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Modeling differences in neurodevelopmental maturity of the reading network using support vector regression on

Oliver H M Lasnick1,2, Jie Luo1, Brianna Kinnie1

  • 1University of Connecticut, Storrs-Mansfield, CT, United States 06269.

Biorxiv : the Preprint Server for Biology
|August 13, 2025
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Summary

Brain-age models accurately predict maturity in reading networks. Poor readers show underestimated brain-age, indicating atypical neurodevelopment, while advanced readers show overestimation. This research offers insights into reading disorder (RD) and brain maturation.

Keywords:
brain-agedyslexiamaturationsupport vector regression (SVR)

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Computational Psychiatry

Background:

  • Brain-age indices, derived from neuroimaging data, can reveal neurodevelopmental trajectories.
  • Reading disorder (RD) is associated with atypical brain network maturation.
  • Functional connectivity (FC) patterns are crucial for understanding brain network development.

Purpose of the Study:

  • To investigate the relationship between reading ability and brain-age prediction using functional connectivity (FC) data.
  • To assess how different brain network models predict chronological age in individuals with and without reading difficulties.
  • To identify specific brain regions and networks critical for age prediction in the context of reading development.

Main Methods:

  • A cross-sectional study of 742 participants aged 6-21 years.
  • Support vector regression models were trained to predict chronological age from whole-brain and reading network-specific FC data.
  • Comparison of prediction accuracy across models with varying numbers of brain regions.

Main Results:

  • All models successfully predicted brain maturity, with the whole-brain model showing the highest accuracy.
  • Reading ability significantly influenced the brain-age gap: poor readers had underestimated brain-age, and advanced readers had overestimated brain-age.
  • Key predictive regions for brain-age were identified within the default mode and frontoparietal control networks.

Conclusions:

  • Brain-age indices derived from FC data are sensitive to reading ability and neurodevelopmental variations.
  • Atypical maturation of reading and language networks is linked to reading disorder.
  • The findings highlight the potential of neuroimaging-based brain-age models for understanding developmental disorders.