The structural, connectomic and network covariance of the human brain

• Published in: NeuroImage (Orlando, Fla.) Vol. 66; pp. 489 - 499
• Main Authors: Irimia, Andrei, Van Horn, John D.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.02.2013; Elsevier; Elsevier Limited
• Subjects: Adult; Age; Automation; Biological and medical sciences; Brain - anatomy & histology; Brain - physiology; Brain research; Computational neuroscience; Connectivity; Correlation; Diffusion Tensor Imaging; DTI; Female; Fundamental and applied biological sciences. Psychology; Health Insurance Portability & Accountability Act 1996-US; Human subjects; Humans; Hypotheses; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Medical imaging; Models, Neurological; MRI; Nerve Net - anatomy & histology; Nerve Net - physiology; Neuroimaging; NMR; Nuclear magnetic resonance; Software; Studies; Vertebrates: nervous system and sense organs; LOD; Par; Neuroimaging; Fro; Correlation; MRI; GM; IDA; Occ; Ins; Lim; WM; HIPAA; DTI; Connectivity; FA; LONI; Tem; Human; Central nervous system; Nuclear magnetic resonance imaging; Encephalon
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.10.066

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. ► Structure and connectivity modulate inter-regional brain correlations differently. ► Network-theoretic cortex properties strongly modulate region-to-region covariance. ► Findings can inform computational models of cortical information processing.