Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies
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Nature Research
Department of Clinical Neurosciences
http://dx.doi.org/10.1038/s41551-024-01242-2
Department of Clinical Neurosciences
http://dx.doi.org/10.1038/s41551-024-01242-2
Abstract
Description
Patterns of neural activity underlie human cognition. Transitions between these patterns are orchestrated by the brain’s network architecture. What are the mechanisms linking network structure to cognitively relevant activation patterns? Here we implement principles of network control to investigate how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic engine. We also systematically incorporate neurotransmitter receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range
of mental health, neurodegenerative, psychiatric, and neurodevelopmental diagnostic categories (N = 17 000 patients, N = 22 000 controls). Integrating large-scale multimodal neuroimaging data from functional MRI, diffusion tractography, cortical morphometry, and positron emission tomography, we simulate how anatomically-guided transitions between cognitive states can be reshaped by neurotransmitter engagement or changes in cortical thickness. Our results provide a comprehensive look-up table charting how brain network organisation and chemoarchitecture interact to manifest different cognitive topographies. This computational framework establishes a principled foundation for systematically identifying ways to promote selective transitions between desired cognitive topographies.
A.I.L. is supported by the Molson Neuro-Engineering Fellowship and FRQNT Strategic Clusters Program (2020-RS4-265502 - Centre UNIQUE - Union Neuroscience & Artificial Intelligence - Quebec) via the UNIQUE Neuro-AI Excellence Scholarship. S.P.S. is supported by the National Science Foundation Graduate Research Fellowship (Grant No. DGE-1650441). J.Y.H. is supported by the Helmholtz International BigBrain Analytics & Learning Laboratory, the Natural Sciences and Engineering Research Council of Canada, and Fonds de reserches de Québec. D.B. is supported by the Brain Canada Foundation, through the Canada Brain Research Fund, with the financial support of Health Canada, National Institutes of Health (grants no. NIH R01 AG068563A and NIH R01 R01DA053301-01A1 to DB), the Canadian Institute of Health Research (grants no. CIHR 438531 and CIHR 470425 to D.B.), the Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund), Google (Research Award, Teaching Award to D.B.) and by the CIFAR Artificial Intelligence Chairs programme (Canada Institute for Advanced Research to D.B.). A.K. is supported by the National Institutes of Health (RF1MH123232, R01NS102646 and R21NS104634). B.M. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research (CIHR), Brain Canada Foundation Future Leaders Fund, the Canada Research Chairs Program, the Michael J. Fox Foundation, and the Healthy Brains for Healthy Lives initiative.
A.I.L. is supported by the Molson Neuro-Engineering Fellowship and FRQNT Strategic Clusters Program (2020-RS4-265502 - Centre UNIQUE - Union Neuroscience & Artificial Intelligence - Quebec) via the UNIQUE Neuro-AI Excellence Scholarship. S.P.S. is supported by the National Science Foundation Graduate Research Fellowship (Grant No. DGE-1650441). J.Y.H. is supported by the Helmholtz International BigBrain Analytics & Learning Laboratory, the Natural Sciences and Engineering Research Council of Canada, and Fonds de reserches de Québec. D.B. is supported by the Brain Canada Foundation, through the Canada Brain Research Fund, with the financial support of Health Canada, National Institutes of Health (grants no. NIH R01 AG068563A and NIH R01 R01DA053301-01A1 to DB), the Canadian Institute of Health Research (grants no. CIHR 438531 and CIHR 470425 to D.B.), the Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund), Google (Research Award, Teaching Award to D.B.) and by the CIFAR Artificial Intelligence Chairs programme (Canada Institute for Advanced Research to D.B.). A.K. is supported by the National Institutes of Health (RF1MH123232, R01NS102646 and R21NS104634). B.M. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research (CIHR), Brain Canada Foundation Future Leaders Fund, the Canada Research Chairs Program, the Michael J. Fox Foundation, and the Healthy Brains for Healthy Lives initiative.