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Updated: Jan 11, 2026

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Lifespan Normative Modeling of Brain Microstructure.

Julio E Villalón-Reina1, Alyssa H Zhu1, Sebastian Benavidez1

  • 1Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

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Summary
This summary is machine-generated.

Scientists created the first large-scale normative models for brain white matter (WM) microstructure using diffusion MRI data from over 54,000 individuals. This resource helps detect brain abnormalities across the lifespan.

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Area of Science:

  • Neuroimaging
  • Biostatistics
  • Human Brain Anatomy

Background:

  • Normative models of brain metrics are crucial for identifying abnormalities in various neurological and psychiatric disorders.
  • Existing models do not adequately cover the brain's white matter (WM) microstructure.
  • A comprehensive model is needed for accurate diagnosis and research.

Purpose of the Study:

  • To develop the first large-scale normative model of brain white matter (WM) microstructure.
  • To establish age- and sex-specific statistical distributions for WM metrics.
  • To provide a tool for detecting and visualizing WM abnormalities in different conditions.

Main Methods:

  • Utilized 19 international diffusion MRI datasets (N=54,583, ages 4-91).
  • Extracted regional diffusion tensor imaging (DTI) metrics using standardized protocols.
  • Applied hierarchical Bayesian regression (HBR) to model WM metrics against age and sex.

Main Results:

  • Generated average lifespan trajectories and centile curves for WM microstructure across regions.
  • Demonstrated utility in detecting WM deviations in mild cognitive impairment, Alzheimer's disease, and 22q11.2 deletion syndrome.
  • The model provides a common reference for identifying disease effects and influencing factors.

Conclusions:

  • The developed large-scale normative model of WM microstructure is a novel resource for the scientific community.
  • It enables identification of individual or group deviations and comparison across disorders.
  • The model is adaptable and extendable, supporting future research on brain health and disease.