Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation.
Kirkpatrick Reardon, Paul
Leopold, David A
Dolan, Raymond J
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Seidlitz, J., Váša, F., Shinn, M., Romero-Garcia, R., Whitaker, K., Vertes, P., Wagstyl, K., et al. (2018). Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation.. Neuron, 97 (1), 231-247.e7. https://doi.org/10.1016/j.neuron.2017.11.039
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.
NSPN Consortium, Cerebral Cortex, Neural Pathways, Animals, Macaca, Humans, Magnetic Resonance Imaging, Intelligence, Cognition, Female, Male, Young Adult, Connectome
Wellcome Trust (095844/Z/11/Z)
MEDICAL RESEARCH COUNCIL (G0001354)
External DOI: https://doi.org/10.1016/j.neuron.2017.11.039
This record's URL: https://www.repository.cam.ac.uk/handle/1810/271843