Functional brain networks reconstruction using group sparsity-regularized learning.
Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date...
| Publicado en: | Brain Imaging & Behavior Vol. 12; no. 3; pp. 758 - 771 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2018
|
| 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=129999112&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129999112 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Jun2018 vid: 12 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129999112 129999112 NLM28600738 10.1007/s11682-017-9737-4 NLM28600738 129999112 ppf: 758 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Functional brain networks reconstruction using group sparsity-regularized learning. aug: au: Zhao, Qinghua Li, Will X. Y. Jiang, Xi Lv, Jinglei Lu, Jianfeng Liu, Tianming affil: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China sug: subj: Brain Brain Physiology Brain Mapping Methods Magnetic Resonance Imaging Methods Mental Processes Reproducibility of Results Brain Anatomy and Histology Clinical Assessment Tools Scales ab: Investigating functional brain networks and patterns using sparse representation of fMRI data has received significant interests in the neuroimaging community. It has been reported that sparse representation is effective in reconstructing concurrent and interactive functional brain networks. To date, most of data-driven network reconstruction approaches rarely take consideration of anatomical structures, which are the substrate of brain function. Furthermore, it has been rarely explored whether structured sparse representation with anatomical guidance could facilitate functional networks reconstruction. To address this problem, in this paper, we propose to reconstruct brain networks utilizing the structure guided group sparse regression (S2GSR) in which 116 anatomical regions from the AAL template, as prior knowledge, are employed to guide the network reconstruction when performing sparse representation of whole-brain fMRI data. Specifically, we extract fMRI signals from standard space aligned with the AAL template. Then by learning a global over-complete dictionary, with the learned dictionary as a set of features (regressors), the group structured regression employs anatomical structures as group information to regress whole brain signals. Finally, the decomposition coefficients matrix is mapped back to the brain volume to represent functional brain networks and patterns. We use the publicly available Human Connectome Project (HCP) Q1 dataset as the test bed, and the experimental results indicate that the proposed anatomically guided structure sparse representation is effective in reconstructing concurrent functional brain networks. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|