RT Journal Article SR Electronic T1 Morphometric Similarity Networks Detect Microscale Cortical Organisation and Predict Inter-Individual Cognitive Variation JF bioRxiv FD Cold Spring Harbor Laboratory SP 135855 DO 10.1101/135855 A1 Jakob Seidlitz A1 František Váša A1 Maxwell Shinn A1 Rafael Romero-Garcia A1 Kirstie J. Whitaker A1 Petra E. Vértes A1 Paul Kirkpatrick Reardon A1 Liv Clasen A1 Adam Messinger A1 David A. Leopold A1 Peter Fonagy A1 Raymond J. Dolan A1 Peter B. Jones A1 Ian M. Goodyer A1 the NSPN Consortium A1 Armin Raznahan A1 Edward T. Bullmore YR 2017 UL http://biorxiv.org/content/early/2017/05/09/135855.abstract AB 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.