Cortical anatomical networks identified by structural MRI

Abstract

Event Abstract Back to Event Cortical anatomical networks identified by structural MRI A C. Evans1*, Y. He1 and Z. Chen1 1 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill Uni., Canada Most imaging studies of brain connectivity have focused upon functional connectivity (Micheloyannis et al. 2006; Stam et al. 2007, Salvador et al. 2005; Achard et al. 2006) These studies demonstrated that functional networks have “small-world” properties, where the minimum path length between any pair of nodes approximates that of a comparable random network but the nodes have greater local interconnectivity (Watts and Strogatz 1998). However, little is known about anatomical connectivity. Connection models of the human brain are usually inferred from primate research (Stephan et al. 2000; Penny et al. 2004). We have developed a methodology for characterizing the regional correlation of MRI-derived cortical thickness, using (i) the CLASP algorithm for the fully-automated extraction of cortical surfaces (MacDonald et al., 2000 ; Kim et al., 2005, Lyttelton et al., 2007), and (ii) a graph theoretical approach to the study of cortical morphology. This technique, GRETNA (Figure 1), examines the correlation among morphological properties of different cortical regions. It employs network theory to assess the clustering (Cp) and mean path length (Lp) among cortical regions. GRETNA was applied to brain MRI data from 124 right-handed normal adults. The resultant brain network contained 45 nodes and 102 edges2 (~7% of all possible pairs). It demonstrated significant short- and long-range correlations of cortical thickness, corresponding to known fibre connections. The pattern of coordinated variation in the thickness was neither regular nor random but “small-world” in nature, characterized by a high Cp and short Lp. GRETNA revealed a modular architecture in the normal structural brain network which reflects known functional neuroanatomy (Chen et al., 2008). Six modules with denser intra-module than inter-module connections (Newman and Girvan, 2004). This segregation into six modules with apparent functional significance suggests that functional organization of human brain networks may have a modular anatomical correlate. Finally, we used GRETNA to investigate the small-world efficiency of cortical networks in 425 relapsing–remitting MS patients (Charil et al., 2007). Using total WM lesion load (TWMLL) as a measure of disease severity, we defined six groups with increasing mean TWMLL. Both local (~Cp) and global (~1/Lp) efficiency showed significantly negative correlation with TWMLL (Fig. 2). Our results suggest that distributed cortical networks exhibit reduced efficiency in MS, consistent with aberrant fibre connection caused by MS lesions in white matter. Conference: Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008. Presentation Type: Oral Presentation Topic: Workshop Citation: Evans AC, He Y and Chen Z (2008). Cortical anatomical networks identified by structural MRI. Front. Neuroinform. Conference Abstract: Neuroinformatics 2008. doi: 10.3389/conf.neuro.11.2008.01.152 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 28 Jul 2008; Published Online: 28 Jul 2008. * Correspondence: A C Evans, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill Uni., Montréal, Canada, nemoABS01@frontiersin.org Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers A C Evans Y. He Z. Chen Google A C Evans Y. He Z. Chen Google Scholar A C Evans Y. He Z. Chen PubMed A C Evans Y. He Z. Chen Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.