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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Simulations to benchmark time-varying connectivity methods for fMRI.

William Hedley Thompson1,2, Craig Geoffrey Richter3, Pontus Plavén-Sigray2

  • 1Department of Psychology, Stanford University, Palo Alto, California, United States of America.

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Quantifying time-varying connectivity (TVC) in brain imaging is challenging due to unknown ground truth. Our tvc_benchmarker Python package offers simulations to evaluate and compare different TVC estimation methods for improved accuracy.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Connectivity Analysis

Background:

  • Quantifying time-varying connectivity (TVC) from neuroimaging data like fMRI is crucial for understanding brain dynamics.
  • Existing TVC methods lack standardized evaluation, hindering inter-study comparisons and trust in results.
  • The ground truth for TVC in the brain remains unknown, posing challenges for accuracy assessment.

Purpose of the Study:

  • To introduce tvc_benchmarker, a Python package with simulations for testing and evaluating time-varying connectivity methods.
  • To provide a standardized benchmark for assessing the performance of different TVC estimation approaches.
  • To facilitate the comparison and development of novel TVC methods.

Main Methods:

  • Development of tvc_benchmarker, a Python package featuring four distinct simulation environments.
  • Evaluation of five representative TVC estimation methods: sliding window, tapered sliding window, multiplication of temporal derivatives, spatial distance, and jackknife correlation.
  • Design of simulations to specifically test the ability of methods to track temporal covariance changes.

Main Results:

  • All evaluated TVC methods showed positive correlations with each other.
  • Significant variations in the strength of correlations were observed between different methods.
  • The simulations effectively highlighted differences in how methods track changes in covariance over time.

Conclusions:

  • The developed simulations within tvc_benchmarker can serve as benchmark tests for evaluating TVC methods.
  • The package enables researchers to easily add, compare, and submit their own TVC methods for performance evaluation.
  • Standardized benchmarking is essential for advancing the field of time-varying brain connectivity analysis.