Robust brain parcellation using sparse representation on resting-state fMRI

Zhang, Yu, Caspers, Svenja, Fan, Lingzhong, Fan, Yong, Song, Ming, Liu, Cirong, Mo, Yin, Roski, Christian, Eickhoff, Simon, Amunts, Katrin and Jiang, Tianzi (2014) Robust brain parcellation using sparse representation on resting-state fMRI. Brain Structure and Function, 220 6: 3565-3579. doi:10.1007/s00429-014-0874-x

Author Zhang, Yu
Caspers, Svenja
Fan, Lingzhong
Fan, Yong
Song, Ming
Liu, Cirong
Mo, Yin
Roski, Christian
Eickhoff, Simon
Amunts, Katrin
Jiang, Tianzi
Title Robust brain parcellation using sparse representation on resting-state fMRI
Journal name Brain Structure and Function   Check publisher's open access policy
ISSN 1863-2653
Publication date 2014-08-26
Year available 2014
Sub-type Article (original research)
DOI 10.1007/s00429-014-0874-x
Open Access Status
Volume 220
Issue 6
Start page 3565
End page 3579
Total pages 15
Place of publication Heidelberg Germany
Publisher Springer
Collection year 2015
Language eng
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.
Keyword Resting state
Functional connectivity
Robust brain parcellation
Medial frontal cortex
Parietal operculum
Sparse representation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2015 Collection
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 1 times in Scopus Article | Citations
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