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A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.

Jaylen Lee1, Sana Hussain2, Ryan Warnick3

  • 1Department of Statistics, University of California, Irvine, Irvine, California, United States of America.

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This summary is machine-generated.

This study introduces a Bayesian framework to analyze dynamic functional connectivity in brain imaging (fMRI) by incorporating physiological data. The method reveals how factors like pupil dilation influence brain state transitions.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • Dynamic functional connectivity (dFC) tracks changing brain region interactions during fMRI.
  • Physiological factors like attention and cognitive effort can modulate these connectivity states.
  • Existing methods may not fully capture the influence of time-varying physiological signals on brain network dynamics.

Purpose of the Study:

  • To develop a novel multi-subject Bayesian framework for estimating dynamic functional networks.
  • To integrate time-varying physiological covariates into the analysis of functional magnetic resonance imaging (fMRI) data.
  • To investigate the relationship between physiological changes (e.g., pupil dilation) and brain connectivity state transitions.

Main Methods:

  • Utilized a dynamic Gaussian graphical model with a non-homogeneous hidden Markov model to identify latent neurological states.
  • Incorporated time-varying exogenous physiological covariates to model state-transition probabilities across subjects.
  • Employed shrinkage priors for network sparsity and a Bayesian false discovery rate control for edge selection.

Main Results:

  • The developed Bayesian framework successfully estimated dynamic functional networks informed by physiological covariates.
  • The model identified recurrent connectivity patterns and allowed for network sharing among subjects.
  • Analysis of resting-state fMRI and pupillometry data revealed heterogeneous effects of pupil dilation on brain state transitions.

Conclusions:

  • The proposed Bayesian framework provides a robust method for analyzing dynamic functional connectivity influenced by physiological factors.
  • Understanding the heterogeneity of state occupancy across subjects offers insights into cognitive processing.
  • This approach enhances the interpretation of fMRI data by linking neural dynamics to concurrent physiological measurements.