Format

Send to

Choose Destination
Front Hum Neurosci. 2015 Aug 20;9:440. doi: 10.3389/fnhum.2015.00440. eCollection 2015.

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

Author information

1
Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University Richmond, VA, USA.
2
Department of Psychiatry, University of California, San Diego San Diego, CA, USA ; Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego San Diego, CA, USA.
3
Department of Psychiatry, University of California, San Diego San Diego, CA, USA ; Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System San Diego, CA, USA.
4
Department of Psychiatry, University of California, San Diego San Diego, CA, USA ; Department of Radiology, University of California, San Diego San Diego, CA, USA.
5
Department of Radiology, University of California, San Diego San Diego, CA, USA.
6
Department of Psychiatry, University of California, San Diego San Diego, CA, USA ; Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego San Diego, CA, USA ; Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System San Diego, CA, USA ; Department of Neurosciences, University of California, San Diego San Diego, CA, USA.
7
Department of Psychiatry, University of California, San Diego San Diego, CA, USA ; Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego San Diego, CA, USA ; Department of Neurosciences, University of California, San Diego San Diego, CA, USA.

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.

KEYWORDS:

cortical thickness; genetic; imaging; network; small world; structural; surface area; twin

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central
Loading ...
Support Center