Quantifying the link between anatomical connectivity, gray matter volume and regional cerebral blood flow: an integrative MRI study.

• Main Authors: Bálint Várkuti, Mustafa Cavusoglu, Alexander Kullik, Björn Schiffler, Ralf Veit, Özge Yilmaz, Wolfgang Rosenstiel, Christoph Braun, Kamil Uludag, Niels Birbaumer, Ranganatha Sitaram
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2011-04-01
• Series: PLoS ONE
• Online Access: https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21525993/?tool=EBI

<h4>Background</h4>In the graph theoretical analysis of anatomical brain connectivity, the white matter connections between regions of the brain are identified and serve as basis for the assessment of regional connectivity profiles, for example, to locate the hubs of the brain. But regions of the brain can be characterised further with respect to their gray matter volume or resting state perfusion. Local anatomical connectivity, gray matter volume and perfusion are traits of each brain region that are likely to be interdependent, however, particular patterns of systematic covariation have not yet been identified.<h4>Methodology/principal findings</h4>We quantified the covariation of these traits by conducting an integrative MRI study on 23 subjects, utilising a combination of Diffusion Tensor Imaging, Arterial Spin Labeling and anatomical imaging. Based on our hypothesis that local connectivity, gray matter volume and perfusion are linked, we correlated these measures and particularly isolated the covariation of connectivity and perfusion by statistically controlling for gray matter volume. We found significant levels of covariation on the group- and regionwise level, particularly in regions of the Default Brain Mode Network.<h4>Conclusions/significance</h4>Connectivity and perfusion are systematically linked throughout a number of brain regions, thus we discuss these results as a starting point for further research on the role of homology in the formation of functional connectivity networks and on how structure/function relationships can manifest in the form of such trait interdependency.