PT  - JOURNAL ARTICLE
AU  - Seidlitz, Jakob
AU  - Váša, František
AU  - Shinn, Maxwell
AU  - Romero-Garcia, Rafael
AU  - Whitaker, Kirstie J.
AU  - Vértes, Petra E.
AU  - Reardon, Paul Kirkpatrick
AU  - Clasen, Liv
AU  - Messinger, Adam
AU  - Leopold, David A.
AU  - Fonagy, Peter
AU  - Dolan, Raymond J.
AU  - Jones, Peter B.
AU  - Goodyer, Ian M.
AU  - , 
AU  - Raznahan, Armin
AU  - Bullmore, Edward T.
TI  - Morphometric Similarity Networks Detect Microscale Cortical Organisation and Predict Inter-Individual Cognitive Variation
AID  - 10.1101/135855
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 135855
4099  - http://biorxiv.org/content/early/2017/05/09/135855.short
4100  - http://biorxiv.org/content/early/2017/05/09/135855.full
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.