Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations.
A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representa...
| Publicado en: | Brain Imaging & Behavior Vol. 10; no. 1; pp. 21 - 33 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113546416&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113546416 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Mar2016 vid: 10 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 113546416 113546416 NLM25732072 113546416 10.1007/s11682-015-9359-7 NLM25732072 PMC4559495 [Available on 03/01/17] 113546416 ppf: 21 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations. aug: au: Zhang, Shu Li, Xiang Lv, Jinglei Jiang, Xi Guo, Lei Liu, Tianming affil: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens USA sug: subj: Relaxation Brain Physiology Neuropsychological Tests Magnetic Resonance Imaging Methods Language Motor Activity Physiology Memory, Short Term Brain Mapping Methods Emotions Human Gambling Physiopathology Social Behavior Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. Specifically, in the first stage, the whole-brain tfMRI or rsfMRI signals of each subject were composed into a big data matrix, which was then factorized into a subject-specific dictionary matrix and a weight coefficient matrix for sparse representation. In the second stage, all of the dictionary matrices from both tfMRI/rsfMRI data across multiple subjects were composed into another big data-matrix, which was further sparsely represented by a cross-subjects common dictionary and a weight matrix. This framework has been applied on the recently publicly released Human Connectome Project (HCP) fMRI data and experimental results revealed that there are distinctive and descriptive atoms in the cross-subjects common dictionary that can effectively characterize and differentiate tfMRI and rsfMRI signals, achieving 100% classification accuracy. Moreover, our methods and results can be meaningfully interpreted, e.g., the well-known default mode network (DMN) activities can be recovered from the very noisy and heterogeneous aggregated big-data of tfMRI and rsfMRI signals across all subjects in HCP Q1 release. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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