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Test-retest reliability of regression dynamic causal modeling.

Stefan Frässle1, Klaas E Stephan1,2

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Regression dynamic causal modeling (rDCM) offers reliable whole-brain effective connectivity estimates, especially for strong connections. This method shows high consistency across sessions and holds promise for connectomics and clinical use.

Keywords:
ConnectomicsEffective connectivityGenerative modelRegression dynamic causal modelingTest-retest reliabilityrDCM

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience

Background:

  • Regression dynamic causal modeling (rDCM) is a new, efficient method for whole-brain effective connectivity.
  • Previous studies confirmed its face and construct validity.

Purpose of the Study:

  • Assess the test-retest reliability of rDCM.
  • Evaluate group-level consistency of connection-specific estimates and whole-brain patterns over sessions.
  • Compare rDCM reliability with functional connectivity measures.

Main Methods:

  • Utilized the Human Connectome Project dataset across eight paradigms (tasks and rest).
  • Employed two different parcellation schemes.
  • Analyzed test-retest reliability and group-level consistency of rDCM estimates.

Main Results:

  • rDCM demonstrated high group-level consistency of connectivity estimates across sessions.
  • Test-retest reliability increased with connection strength, with strong connections showing good to excellent reliability.
  • Whole-brain connectivity patterns identified individual participants with high accuracy.
  • rDCM favorably compared to functional connectivity, particularly for strong connections.
  • Task-based connectivity estimates were generally more reliable than resting-state estimates.

Conclusions:

  • rDCM provides reliable and consistent whole-brain effective connectivity estimates.
  • The method shows significant potential for human connectomics and clinical applications.
  • Reliability is enhanced when focusing on stronger neural connections and task-based paradigms.