title: Robust brain parcellation using sparse representation on resting-state fMRI.
abstract: 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.
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, 52425, Juelich, Germany.
authors.forename: Svenja
authors.initials: S
authors.lastname: Caspers
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Lingzhong
authors.initials: L
authors.lastname: Fan
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Fan
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Song
authors.affiliation: Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4072, Australia.
authors.forename: Cirong
authors.initials: C
authors.lastname: Liu
authors.affiliation: The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, People's Republic of China.
authors.forename: Yin
authors.initials: Y
authors.lastname: Mo
authors.affiliation: Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, 52425, Juelich, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Roski
authors.affiliation: Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, 52425, Juelich, Germany.|Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine-University Düsseldorf, 40225, Düsseldorf, Germany.
authors.forename: Simon
authors.initials: S
authors.lastname: Eickhoff
authors.affiliation: Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, 52425, Juelich, Germany.|C. and O. Vogt Institute for Brain Research, Heinrich-Heine-University Düsseldorf, 40225, Düsseldorf, Germany.
authors.forename: Katrin
authors.initials: K
authors.lastname: Amunts
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. jiangtz@nlpr.ia.ac.cn.|National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. jiangtz@nlpr.ia.ac.cn.|Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4072, Australia. jiangtz@nlpr.ia.ac.cn.
authors.forename: Tianzi
authors.initials: T
authors.lastname: Jiang
mesh_terms: D000328:Adult; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: Functional connectivity; Medial frontal cortex; Parietal operculum; Resting state; Robust brain parcellation; Sparse representation