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Assessing parameter identifiability for dynamic causal modeling of fMRI data.

Carolin Arand1, Elisa Scheller2, Benjamin Seeber3

  • 1Center for Data Analysis and Modelling (FDM), University of Freiburg Freiburg, Germany ; Department of Physics, University of Freiburg Freiburg, Germany ; Department of Radiology, Medical Physics, University Medical Center Freiburg Freiburg, Germany.

Frontiers in Neuroscience
|March 10, 2015
PubMed
Summary

Assessing dynamic causal modeling (DCM) parameter identifiability in fMRI data is crucial. Our approach and DCMident toolbox help determine if data supports precise DCM parameter estimation, preventing suboptimal acquisition.

Keywords:
dynamic causal modelingfunctional magnetic resonance imagingmodel parametersparameter identifiabilityprofile likelihood

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Systems Biology

Background:

  • Dynamic Causal Modeling (DCM) analyzes effective connectivity in fMRI data.
  • Precise estimation of DCM parameters from fMRI data remains challenging.
  • Parameter identifiability is essential for valid inferences on directed brain connectivity.

Purpose of the Study:

  • To develop and present an approach for inferring the identifiability of parameters in an intended DCM based on fMRI data.
  • To investigate the impact of different imaging specifications on DCM parameter identifiability.
  • To provide a tool for assessing parameter identifiability prior to data acquisition.

Main Methods:

  • Utilized the "attention to motion" fMRI dataset.
  • Employed the profile likelihood method, adapted from systems biology, to assess parameter identifiability.
  • Evaluated two distinct DCMs under specified scanning parameters.

Main Results:

  • Identified that intermediate epoch duration, shorter TR, and longer session duration generally enhance data information content and improve parameter identifiability.
  • Found that densely interconnected regions within a DCM are prone to non-identifiability, irrespective of biological factors.
  • Demonstrated that the DCMident toolbox can assess parameter identifiability without relying on Bayesian priors.

Conclusions:

  • The developed approach and DCMident toolbox enable the evaluation of whether DCM parameters are sufficiently determined by the data.
  • Pre-study assessments using the DCMident toolbox can lead to improved parameter identifiability and prevent suboptimal data acquisition.
  • This method serves as a crucial preprocessing step for robust DCM analysis in fMRI research.