TY - JOUR T1 - Morphometric Similarity Networks Detect Microscale Cortical Organisation and Predict Inter-Individual Cognitive Variation JF - bioRxiv DO - 10.1101/135855 SP - 135855 AU - Jakob Seidlitz AU - František Váša AU - Maxwell Shinn AU - Rafael Romero-Garcia AU - Kirstie J. Whitaker AU - Petra E. Vértes AU - Paul Kirkpatrick Reardon AU - Liv Clasen AU - Adam Messinger AU - David A. Leopold AU - Peter Fonagy AU - Raymond J. Dolan AU - Peter B. Jones AU - Ian M. Goodyer AU - the NSPN Consortium AU - Armin Raznahan AU - Edward T. Bullmore Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/05/09/135855.abstract N2 - Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping, based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organisation comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs to tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions. ER -