期刊
MEDICAL IMAGE ANALYSIS
卷 20, 期 1, 页码 112-134出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2014.10.011
关键词
Task-based fMRI; Activation; Intrinsic networks; Connectivity
类别
资金
- NSF CAREER Award [IIS-1149260]
- NIH [R01 DA-033393, R01 AG-042599]
- NSF [CBET-1302089, BCS-143905]
- Doctorate Foundation of Northwestern Polytechnical University
- NSFC [61273362]
- Yale University
- National Science Foundation of China [61273362, 61005018, 91120005, 61103061]
- Program for New Century Excellent Talents in University [NCET-10-0079]
- China Postdoctoral Science Foundation [20110490174, 2012T50819]
- [NPU-FFR-JC20120237]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1149260] Funding Source: National Science Foundation
- Direct For Social, Behav & Economic Scie
- Division Of Behavioral and Cognitive Sci [1439051] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1263524] Funding Source: National Science Foundation
There have been several recent studies that used sparse representation for fMRI signal analysis and activation detection based on the assumption that each voxel's fMRI signal is linearly composed of sparse components. Previous studies have employed sparse coding to model functional networks in various modalities and scales. These prior contributions inspired the exploration of whether/how sparse representation can be used to identify functional networks in a voxel-wise way and on the whole brain scale. This paper presents a novel, alternative methodology of identifying multiple functional networks via sparse representation of whole-brain task-based fMRI signals. Our basic idea is that all fMRI signals within the whole brain of one subject are aggregated into a big data matrix, which is then factorized into an over-complete dictionary basis matrix and a reference weight matrix via an effective online dictionary learning algorithm. Our extensive experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge. Importantly, these well-characterized functional network components are quite reproducible in different brains. In general, our methods offer a novel, effective and unified solution to multiple fMRI data analysis tasks including activation detection, de-activation detection, and functional network identification. (C) 2014 Elsevier B.V. All rights reserved.
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