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Abstract #0210

Unique insights into visual network development over childhood and adolescence from microstructure informed tractography

Simona Schiavi1, Sila Genc2, Maxime Chamberland2, Chantal M.W. Tax2,3, Erika P. Raven4, Alessandro Daducci1, and Derek K Jones2,5
1Department of Computer Science, University of Verona, Verona, Italy, 2CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom, 3Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, 4Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States, 5Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia

We employed the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) approach to construct microstructure-informed connectomes and study the distinct patterns of age-related development in structural whole-brain network and sub-networks using global graph metrics. Whole brain analyses showed that with the new edge-weighting, the shortest-path length between all pairs of nodes decreases with age and thus efficiency increases. This reduction in shortest-path length is probably driven by previously reported age-related increases in the intra-axonal signal fraction. Sub-networks analyses revealed unique visual network characteristics over development and confirmed previously observed maturational pattern of posterior regions across childhood and adolescence.

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