Dynamic Functional Connectomics Signatures for Characterization and Differentiation of PTSD Patients

• Type of Material: Article
• Authors: Xiang Li, University of Georgia; Dajiang Zhu, University of Georgia; Xi Jiang, University of Georgia; Changfeng Jin, Central South University; Xin Zhang, University of Georgia; Lei Guo, Northwestern Polytechnical University; Jing Zhang, Yale University; Xiaoping Hu, Emory University; Jingjiang Li, Central South University; Tianming Liu, University of Georgia
• Language: English
• Date: 2014-04-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2013 Wiley Periodicals, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/hbm.22290
• Title of Journal or Parent Work: Human Brain Mapping
• ISSN: 1065-9471
• Volume: 35
• Issue: 4
• Start Page: 1761
• End Page: 1778
• Grant/Funding Information: X Hu was supported by the Georgia Research Alliance and NIH R01 DA033393.; J Zhang was supported by start-up funding and Sesseel Award from Yale University.; T Liu was supported by the NIH K01 EB 006878; NIH R01 HL087923-03S2; NIH R01 DA033393; NSF CAREER Award IIS-1149260; and The University of Georgia start-up research funding.; Lingjiang Li was supported by The National Natural Science Foundation of China (30830046); and The National 973 Program of China (2009 CB918303).; L Guo was supported by the NWPU Foundation for Fundamental Research.
• Keywords: Neuroimaging; diffusion tensor imaging; BRAIN; Life Sciences & Biomedicine; ALZHEIMERS-DISEASE; NETWORKS; DISCONNECTIVITY; connectivity; POSTTRAUMATIC-STRESS-DISORDER; Neurosciences & Neurology; Neurosciences; LOCALIZATION; DEFAULT MODE; CONNECTIVITY; MICROSTATE; Science & Technology; resting state fMRI; Radiology, Nuclear Medicine & Medical Imaging; FLUCTUATIONS
• Research Categories: Health Sciences, Radiology; Engineering, Biomedical

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.