@article{ref1,
title="Individualized prediction of PTSD symptom severity in trauma survivors from whole-brain resting-state functional connectivity",
journal="Frontiers in behavioral neuroscience",
year="2020",
author="Gong, Qiyong and Sweeney, John A. and Li, Lingjiang and Yang, Jing and Li, Wenbin and Lei, Du and Suo, Xueling",
volume="14",
number="",
pages="e563152-e563152",
abstract="Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting-state functional magnetic resonance imaging (fMRI) and symptom  severity in post-traumatic 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 10-15 months  after the earthquake from 122 earthquake survivors. Symptom severity of  post-traumatic stress disorder features for each survivor was evaluated using the  Clinician Administered Post-traumatic 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 (LOOCV) 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.<p /> <p>Language: en</p>",
language="en",
issn="1662-5153",
doi="10.3389/fnbeh.2020.563152",
url="http://dx.doi.org/10.3389/fnbeh.2020.563152"
}