Journal
MEDICAL IMAGE ANALYSIS
Volume 17, Issue 8, Pages 1106-1122Publisher
ELSEVIER
DOI: 10.1016/j.media.2013.07.003
Keywords
R-fMRI; T-fMRI; Structural connectome; Functional connectome; Brain architecture
Categories
Funding
- NIH [K01EB 006878, R01 HL087923-03S2, R01 DA033393]
- NSF CAREER Award [IIS-1149260]
- University of Georgia
- NWPU Foundation for Fundamental Research
- China Government Scholarship
- National Natural Science Foundation of China [30830046]
- National 973 Program of China [2009 CB918303]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1149260] Funding Source: National Science Foundation
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Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively. However, due to the difficulty in strictly controlling the participating subject's mental status and their cognitive behaviors during R-fMRI/T-fMRI scans, it has been challenging to ascertain whether or not an R-fMRI/T-fMRI scan truly reflects the participant's functional brain states during task-free/task-performance periods. This paper presents a novel computational approach to characterizing and differentiating the brain's functional status into task-free or task-performance states, by which the functional brain activities can be effectively understood and differentiated. Briefly, the brain's functional state is represented by a whole-brain quasi-stable connectome pattern (WQCP) of R-fMRI or T-fMRI data based on 358 consistent cortical landmarks across individuals, and then an effective sparse representation method was applied to learn the atomic connectome patterns (ACPs) of both task-free and task-performance states. Experimental results demonstrated that the learned ACPs for R-fMRI and T-fMRI datasets are substantially different, as expected. A certain portion of ACPs from R-fMRI and T-fMRI data were overlapped, suggesting some subjects with overlapping ACPs were not in the expected task-free/task-performance brain states. Besides, potential outliers in the T-fMRI dataset were further investigated via functional activation detections in different groups, and our results revealed unexpected task-performances of some subjects. This work offers novel insights into the functional architectures of the brain. (C) 2013 Elsevier B.V. All rights reserved.
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