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The Structural, Connectomic and Network Covariance of the Human Brain

Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA, USA.
Elsevier Science
Publication Date:
Volume Number:
Neuroimage. 2013 Feb 1;66:489-99.
PubMed ID:
Neuroimaging, MRI, DTI, Connectivity, Correlation
Appears in Collections:
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Irimia A., Van Horn J.D. The Structural, Connectomic and Network Covariance of the Human Brain. Neuroimage. 2013 Feb 1;66:489-99. PMID: 23116816. PMCID: PMC3586751.
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Though it is widely appreciated that complex structural, functional and morphological relationships exist between distinct areas of the human cerebral cortex, the extent to which such relationships coincide remains insufficiently appreciated. Here we determine the extent to which correlations between brain regions are modulated by either structural, connectomic or network-theoretic properties using a structural neuroimaging data set of magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) volumes acquired from N=110 healthy human adults. To identify the linear relationships between all available pairs of regions, we use canonical correlation analysis to test whether a statistically significant correlation exists between each pair of cortical parcels as quantified via structural, connectomic or network-theoretic measures. In addition to this, we investigate (1) how each group of canonical variables (whether structural, connectomic or network-theoretic) contributes to the overall correlation and, additionally, (2) whether each individual variable makes a significant contribution to the test of the omnibus null hypothesis according to which no correlation between regions exists across subjects. We find that, although region-to-region correlations are extensively modulated by structural and connectomic measures, there are appreciable differences in how these two groups of measures drive inter-regional correlation patterns. Additionally, our results indicate that the network-theoretic properties of the cortex are strong modulators of region-to-region covariance. Our findings are useful for understanding the structural and connectomic relationship between various parts of the brain, and can inform theoretical and computational models of cortical information processing.

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