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Robust brain parcellation using sparse representation on resting-state fMRI

文献类型：期刊论文

作者Zhang, Yu1,2; Caspers, Svenja3; Fan, Lingzhong1; Fan, Yong1,2; Song, Ming1,2; Liu, Cirong4; Mo, Yin5; Roski, Christian3; Eickhoff, Simon3,6; Amunts, Katrin3,7
刊名BRAIN STRUCTURE & FUNCTION
出版日期2015-11-01
卷号220期号:6页码:3565-3579
关键词Resting state Functional connectivity Robust brain parcellation Medial frontal cortex Parietal operculum Sparse representation
英文摘要Resting-state fMRI (rs-fMRI) has been widely used to segregate the brain into individual modules based on the presence of distinct connectivity patterns. Many parcellation methods have been proposed for brain parcellation using rs-fMRI, but their results have been somewhat inconsistent, potentially due to various types of noise. In this study, we provide a robust parcellation method for rs-fMRI-based brain parcellation, which constructs a sparse similarity graph based on the sparse representation coefficients of each seed voxel and then uses spectral clustering to identify distinct modules. Both the local time-varying BOLD signals and whole-brain connectivity patterns may be used as features and yield similar parcellation results. The robustness of our method was tested on both simulated and real rs-fMRI datasets. In particular, on simulated rs-fMRI data, sparse representation achieved good performance across different noise levels, including high accuracy of parcellation and high robustness to noise. On real rs-fMRI data, stable parcellation of the medial frontal cortex (MFC) and parietal operculum (OP) were achieved on three different datasets, with high reproducibility within each dataset and high consistency across these results. Besides, the parcellation of MFC was little influenced by the degrees of spatial smoothing. Furthermore, the consistent parcellation of OP was also well corresponding to cytoarchitectonic subdivisions and known somatotopic organizations. Our results demonstrate a new promising approach to robust brain parcellation using resting-state fMRI by sparse representation.
WOS标题词Science & Technology ; Life Sciences & Biomedicine
类目[WOS]Anatomy & Morphology ; Neurosciences
研究领域[WOS]Anatomy & Morphology ; Neurosciences & Neurology
关键词[WOS]INTRINSIC FUNCTIONAL CONNECTIVITY ; HUMAN PARIETAL OPERCULUM ; CYTOARCHITECTONIC AREAS ; INDIVIDUAL VARIABILITY ; VESTIBULAR CORTEX ; FRONTAL-CORTEX ; MOTOR AREAS ; SMA/PRE-SMA ; REGIONS ; MRI
收录类别SCI
语种英语
WOS记录号WOS:000361566000030
公开日期2015-12-24
源URL[http://ir.ia.ac.cn/handle/173211/9017]  
专题自动化研究所_脑网络组研究中心
作者单位1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Res Ctr Juelich, Inst Neurosci & Med INM 1, D-52425 Julich, Germany
4.Univ Queensland, Queensland Brain Inst, St Lucia, Qld 4072, Australia
5.Kunming Med Univ, Affiliated Hosp 1, Kunming 650032, Peoples R China
6.Univ Dusseldorf, Inst Clin Neurosci & Med Psychol, D-40225 Dusseldorf, Germany
7.Univ Dusseldorf, C & O Vogt Inst Brain Res, D-40225 Dusseldorf, Germany
推荐引用方式
GB/T 7714		 Zhang, Yu,Caspers, Svenja,Fan, Lingzhong,et al. Robust brain parcellation using sparse representation on resting-state fMRI[J]. BRAIN STRUCTURE & FUNCTION,2015,220(6):3565-3579.
APA Zhang, Yu.,Caspers, Svenja.,Fan, Lingzhong.,Fan, Yong.,Song, Ming.,...&Jiang, Tianzi.(2015).Robust brain parcellation using sparse representation on resting-state fMRI.BRAIN STRUCTURE & FUNCTION,220(6),3565-3579.
MLA Zhang, Yu,et al."Robust brain parcellation using sparse representation on resting-state fMRI".BRAIN STRUCTURE & FUNCTION 220.6(2015):3565-3579.

入库方式： OAI收割

来源：自动化研究所

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