Dynamic brain connectivity is a better predictor of PTSD than static connectivity

• Published in: Human brain mapping Vol. 38; no. 9; pp. 4479 - 4496
• Main Authors: Jin, Changfeng, Jia, Hao, Lanka, Pradyumna, Rangaprakash, D, Li, Lingjiang, Liu, Tianming, Hu, Xiaoping, Deshpande, Gopikrishna
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.09.2017; John Wiley and Sons Inc
• Subjects: Brain; Brain - diagnostic imaging; Brain - physiopathology; Brain mapping; Brain Mapping - methods; Classification; Diagnosis, Computer-Assisted - methods; Diagnostic systems; Disasters; dynamic brain connectivity; Earthquakes; effective connectivity; functional connectivity; Functional magnetic resonance imaging; Humans; Hypotheses; Learning algorithms; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical models; Mental disorders; Neural networks; Neural Pathways - diagnostic imaging; Neural Pathways - physiopathology; Neuroimaging; Pathology; Post traumatic stress disorder; Rest; resting‐state functional magnetic resonance imaging; Statistical analysis; Stress Disorders, Post-Traumatic - classification; Stress Disorders, Post-Traumatic - diagnostic imaging; Stress Disorders, Post-Traumatic - etiology; Stress Disorders, Post-Traumatic - physiopathology; Support Vector Machine; Variability; resting-state functional magnetic resonance imaging; dynamic brain connectivity; post-traumatic stress disorder; functional connectivity; support vector machine; effective connectivity
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.23676

Using resting‐state functional magnetic resonance imaging, we test the hypothesis that subjects with post‐traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero‐lag correlation) and static effective connectivity (EC; directional time‐lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479–4496, 2017. © 2017 Wiley Periodicals, Inc.