Robust estimation of cortical similarity networks from brain MRI.

Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n > 11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically validated lens for cortical connectomics using readily available MRI data.

Keywords

Animals, Humans, Magnetic Resonance Imaging, Brain, Diffusion Magnetic Resonance Imaging, Connectome, Macaca

Journal Title

Nat Neurosci

Journal ISSN

1097-6256
1546-1726

Publisher

Springer Science and Business Media LLC

Publisher DOI

https://doi.org/10.1038/s41593-023-01376-7

Rights and licensing

Except where otherwised noted, this item's license is described as Attribution 4.0 International

Sponsorship

Medical Research Council (MR/M009041/1)

I.S. was generously supported by a Gates-Cambridge Scholarship, and by the Accelerate Programme for Scientific Discovery, funded by Schmidt Futures. J.S. was supported by NIMH T32MH019112. A.A.B and J.S. were supported by NIMH K08MH120564. V.W. was supported by St. Catharine’s College Cambridge. R.A.I.B. was supported by the Autism Research Trust. R.R.G. is funded by the EMERGIA Junta de Andalucía program (EMERGIA20\_00139) and the Plan Propio of the University of Seville. T.T.M. was supported by funds from NIH T32HG010464. E.T.B. was supported by an NIHR Senior Investigator award. S.E.M. was supported by the Accelerate Programme for Scientific Discovery, funded by Schmidt Futures, and a Fellowship from The Alan Turing Institute, London (EPSRC grant EP/N510129/1). We thank Dr. Lisa Ronan for help in processing the ABCD imaging data. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NIH, NHS, the NIHR or the Department of Health and Social Care. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9-10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium\_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from NIMH Data Archive Digital Object Identifier (10.15154/1528079)]. DOIs can be found at [https://dx.doi.org/10.15154/1528079. Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. All research at the Department of Psychiatry in the University of Cambridge was supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312) and the NIHR Applied Research Collaboration East of England. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. We also thank the Allen Human Brain Atlas (AHBA) for their valuable contributions to open science. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission.

Collections

University of Cambridge Research Outputs (Articles and Conferences)