Paper Info
Title
Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.
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.
Year
DOI
Venue
2013
10.1016/j.neuroimage.2013.05.081
NeuroImage
Keywords
Field
DocType
Functional MRI,Resting-state connectivity,Network analysis,Graph-theory-based parcellation,Whole-brain atlas
Brain mapping,Computer vision,Pattern recognition,Computer science,Resting state fMRI,Cognitive psychology,Node (networking),Cytoarchitecture,Artificial intelligence,Network analysis
Journal
Volume
ISSN
Citations 
82
1053-8119
88
PageRank 
References 
Authors
2.61
21
4
Name
Order
Citations
PageRank
Xilin Shen127814.18
F Tokoglu2882.61
X Papademetris31063.95
R Todd Constable484877.34