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<p>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 <i>N</i> = 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.</p>

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Lifespan normative modeling of brain microstructure

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月27日

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

摘要

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.