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Related Experiment Video

Updated: Oct 5, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Charting brain growth and aging at high spatial precision.

Saige Rutherford^1,2,3, Charlotte Fraza^1,2, Richard Dinga^1,2

¹Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands.

|February 1, 2022

Summary

This summary is machine-generated.

This study created brain imaging reference models from over 58,000 individuals to understand variations in brain structure across the lifespan. These models help quantify individual differences relevant to medical conditions and psychiatric disorders.

Keywords:

big data brain chart growth chart human individual prediction lifespan neuroscience normative model

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Area of Science:

Neuroscience
Medical Imaging
Population Health

Background:

Understanding inter-individual variability in brain structure is crucial for medical research.
Reference models are needed to study deviations from population norms and their relation to health and disease.

Purpose of the Study:

To develop normative models of brain structure (cortical thickness and subcortical volume) across the lifespan.
To validate these models and demonstrate their clinical utility in psychiatric research.

Main Methods:

Assembled a large neuroimaging cohort (N=58,836) from 82 sites, covering ages 2-100.
Applied normative modeling to characterize lifespan trajectories of brain structure.
Validated models against a quality-controlled subset (N=24,354) and applied them to a psychiatric sample (N=1985).

Main Results:

Established normative models for cortical thickness and subcortical volume across the lifespan.
Demonstrated the models' ability to quantify individual variability in brain structure.
Showcased clinical value by refining case-control inferences in a transdiagnostic psychiatric sample.

Conclusions:

The developed reference models provide a common platform for analyzing neuroimaging data and understanding population variation.
These models facilitate the quantification of individual deviations relevant to medical conditions and support personalized clinical decision-making.
Future work will augment models with more data and imaging modalities.