Mapping PTSD symptoms to brain networks: a machine learning study

Abstract

Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N = 50) of PTSD patients and characterized clinical features using the PTSD Checklist for DSM-5 (PCL-5). We compared connectivity among 100 cortical and subcortical regions within the default mode, salience, executive, and affective networks. We then used principal component analysis and least-angle regression (LARS) to identify relationships between symptom domain severity and brain networks. We found connectivity predicted PTSD symptom profiles. The goodness of fit (R2) for total PCL-5 score was 0.29 and the R2 for intrusion, avoidance, cognition/mood, and arousal/reactivity symptoms was 0.33, 0.23, −0.01, and 0.06, respectively. The model performed significantly better than chance in predicting total PCL-5 score (p = 0.030) as well as intrusion and avoidance scores (p = 0.002 and p = 0.034). It was not able to predict cognition and arousal scores (p = 0.412 and p = 0.164). While this work requires replication, these findings demonstrate that this computational approach can directly link PTSD symptom domains with neural network connectivity patterns. This line of research provides an important step toward data-driven diagnostic assessments in PTSD, and the use of computational methods to identify individual patterns of network pathology that can be leveraged toward individualized treatment.