Format

Send to

Choose Destination
See comment in PubMed Commons below
Neuroimage. 2017 Sep 4. pii: S1053-8119(17)30713-9. doi: 10.1016/j.neuroimage.2017.08.068. [Epub ahead of print]

An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks.

Author information

1
Department of Electrical Engineering, Yale University, New Haven, CT, USA; Yale Institute for Network Science, Yale University, New Haven, CT, USA. Electronic address: mehraveh.salehi@yale.edu.
2
Department of Electrical Engineering, Yale University, New Haven, CT, USA; Yale Institute for Network Science, Yale University, New Haven, CT, USA.
3
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
4
Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.

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 individual 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 for example 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.

KEYWORDS:

Exemplar-based clustering; Functional parcellation; Human connectome project; Individual differences; Predictive modeling; Sex differences; Submodularity

Free full text
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science
    Loading ...
    Support Center