The goal of this study is to advance PTSD research by using novel multimodal magnetic resonance imaging (MRI) features including structural MRI, resting state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI), with machine learning methods, in classifying patients with PTSD from trauma-exposed healthy controls (TEHC) individuals using large-scale dataset from ENIGMA PTSD Working Group, and to examine its utility in predicting diagnosis, functional impairment, and symptom severity. This study will lay the groundwork to further utilize multimodal fMRI and machine-learning algorithms to quantify both functional and structural large-scale networks in PTSD, and offer new insights into the identification of potential biomarkers for the clinical diagnosis of PTSD.
Rs-fMRI will be preprocessed according to the ENIGMA rs-fMRI pipeline  and parcellated using a high-resolution parcellation (such as the Power parcellation  adjusted to include subcortical regions such as amygdala, ACC, etc.). Functional connectivity (FC) will be calculated using pairwise Pearson linear correlations between the individual ROIs and Fisher’s r-to-z transformations will be applied. Nuisance regression with sites, age, etc. will be performed and the cleaned FC measures will be entered into a sparse-CCA analysis [8-10] with individual CAPS scores as clinical measures. Only patients with available CAPS scores will be included in this part of the analysis.
We will compare patients with PTSD (N~=1500) to trauma-exposed controls (N~=1500) from ENIGMA-PGC PTSD project on dynamic rsFC based on the standard deviations in rsFC of amygdala, hippocampus and mPFC over a series of sliding windows. The proposed research may advance knowledges of fluctuating communications among brain systems in PTSD.
a large PTSD study investigating within and between network resting state functional connectivity is needed to better understand the role of these networks in the disorder, which may eventually contribute to the development new interventions or treatments for PTSD. Here we propose to investigate resting state functional connectivity in a large sample of PTSD patients (~1500) and controls (~1500) recruited through ENIGMA-PGC PTSD. A standardized seed-based and dual regression approach will be used, which allows future cross-disorder analyses
In the present study, we aim to compare results from three different harmonization approaches (1) LME, (2) ComBat, and (3) ComBat-GAM. We propose comparing methods using cortical thickness data from 29 ENIGMA-PTSD sites as a test case to investigate age-related trajectories of cortical thickness in participants with PTSD.
the bulk of imaging research to-date has focused on these regions
with respect to major symptoms of the disorder (i.e. amygdala and hyperarousal; hippocampus and
memory deficits; frontal cortices and impaired extinction learning) (Shin & Liberzon; Rauch, Shin, &
Phelps, 2006). This approach, however, ignores the contribution of other, larger network disturbances
that may also be important for the pathophysiology of PTSD (Negreira & Abdallah, 2019).
We propose to utilize the resting-state fMRI data of the ENIGMA-PGC PTSD consortium to characterize topological organization of functional network properties in adult women with IPV-related PTSD. To achieve this goal, we propose to use for the first time novel algorithms (e.g., PACO and Asymptotic Surprise) to define patterns of intrinsic network organization in a large PTSD sample and thereby define a contemporary and more neurophysiologically plausible understanding of the impact of PTSD on the organization of functional networks.
In this study we propose to adopt a normative modeling/conformal prediction approach for examining
neurobiological heterogeneity within PTSD patients. We plan to do this by using statistical tolerance
intervals (TIs) calculated on the control population to extract informative features from rsfMRI data and
detect patterns of abnormality in patients.
We propose to use the ENIGMA-PTSD sample to conduct the largest confirmatory “mega-analysis” to test a priori hypotheses about the role of altered SN and DMN connectivity in PTSD, and to also conduct exploratory analyses using unbiased whole-brain connectomic methodologies to identify potential contributions of additional networks, and elucidate the effects of PTSD on global neural network architecture.
Determining the pathophysiology of posttraumatic stress disorder (PTSD) is a critical step toward reducing its debilitating impact. A key step in that process is identifying regions in the brain where changes in resting-state activity may be associated with PTSD or PTSD symptom severity. Identification of such regions of interest (ROIs) would not only shed light on the neural processes underlying PTSD, but could also serve as markers of relevant networks, seeds for functional connectivity analysis, or targets for clinical intervention (e.g. neuromodulation).