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Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients
Author(s) -
Li Xiang,
Zhu Dajiang,
Jiang Xi,
Jin Changfeng,
Zhang Xin,
Guo Lei,
Zhang Jing,
Hu Xiaoping,
Li Lingjiang,
Liu Tianming
Publication year - 2014
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22290
Subject(s) - connectomics , dynamic functional connectivity , neuroscience , connectome , diffusion mri , resting state fmri , psychology , tractography , functional connectivity , nerve net , medicine , magnetic resonance imaging , radiology
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. Hum Brain Mapp 35:1761–1778, 2014 . © 2013 Wiley Periodicals, Inc.

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