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A synergistic core for human brain evolution and cognition
File | Description | Size | Format | |
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A synergistic core for human brain evolution and cognition.pdf | Accepted version | 5.64 MB | Adobe PDF | View/Open |
Title: | A synergistic core for human brain evolution and cognition |
Authors: | Luppi, A Mediano, PAM Rosas, FE Holland, N Fryer, TD O'Brien, JT Rowe, JB Menon, DK Bor, D Stamatakis, EA |
Item Type: | Journal Article |
Abstract: | How does the organization of neural information processing enable humans’ sophisticated cognition? Here we decompose functional interactions between brain regions into synergistic and redundant components, revealing their distinct information-processing roles. Combining functional and structural neuroimaging with meta-analytic results, we demonstrate that redundant interactions are predominantly associated with structurally coupled, modular sensorimotor processing. Synergistic interactions instead support integrative processes and complex cognition across higher-order brain networks. The human brain leverages synergistic information to a greater extent than nonhuman primates, with high-synergy association cortices exhibiting the highest degree of evolutionary cortical expansion. Synaptic density mapping from positron emission tomography and convergent molecular and metabolic evidence demonstrate that synergistic interactions are supported by receptor diversity and human-accelerated genes underpinning synaptic function. This information-resolved approach provides analytic tools to disentangle information integration from coupling, enabling richer, more accurate interpretations of functional connectivity, and illuminating how the human neurocognitive architecture navigates the trade-off between robustness and integration. |
Issue Date: | Jun-2022 |
Date of Acceptance: | 30-Mar-2022 |
URI: | http://hdl.handle.net/10044/1/115643 |
DOI: | 10.1038/s41593-022-01070-0 |
ISSN: | 1097-6256 |
Publisher: | Nature Research |
Start Page: | 771 |
End Page: | 782 |
Journal / Book Title: | Nature Neuroscience |
Volume: | 25 |
Issue: | 6 |
Copyright Statement: | Copyright © 2022 Springer-Verlag. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1038/s41593-022-01070-0 |
Publication Status: | Published |
Online Publication Date: | 2022-05-26 |
Appears in Collections: | Computing Faculty of Medicine Department of Brain Sciences Faculty of Engineering |