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Related Experiment Video

Updated: Aug 7, 2025

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
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Predicting 'Brainage' in the Developmental Period using Structural MRI, Morphometric Similarity, and Machine

Daniel J Griffiths-King1, Amanda G Wood2, Jan Novak1

  • 1Aston University.

Research Square
|March 13, 2023
PubMed
Summary
This summary is machine-generated.

Researchers explored using morphometric similarity from structural MRI to predict brain age in children. This network-level approach did not outperform traditional measures like cortical thickness for assessing individual differences in brain development.

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Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Computational Psychiatry

Background:

  • Structural magnetic resonance imaging (MRI) is crucial for studying brain development.
  • Predicting an individual's age from brain scans (brain-age) using statistical learning is a growing area.
  • Few studies have explored higher-order or network-level representations for brain-age prediction.

Conclusions:

  • While morphometric similarity offers a novel way to analyze structural MRI data, it does not enhance brain-age prediction accuracy compared to conventional methods.
  • The network-level approach using morphometric similarity may not be optimally suited for studying individual differences in healthy brain development.
  • Future research may need to explore alternative network-based or higher-order representations for more sensitive brain-age modeling.