Classification of posttraumatic stress disorder and related outcomes in women veterans using magnetoencephalography

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.

The brain landscape of the two-hit model of posttraumatic stress disorder

The neurophysiological mechanisms underlying the development of posttraumatic stress disorder (PTSD) are poorly understood. Here we test a proposal that PTSD symptoms reflect fixed, highly correlated neural networks resulting from massive engagement of sensory inputs and the sequential involvement of those projections to limbic areas. Three-tesla functional magnetic resonance imaging (fMRI) data were acquired at rest in 15 veterans diagnosed with PTSD and 21 healthy control veterans from which zero-lag cross correlations between 50 brain areas (N = 1,225 pairs) were computed and analyzed. The brain areas were assigned to tiers based on the neurocircuitry of successively converging sensory pathways proposed by Jones and Powell (Jones EG, Powell TP. Brain 93: 793-820, 1970). The primary analyses assessed normalized proportional differences in cross correlation strength within and across tiers in veterans with PTSD and control veterans. Compared with control veterans, cross correlation strength was higher in veterans with PTSD, within and across tiers of areas involved in processing sensory inputs, and systematically increased from sensory processing areas to limbic areas. The functional relevance of this hypercorrelation was further documented by the finding that the severity of self-reported PTSD symptomatology was positively associated with higher neural correlations.NEW & NOTEWORTHY The neurophysiological mechanisms underlying the development of PTSD are poorly understood. Here we document that massive engagement of sensory modalities during trauma exposure leads to fixed, hypercorrelated frontal, parietal, temporal, and limbic networks, reflecting the successive integration of salient sensory inputs along the framework of Jones and Powell.

MEG neural signature of sexual trauma in women veterans with PTSD

Previous research has documented the utility of synchronous neural interactions (SNI) in classifying women veterans with and without posttraumatic stress disorder (PTSD) and other trauma-related outcomes based on functional connectivity using magnetoencephalography (MEG). Here, we extend that line of research to evaluate trauma-specific PTSD neural signatures with MEG in women veterans. Participants completed diagnostic interviews and underwent a task-free MEG scan from which SNI was computed. Thirty-five women veterans were diagnosed with PTSD due to sexual trauma and sixteen with PTSD due to non-sexual trauma. Strength of SNI was compared in women with and without sexual trauma, and linear discriminant analysis was used to classify the brain patterns of women with PTSD due to sexual trauma and non-sexual trauma. Comparison of SNI strength between the two groups revealed widespread hypercorrelation in women with sexual trauma relative to those without sexual trauma. Furthermore, using SNI, the brains of participants were classified as sexual trauma or non-sexual trauma with 100% accuracy. These findings bolster evidence supporting the utility of task-free SNI and suggest that neural signatures of PTSD are trauma-specific.