Title
An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks.
Abstract
Recent work with functional connectivity data has led to significant progress in understanding the functional organization of the brain. While the majority of the literature has focused on group-level parcellation approaches, there is ample evidence that the brain varies in both structure and function across individuals. In this work, we introduce a parcellation technique that incorporates delineation of functional networks both at the individual- and group-level. The proposed technique deploys the notion of “submodularity” to jointly parcellate the cerebral cortex while establishing an inclusive correspondence between the individualized functional networks. Using this parcellation technique, we successfully established a cross-validated predictive model that predicts individuals' sex, solely based on the parcellation schemes (i.e. the node-to-network assignment vectors). The sex prediction finding illustrates that individualized parcellation of functional networks can reveal subgroups in a population and suggests that the use of a global network parcellation may overlook fundamental differences in network organization. This is a particularly important point to consider in studies comparing patients versus controls or even patient subgroups. Network organization may differ between individuals and global configurations should not be assumed. This approach to the individualized study of functional organization in the brain has many implications for both neuroscience and clinical applications.
Year
DOI
Venue
2018
10.1016/j.neuroimage.2017.08.068
NeuroImage
Keywords
Field
DocType
Individual differences,Exemplar-based clustering,Submodularity,Functional parcellation,Sex differences,Predictive modeling,Human connectome project
Brain mapping,Population,Developmental psychology,Global network,Human Connectome Project,Structure and function,Psychology,Cognitive psychology,Functional networks
Journal
Volume
ISSN
Citations 
170
1053-8119
4
PageRank 
References 
Authors
0.42
22
5
Name
Order
Citations
PageRank
Mehraveh Salehi140.42
Amin Karbasi245545.00
Xilin Shen327814.18
Dustin Scheinost428722.17
R Todd Constable584877.34