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Groupwise whole-brain parcellation from resting-state fMRI data for network node identification
Author(s):
X. Shen
,
F. Tokoglu
,
X. Papademetris
,
R.T. Constable
Publication date
Created:
November 2013
Publication date
(Print):
November 2013
Journal:
NeuroImage
Publisher:
Elsevier BV
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Abstract
In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages. Copyright © 2013 Elsevier Inc. All rights reserved.
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Journal
Title:
NeuroImage
Abbreviated Title:
NeuroImage
Publisher:
Elsevier BV
ISSN (Print):
10538119
Publication date Created:
November 2013
Publication date (Print):
November 2013
Volume
: 82
Pages
: 403-415
Article
DOI:
10.1016/j.neuroimage.2013.05.081
SO-VID:
25060b0d-7c8b-42b0-8ddb-24c686d10726
Copyright ©
© 2013
License:
https://www.elsevier.com/tdm/userlicense/1.0/
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