NDLI: Connectivity-based parcellation of putamen using resting state fMRI data

Content Provider	IEEE Xplore Digital Library
Author	Yiming Zhang Aiping Liu Sun Nee Tan McKeown, M.J. Wang, Z.J.
Copyright Year	2015
Description	Author affiliation: Dept. of Neurology & Pacific Parkinsons Res. Centre, Univ. of British Columbia, Vancouver, BC, Canada (Sun Nee Tan; McKeown, M.J.) \|\| Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC, Canada (Yiming Zhang; Aiping Liu; Wang, Z.J.)
Abstract	In this paper, we present a novel framework for parcellation of a brain region into functional sub-regions based on connectivity patterns between brain regions. The proposed method takes the prior neurological information into consideration and aims at finding spatially continuous and functionally consistent sub-regions in a given brain area. The proposed framework relies on 1) a sparse spatially regularized fused lasso regression model for encouraging spatially and functionally adjacent voxels to share similar regression coefficients despite of spatial noise; 2) an iterative voxels (groups) merging and adaptive parameter tuning process; and 3) a Graph-Cut optimization algorithm for assigning overlapped voxels into separate sub-regions. With spatial information incorporated, spatially continuous and functionally consistent sub-regions could be obtained and further used for subsequent brain connectivity analysis.
Starting Page	34
Ending Page	37
File Size	511407
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7163810
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Clustering algorithms Correlation Brain modeling Optimization Algorithm design and analysis spatial regularization fMRI connectivity brain parcellation
Content Type	Text
Resource Type	Article

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