Genetic network properties of the human cortex based on regional thickness and surface area measures

Authors

Anna R. Docherty, Virginia Commonwealth UniversityFollow
Chelsea K. Sawyers, Virginia Commonwealth UniversityFollow
Matthew S. Panizzon, University of California - San Diego
Michael C. Neale, Virginia Commonwealth UniversityFollow
Lisa T. Eyler, University of California, VA San Diego Healthcare System
Christine Fennema-Notestine, University of California - San Diego
Carol E. Franz, University of California - San Diego
Chi-Hua Chen, University of California - San Diego
Linda K. McEvoy, University of California - San Diego
Brad Verhulst, Virginia Commonwealth University
Ming T. Tsuang, University of California - San Diego, VA San Diego Healthcare System
William S. Kremen, University of California - San Diego

Document Type

Article

Original Publication Date

2015

Journal/Book/Conference Title

Frontiers in Human Neuroscience

Volume

9

DOI of Original Publication

10.3389/fnhum.2015.00440

Comments

Originally published at http://dx.doi.org/10.3389/fnhum.2015.00440

Date of Submission

April 2016

Abstract

We examined network properties of genetic covariance between average cortical thickness (CT) and surface area (SA) within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques—biometrical genetic modeling, cluster analysis, and graph theory—to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral parcellations. Graph theoretical analysis suggested that small world, but not rich club, network properties may characterize the genetic relationships between these regional size measures. These findings suggest that cortical genetic parcellations exhibit short characteristic path lengths across a broad network of connections. This property may be protective against network failure. In contrast, previous research with structural data has observed strong rich club properties with tightly interconnected hub networks. Future studies of these genetic networks might provide powerful phenotypes for genetic studies of normal and pathological brain development, aging, and function.

Rights

Copyright © 2015 Docherty, Sawyers, Panizzon, Neale, Eyler, Fennema-Notestine, Franz, Chen, McEvoy, Verhulst, Tsuang and Kremen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Is Part Of

VCU Psychiatry Publications