Individualized Prediction of PTSD Symptom Severity in Trauma Survivors From Whole-Brain Resting-State Functional Connectivity - Suo, Xueling; Lei, Du; Li, Wenbin; Yang, Jing; Li, Lingjiang; Sweeney, John A.; Gong, Qiyong (Frontiers in Behavioral Neuroscience, 2020)
Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting state functional magnetic resonance imaging (fMRI) and symptom severity in post-trauma stress disorder. However, few studies have used brain-based measures to identify imaging associations with illness severity at the level of individual patients. This study applied connectome-based predictive modeling (CPM), a recently developed data-driven and subject-level method, to identify brain function features that are related to symptom severity of trauma survivors. Resting state fMRI scans and clinical ratings were obtained 10 to 15 months after the earthquake from 122 earthquake survivors. Symptom severity of post-trauma stress disorder features for each survivor was evaluated using Clinician Administered Post-trauma stress disorder Scale (CAPS-IV). A functionally pre-defined atlas was applied to divide the human brain into 268 regions. Each individual’s functional connectivity 268×268 matrix was created to reflect correlations of functional time series data across each pair of nodes. The relationship between CAPS-IV scores and brain functional connectivity was explored in a CPM linear model. Using a leave-one-out cross validation procedure, findings showed that the positive network model predicted the left-out individual’s CAPS-IV scores from resting state functional connectivity. CPM predicted CAPS-IV scores, as indicated by a significant correspondence between predicted and actual values (r = 0.30, P = 0.001) utilizing primarily functional connectivity between visual cortex, subcortical-cerebellum, limbic and motor systems. The current study provides data-driven evidence regarding the functional brain features that predict symptom severity based on the organization of intrinsic brain networks, and highlights its potential application in making clinical evaluation of symptom severity at the individual level.