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Paper Title

Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation

Authors

Peter Fonagy
Peter Fonagy
Raymond J. Dolan
Raymond J. Dolan
Edward T. Bullmore
Edward T. Bullmore
Rafael Romero-Garcia
Rafael Romero-Garcia
Ian M. Goodyer
Ian M. Goodyer
František Váša
František Váša
Petra E Vértes
Petra E Vértes
Jakob Seidlitz
Jakob Seidlitz

Article Type

Research Article

Journal

Neuron

Research Impact Tools

Issue

Volume : 97 | Issue : 1 | Page No : p231-247

Published On

January, 2018

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Abstract

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 organization 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 with 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.

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