Mapping the structure-function relationship along macroscale gradients in the human brain

Evan Collins (), Omar Chishti, Sami Obaid, Hari McGrath, Alex King, Xilin Shen, Jagriti Arora, Xenophon Papademetris, R. Todd Constable, Dennis D. Spencer and Hitten P. Zaveri
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Evan Collins: Yale School of Medicine
Omar Chishti: Yale School of Medicine
Sami Obaid: Yale School of Medicine
Hari McGrath: Yale School of Medicine
Alex King: Yale School of Medicine
Xilin Shen: Yale School of Medicine
Jagriti Arora: Yale School of Medicine
Xenophon Papademetris: Yale University
R. Todd Constable: Yale School of Medicine
Dennis D. Spencer: Yale School of Medicine
Hitten P. Zaveri: Yale School of Medicine

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Functional coactivation between human brain regions is partly explained by white matter connections; however, how the structure-function relationship varies by function remains unclear. Here, we reference large data repositories to compute maps of structure-function correspondence across hundreds of specific functions and brain regions. We use natural language processing to accurately predict structure-function correspondence for specific functions and to identify macroscale gradients across the brain that correlate with structure-function correspondence as well as cortical thickness. Our findings suggest structure-function correspondence unfolds along a sensory-fugal organizational axis, with higher correspondence in primary sensory and motor cortex for perceptual and motor functions, and lower correspondence in association cortex for cognitive functions. Our study bridges neuroscience and natural language to describe how structure-function coupling varies by region and function in the brain, offering insight into the diversity and evolution of neural network properties.

Date: 2024
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DOI: 10.1038/s41467-024-51395-6

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