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

Accepted version
Peer-reviewed

Type

Article

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Authors

Váša, František 
Shinn, Maxwell 
Romero-Garcia, Rafael 
Whitaker, Kirstie J 

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.

Description

Keywords

IQ, MRI, connectome, cross-species, cytoarchitecture, gene expression, macaque, morphology, multi-modal, Animals, Cerebral Cortex, Cognition, Connectome, Female, Humans, Intelligence, Macaca, Magnetic Resonance Imaging, Male, Neural Pathways, Young Adult

Journal Title

Neuron

Conference Name

Journal ISSN

0896-6273
1097-4199

Volume Title

97

Publisher

Elsevier BV
Sponsorship
Wellcome Trust (095844/Z/11/Z)
Medical Research Council (MR/K020706/1)
Medical Research Council (G1000183)
Medical Research Council (G0001354)
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