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Artificial Intelligence to Analyze the Cortical Thickness Through Age.

Sergio Ledesma1,2, Mario-Alberto Ibarra-Manzano2, Dora-Luz Almanza-Ojeda2

  • 1Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.

Frontiers in Artificial Intelligence
|November 1, 2021
PubMed
Summary

Artificial intelligence modeled brain cortical thickness changes across the lifespan in 1,100 healthy individuals. This analysis revealed distinct developmental patterns for brain regions, highlighting the need for dynamic modeling.

Keywords:
adaptive modelsartificial neural networkchanges with agecortical thicknessderivativemodelingneuroimaging

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Artificial Intelligence

Background:

  • Cortical thickness is a key neuroanatomical feature.
  • Understanding age-related changes in cortical thickness is crucial for brain health.
  • Previous models often simplify complex, non-linear brain development.

Purpose of the Study:

  • To model age-related cortical thickness dynamics in distinct brain regions.
  • To identify periods of cortical thickness increase and reduction throughout life.
  • To investigate hemispheric differences in cortical development patterns.

Main Methods:

  • Utilized a dataset of cortical thickness from 1,100 healthy individuals across 62 brain regions.
  • Trained artificial neural networks (ANNs) to model cortical thickness as a function of age.
  • Employed numerical differentiation to calculate the rate of change (derivative) of cortical thickness.

Main Results:

  • Developed dynamic models for cortical thickness in each brain region.
  • Identified specific life periods associated with cortical thickness increase or decrease.
  • Observed unique developmental trajectories in some left hemisphere regions compared to their right hemisphere counterparts.
  • Clustered brain regions with similar age-related cortical thickness patterns.

Conclusions:

  • Each brain region exhibits unique, dynamic age-related cortical thickness changes.
  • Artificial neural networks are effective for modeling complex, non-linear neurodevelopmental data.
  • The findings emphasize the need for region-specific, dynamic approaches to understanding brain aging.