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Multivariate dynamical modelling of structural change during development.

Gabriel Ziegler1, Gerard R Ridgway2, Sarah-Jayne Blakemore3

  • 1Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany.

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Summary
This summary is machine-generated.

This study introduces a new dynamical systems framework to model brain changes over time using structural MRI. The model reveals consistent anterior-posterior gradients in brain development, particularly impacting the prefrontal cortex (PFC).

Keywords:
AgeingBayesian inferenceBrain maturationConnectivityDynamical systemsLongitudinal analysisStructural brain mapping

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Area of Science:

  • Neuroimaging
  • Dynamical Systems Theory
  • Developmental Neuroscience

Background:

  • Longitudinal changes in structural magnetic resonance imaging (MRI) are crucial for understanding brain development, aging, and neurodegeneration.
  • Existing methods often struggle to capture the complex, multivariate dynamics of these changes across multiple brain regions.
  • A dynamical systems approach offers a powerful framework to model these intricate longitudinal alterations.

Purpose of the Study:

  • To introduce a novel multivariate dynamical systems framework for characterizing longitudinal changes in structural MRI.
  • To model brain development, plasticity, aging, and degeneration by analyzing changes in multiple imaging biomarkers, such as regional gray matter volume.
  • To investigate region-specific developmental factors and interactions between brain regions.

Main Methods:

  • Developed a multivariate framework using dynamical systems to model longitudinal changes in structural MRI data.
  • Employed a linear system with inputs to account for developmental growth/decline factors, including region-specific sensitivities.
  • Utilized variational methods for model inversion and Bayesian inference, applied to a large, openly available longitudinal pediatric dataset (637 scans, 289 individuals).

Main Results:

  • Demonstrated dynamic cortical changes during brain maturation in individuals aged 6 to 22 years.
  • Modeled volumetric changes in 26 bilateral regions of interest (ROIs), accounting for puberty, early growth processes, and sexual dimorphism.
  • Identified a consistent anterior-posterior gradient in sensitivity to growth factors, with the prefrontal cortex (PFC) showing the strongest impact, similar across genders.

Conclusions:

  • The proposed dynamical systems framework effectively characterizes longitudinal structural brain changes.
  • Revealed specific patterns and gradients of brain maturation, highlighting the significant role of the prefrontal cortex.
  • The framework has potential for exploring structural changes across brain subnetworks and their relation to functional connectivity.