs, differentiate individuals with PTSD from those with PTSD+MDD, and demonstrate clinical utility in predicting levels of symptomatology and treatment response.

Regulating posttraumatic stress disorder symptoms with neurofeedback: Regaining control of the mind

Neurofeedback is emerging as a psychophysiological treatment where self-regulation is achieved through online feedback of neural states. Novel personalized medicine approaches are particularly important for the treatment of posttraumatic stress disorder (PTSD), as symptom presentation of the disorder, as well as responses to treatment, are highly heterogeneous. Learning to achieve control of specific neural substrates through neurofeedback has been shown to display therapeutic evidence in patients with a wide variety of psychiatric disorders, including PTSD. This article outlines the neural mechanisms underlying neurofeedback and examines converging evidence for the efficacy of neurofeedback as an adjunctive treatment for PTSD via both electroencephalography (EEG) and real-time functional magnetic resonance imaging (fMRI) modalities. Further, implications for the treatment of PTSD via neurofeedback in the military member and Veteran population is examined.

Uncovering the heterogeneity of posttraumatic stress disorder: Towards a personalized medicine approach for military members and Veterans

Introduction: Recently, there has been substantial interest in exploring the heterogeneity of posttraumatic stress disorder (PTSD) on a neurobiological level, as individuals with PTSD, including military members and Veterans, vary in their presentation of symptoms. Methods: Critically, a dissociative subtype of PTSD (PTSD+DS) has been defined, where a large body of evidence suggests that the unique presentation of symptoms among PTSD+DS patients is associated with aberrant neurobiological underpinnings. Results: PTSD+DS is often characterized by emotion overmodulation, with increased top-down activation from emotion regulation areas, which is associated with emotional detachment, depersonalization, and derealization. This is in stark contrast to the symptoms commonly observed in individuals with PTSD, who exhibit emotion undermodulation, which involves decreased top-down regulation of hyperactive emotion generation areas and is associated with vivid re-experiencing of trauma memories and hyperarousal. Discussion: This article examines a clinical case example that clearly illustrates this heterogeneous presentation of PTSD symptomatology and psychopathology. It discusses the implications this evidence base holds for a neurobiologically-informed, personalized medicine approach to treatment for military members and Veterans.

Back to the Basics: Resting State Functional Connectivity of the Reticular Activation System in PTSD and its Dissociative Subtype

BACKGROUND

Brainstem and midbrain neuronal circuits that control innate, reflexive responses and arousal are increasingly recognized as central to the neurobiological framework of post-traumatic stress disorder (PTSD). The reticular activation system represents a fundamental neuronal circuit that plays a critical role not only in generating arousal but also in coordinating innate, reflexive responding. Accordingly, the present investigation aims to characterize the resting state functional connectivity of the reticular activation system in PTSD and its dissociative subtype.

METHODS

We investigated patterns of resting state functional connectivity of a central node of the reticular activation system, namely, the pedunculopontine nuclei, among individuals with PTSD (n = 77), its dissociative subtype (PTSD+DS; n = 48), and healthy controls (n = 51).

RESULTS

Participants with PTSD and PTSD+DS were characterized by within-group pedunculopontine nuclei resting state functional connectivity to brain regions involved in innate threat processing and arousal modulation (i.e., midbrain, amygdala, ventromedial prefrontal cortex). Critically, this pattern was most pronounced in individuals with PTSD+DS, as compared to both control and PTSD groups. As compared to participants with PTSD and controls, individuals with PTSD+DS showed enhanced pedunculopontine nuclei resting state functional connectivity to the amygdala and the parahippocampal gyrus as well as to the anterior cingulate and the ventromedial prefrontal cortex. No group differences emerged between PTSD and control groups. In individuals with PTSD+DS, state derealization/depersonalization was associated with reduced resting state functional connectivity between the left pedunculopontine nuclei and the anterior nucleus of the thalamus. Altered connectivity in these regions may restrict the thalamo-cortical transmission necessary to integrate internal and external signals at a cortical level and underlie, in part, experiences of depersonalization and derealization.

CONCLUSIONS

The present findings extend the current neurobiological model of PTSD and provide emerging evidence for the need to incorporate brainstem structures, including the reticular activation system, into current conceptualizations of PTSD and its dissociative subtype.