title: Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients.
abstract: Functional connectomes (FCs) have been recently shown to be powerful in characterizing brain conditions. However, many previous studies assumed temporal stationarity of FCs, while their temporal dynamics are rarely explored. Here, based on the structural connectomes constructed from diffusion tensor imaging data, FCs are derived from resting-state fMRI (R-fMRI) data and are then temporally divided into quasi-stable segments via a sliding time window approach. After integrating and pooling over a large number of those temporally quasi-stable FC segments from 44 post-traumatic stress disorder (PTSD) patients and 51 healthy controls, common FC (CFC) patterns are derived via effective dictionary learning and sparse coding algorithms. It is found that there are 16 CFC patterns that are reproducible across healthy controls, and interestingly, two additional CFC patterns with altered connectivity patterns [termed signature FC (SFC) here] exist dominantly in PTSD subjects. These two SFC patterns alone can successfully differentiate 80% of PTSD subjects from healthy controls with only 2% false positive. Furthermore, the temporal transition dynamics of CFC patterns in PTSD subjects are substantially different from those in healthy controls. These results have been replicated in separate testing datasets, suggesting that dynamic functional connectomics signatures can effectively characterize and differentiate PTSD patients.
authors.affiliation: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia.
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.forename: Xi
authors.initials: X
authors.lastname: Jiang
authors.forename: Changfeng
authors.initials: C
authors.lastname: Jin
authors.forename: Xin
authors.initials: X
authors.lastname: Zhang
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Hu
authors.forename: Lingjiang
authors.initials: L
authors.lastname: Li
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000503:physiopathology*; D063132:Connectome / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009434:Neural Pathways / Q000503:physiopathology; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D013313:Stress Disorders, Post-Traumatic / Q000175:diagnosis* / Q000503:physiopathology*
keywords: connectivity; diffusion tensor imaging; resting state fMRI