Lifespan normative modeling of brain microstructure

Normative models of brain metrics based on large populations could be extremely valuable for detecting brain abnormalities in patients with a variety of disorders, including degenerative, psychiatric and neurodevelopmental conditions, but no such models exist for the brain's white matter (WM) microstructure. Here we present a large-scale normative model of brain WM microstructure - based on 19 international diffusion MRI datasets covering almost the entire lifespan (totaling N = 54,583 individuals; age: 4-91 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol and used hierarchical Bayesian regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex. We extracted the average lifespan trajectories and corresponding centile curves for each WM region. We illustrate the utility of the method by applying it to detect and visualize profiles of WM microstructural deviations in a variety of contexts: in mild cognitive impairment, Alzheimer's disease, and 22q11.2 deletion syndrome - a neurogenetic condition that markedly increases risk for schizophrenia. The resulting large-scale model provides a common reference to identify disease effects on the brain's microstructure in individuals or groups, and to compare disorders, and discover factors affecting WM abnormalities. The derived normative models are a valuable resource publicly available to the community, adaptable and extendable to future datasets as the available data expands.

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Publication Details

Journal
Nature Communications
Published
2026-05-27
DOI
https://doi.org/10.1038/s41467-026-72875-x
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Lifespan normative modeling of brain microstructure

Clara Moreau, Iyad Ba Gari, Sebastian Benavidez, Alyssa H. Zhu et al.
Nature Communications
Advanced Neuroimaging Techniques and Applications
article

Lifespan normative modeling of brain microstructure

Clara Moreau, Iyad Ba Gari, Sebastian Benavidez, Alyssa H. Zhu, Tamoghna Chattopadhyay, Katherine E. Lawrence, Yixue Feng, Seyed Mostafa Kia, Leila Kushan, Talia M. Nir, Sophia I. Thomopoulos, Andre F. Marquand, Carrie E. Bearden, the Alzheimer’s Disease Neuroimaging Initiative, Neda Jahanshad, Julio E. Villalón-Reina, John P. John, Himanshu Joshi, Leila Nabulsi, Paul M. Thompson
article en

Abstract

Normative models of brain metrics based on large populations could be extremely valuable for detecting brain abnormalities in patients with a variety of disorders, including degenerative, psychiatric and neurodevelopmental conditions, but no such models exist for the brain's white matter (WM) microstructure. Here we present a large-scale normative model of brain WM microstructure - based on 19 international diffusion MRI datasets covering almost the entire lifespan (totaling N = 54,583 individuals; age: 4-91 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol and used hierarchical Bayesian regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex. We extracted the average lifespan trajectories and corresponding centile curves for each WM region. We illustrate the utility of the method by applying it to detect and visualize profiles of WM microstructural deviations in a variety of contexts: in mild cognitive impairment, Alzheimer's disease, and 22q11.2 deletion syndrome - a neurogenetic condition that markedly increases risk for schizophrenia. The resulting large-scale model provides a common reference to identify disease effects on the brain's microstructure in individuals or groups, and to compare disorders, and discover factors affecting WM abnormalities. The derived normative models are a valuable resource publicly available to the community, adaptable and extendable to future datasets as the available data expands.

Nature CommunicationsVol. 17(1)
University of Southern California (US), Radboud University Nijmegen (NL), University of California, Los Angeles (US), Tilburg University (NL), Centre Hospitalier Universitaire Sainte-Justine (CA), National Institute of Mental Health and Neurosciences (IN)
U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Wellcome Trust, Chongqing Postdoctoral Science Foundation, QIMR Berghofer Medical Research Institute, Queensland Cyber Infrastructure Foundation, Cancer Research UK, British Heart Foundation, University of Queensland, National Natural Science Foundation of China, China Postdoctoral Science Foundation, Eisai, Northwest Regional Development Agency, Natural Science Foundation of Chongqing, Southwest University, Fok Ying Tung Education Foundation, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Directorate for Biological Sciences, Genentech, Canadian Institutes of Health Research, Medical Research Council, National Health and Medical Research Council, Fundamental Research Funds for the Central Universities, National Institute on Aging, National Institute of Mental Health, NIH Office of the Director, Fogarty International Center, National Institute of Child Health and Human Development
Openalex Percentile: Top 6%
Advanced Neuroimaging Techniques and Applications
DOIOpen Access PDFOpenAlex
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