Lifespan normative modeling of brain microstructure
9 affiliations
- 1Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. [email protected]
- 2Laboratory of Brain eScience, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, USA
- 3Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- 4Centre de recherche Azrieli, CHU Sainte-Justine, Department of Psychiatry and Addictology, University of Montreal, Montreal, QC, Canada
- 5Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Biobehavioral Sciences and Psychology, University of California, Los Angeles, CA, USA
- 6Multimodal Brain Image Analysis Laboratory, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
- 7Center of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands
- 8Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour. Radboud University, Nijmegen, The Netherlands
- 9Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. [email protected]
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 the first 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 new resource publicly available to the community, adaptable and extendable to future datasets as the available data expands.
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BibTeX
@article{villalonreina2026lifespan,
title = {{Lifespan normative modeling of brain microstructure}},
author = {Julio E. Villalón‐Reina and Alyssa H. Zhu and Leila Nabulsi and Sophia I. Thomopoulos and Clara A. Moreau and Yixue Feng and Tamoghna Chattopadhyay and Sebastian M. Benavidez and Leila Kushan and John P. John and Himanshu Joshi and Iyad Ba Gari and Katherine E. Lawrence and Talia M. Nir and Neda Jahanshad and Carrie E. Bearden and Seyed Mostafa Kia and André F. Marquand and the Frontotemporal Lobar Degeneration Neuroimaging Initiative and Paul M. Thompson},
journal = {Nature Communications},
year = {2026},
month = may,
doi = {10.1038/s41467-026-72875-x},
url = {https://doi.org/10.1038/s41467-026-72875-x},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Lifespan normative modeling of brain microstructure
AU - Julio E. Villalón‐Reina
AU - Alyssa H. Zhu
AU - Leila Nabulsi
AU - Sophia I. Thomopoulos
AU - Clara A. Moreau
AU - Yixue Feng
AU - Tamoghna Chattopadhyay
AU - Sebastian M. Benavidez
AU - Leila Kushan
AU - John P. John
AU - Himanshu Joshi
AU - Iyad Ba Gari
AU - Katherine E. Lawrence
AU - Talia M. Nir
AU - Neda Jahanshad
AU - Carrie E. Bearden
AU - Seyed Mostafa Kia
AU - André F. Marquand
AU - the Frontotemporal Lobar Degeneration Neuroimaging Initiative
AU - Paul M. Thompson
PY - 2026
DA - 2026/05/27
JO - Nature Communications
DO - 10.1038/s41467-026-72875-x
UR - https://doi.org/10.1038/s41467-026-72875-x
AB - 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 the first 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 new resource publicly available to the community, adaptable and extendable to future datasets as the available data expands.
SN - 2041-1723
ER -