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Robust brain parcellation using sparse representation on resting-state fMRI
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; Jiang, Tianzi1,2,4
Source PublicationBRAIN STRUCTURE & FUNCTION
2015-11-01
Volume220Issue:6Pages:3565-3579
SubtypeArticle
AbstractResting-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.
KeywordResting State Functional Connectivity Robust Brain Parcellation Medial Frontal Cortex Parietal Operculum Sparse Representation
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1007/s00429-014-0874-x
WOS KeywordINTRINSIC FUNCTIONAL CONNECTIVITY ; HUMAN PARIETAL OPERCULUM ; CYTOARCHITECTONIC AREAS ; INDIVIDUAL VARIABILITY ; VESTIBULAR CORTEX ; FRONTAL-CORTEX ; MOTOR AREAS ; SMA/PRE-SMA ; REGIONS ; MRI
Indexed BySCI
Language英语
WOS Research AreaAnatomy & Morphology ; Neurosciences & Neurology
WOS SubjectAnatomy & Morphology ; Neurosciences
WOS IDWOS:000361566000030
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9017
Collection脑网络组研究中心
Affiliation1.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
Recommended Citation
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.
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