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  11. Lifespan Normative Models Of White Matter Fractional Anisotropy: Applications To Early Psychosis.
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  11. Lifespan Normative Models Of White Matter Fractional Anisotropy: Applications To Early Psychosis.

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Lifespan Normative Models of White Matter Fractional Anisotropy: Applications to Early Psychosis.

Ramona Cirstian1, Natalie J Forde1, Gary Zhang2

  • 1Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands.

Biological Psychiatry
|August 3, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study created normative models of white matter (WM) organization using diffusion MRI data from over 25,000 individuals. These models help identify WM differences in psychosis, improving psychiatric assessments.

Keywords:
Diffusion MRILifespan trajectoriesNormative modelsPrecision medicine

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Biomedical Engineering

Background:

  • Developed large-scale normative models of white matter (WM) organization across the lifespan (0-100 years) using diffusion MRI data from over 25,000 healthy individuals.
  • These models capture lifespan trajectories and inter-individual variation in fractional anisotropy (FA), a key marker of WM integrity.
  • Addressed challenges like non-Gaussian data distributions, self-reported race, and site effects to provide robust reference baselines.

Purpose of the Study:

  • To establish comprehensive normative reference standards for white matter organization across the human lifespan.
  • To apply these normative models to a psychosis cohort for identifying deviations associated with the disorder.
  • To enhance the precision of psychiatric assessments through individualized analysis of white matter integrity.
Psychosis
White matter integrity

Main Methods:

  • Constructed normative models of white matter fractional anisotropy (FA) using diffusion MRI data from a large, diverse cohort.
  • Applied multi-view sparse canonical correlation analysis (msCCA) to map psychosis symptoms onto deviations from normative FA models.
  • Incorporated methods to account for data non-Gaussianity, self-reported race, and site effects.

Main Results:

  • Revealed extensive white matter heterogeneity in psychosis, surpassing the sensitivity of traditional group-level analyses.
  • Identified specific key regions, including the right uncinate fasciculus and thalami, exhibiting significant deviations in psychosis.
  • Demonstrated the utility of normative models in detecting subtle, individualized white matter alterations.

Conclusions:

  • The developed normative models serve as valuable tools for identifying individualized white matter deviations.
  • These models promise to improve the precision and personalization of psychiatric assessments.
  • All normative models are publicly accessible for broader research and clinical application.