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Characterizing Network Search Algorithms Developed for Dynamic Causal Modeling.

Sándor Csaba Aranyi1, Marianna Nagy2, Gábor Opposits1

  • 1Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.

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|June 28, 2021
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Summary
This summary is machine-generated.

This study introduces graph theoretical search algorithms for Dynamic Causal Modeling (DCM), enhancing brain network analysis. Topological search methods show promise for single-subject studies, while Bayesian model reduction remains optimal for complex statistical analyses.

Keywords:
dynamic causal modelingfMRImodel-spacenetwork topologysearch algorithm

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Dynamic Causal Modeling (DCM) is crucial for estimating effective brain network connectivity.
  • Identifying the best model explaining neural data is a key challenge in Bayesian modeling.
  • Heuristic search algorithms can find optimal models without pre-defined model sets, but are complex for large model spaces.

Purpose of the Study:

  • To adapt graph theoretical search algorithms for DCM analysis.
  • To create a framework for characterizing these search methods.
  • To investigate their relevance for single-subject and group-level neuroimaging studies.

Main Methods:

  • Reimplemented the deterministic bilinear DCM algorithm in the ReDCM R package for faster computation.
  • Adapted three network search algorithms for DCM, enhancing performance with posterior parameter estimates.
  • Separated model estimation from search algorithms using a database of full model-space parameters.
  • Utilized a 60-subject fMRI dataset for testing.

Main Results:

  • Topological search algorithms often outperform analytical methods in single-subject DCM analyses.
  • Network search methods achieve comparable results to Bayesian model reduction (BMR) for group-level network property recovery.
  • BMR remains the recommended approach for optimizing linear modeling schemes in higher-level statistical analyses.

Conclusions:

  • Adapted graph theoretical search algorithms offer a valuable framework for DCM.
  • Freely available databases of estimated model spaces can facilitate future DCM research.
  • The ReDCM package provides a useful tool for Bayesian inference in neuroscience.