Journal
CEREBRAL CORTEX
Volume 22, Issue 7, Pages 1530-1541Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhr221
Keywords
graph theory; gray matter; individual networks; magnetic resonance imaging; morphometry
Categories
Funding
- UK Engineering and Physical Sciences Research Council
- Medical Research Council through the Doctoral Training Centre in NeuroInformatics at the School of Informatics at the University of Edinburgh
- Chief Scientist Office (Scotland) Project Grant [CZB/4/427]
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The characterization of gray matter morphology of individual brains is an important issue in neuroscience. Graph theory has been used to describe cortical morphology, with networks based on covariation of gray matter volume or thickness between cortical areas across people. Here, we extend this research by proposing a new method that describes the gray matter morphology of an individual cortex as a network. In these large-scale morphological networks, nodes represent small cortical regions, and edges connect regions that have a statistically similar structure. The method was applied to a healthy sample (n = 14, scanned at 2 different time points). For all networks, we described the spatial degree distribution, average minimum path length, average clustering coefficient, small world property, and betweenness centrality (BC). Finally, we studied the reproducibility of all these properties. The networks showed more clustering than random networks and a similar minimum path length, indicating that they were small world. The spatial degree and BC distributions corresponded closely to those from group-derived networks. All network property values were reproducible over the 2 time points examined. Our results demonstrate that intracortical similarities can be used to provide a robust statistical description of individual gray matter morphology.
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