Lea Waller 1, , Susanne Erk 1, Elena Pozzi 2,3, Yara J. Toenders 2,3, Courtney C. Haswell 4, Marc Büttner 1, Paul M. Thompson 5, Lianne Schmaal 2,3, Rajendra A. …
We propose to apply the structural covariance network analyses to obtain centrality measures using subjects from PGC-PTSD neuroimaging dataset.
Using data from the PTSD Psychiatric Genetics Consortium (PGC-PTSD) neuroimaging project, we will extract thalamic volumes from subjects across 29 different sites. We have two groups of subjects – controls (trauma exposed or healthy) and subjects with PTSD. Possible covariates for the data include age, gender, childhood trauma score, alcohol use and comorbid MDD.
We specifically hypothesized that
the structural covariance between regions with cortical thickness reduction in PTSD patients
would be stronger compared with the structural covariance between randomly selected regions
in both PTSD and controls, would be stronger in patients with PTSD than in controls, would be
stronger in PTSD patients with more severe symptoms, and would be modulated by factors
such as age and gender.
the goal of the proposed project will be to identify peripheral
epigenomic predictors of brain aging from cross-sectional data in PTSD. This goal will be pursued
along two aims: 1) identify composite DNA methylation-based markers and/or distinct DNA
methylation sites in either blood or saliva that may be associated with accelerated structural MRIpredicted
brain aging and/or its rate of change; and 2) test whether PTSD/control case status and/or
other parameters (such as sex, lifestyle factors, and blood cell composition) moderate these
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.
This study aims to determine the heritability of the amygdala through investigation of these subregion volumes and to refine the association between the amygdala and PTSD through investigation of case-control differences in the volumes of these subregions and determining the degree of genetic overlap between risk variants affecting these volumes and those that increase risk for PTSD.
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 propose to investigate brain shape/volume patterns (vertex-wise radial distance and log of Jacobian determinant for subcortical structures, and FSL/FreeSurfer vertex-wise cortical thickness, surface area, curvature, sulcal depth for cerebral cortex, as well as ROI-based gross volumes for subcortical structures and ROI-based cortical thickness and surface area) in participants with PTSD, BPD and controls using advanced machine learning/deep learning techniques.