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Related Experiment Video

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Multiscale multivariate transfer entropy and application to functional corticocortical coupling.

Yuanyuan Zhang1, Xiaoling Chen1, Xiaohui Pang1

  • 1Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China.

Journal of Neural Engineering
|December 28, 2020
PubMed
Summary
This summary is machine-generated.

A new method, multiscale multivariate transfer entropy (MSMVTE), accurately identifies direct brain network interactions across multiple time scales. This reveals directional motor cortex connectivity and scale-dependent coupling during force tasks.

Keywords:
direct interactionfunctional corticocortical couplingmultiscalemultivariate time series

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Area of Science:

  • Neuroscience
  • Complex Systems Analysis
  • Network Science

Background:

  • Complex biological systems exhibit multi-level correlations, including connectivity, multiscale properties, and nonlinearity.
  • Existing methods for analyzing functional corticocortical coupling (FCCC) often focus on single aspects, limiting a comprehensive understanding of brain network dynamics.
  • Investigating direct interactions across multiple time scales is crucial for accurately modeling complex brain systems.

Purpose of the Study:

  • To introduce a novel method, multiscale multivariate transfer entropy (MSMVTE), for analyzing direct interactions in brain networks at multiple time scales.
  • To demonstrate the superiority of MSMVTE over existing methods (MSTE, MuTE) in detecting direct relationships and mitigating spurious indirect effects.
  • To apply MSMVTE to analyze FCCC during a unilateral right-hand steady-state force task.

Main Methods:

  • Extension of the multivariate transfer entropy (MuTE) method to develop MSMVTE.
  • Validation using three simulation models to compare MSMVTE with multiscale transfer entropy (MSTE) and MuTE.
  • Application of MSMVTE to analyze functional corticocortical coupling (FCCC) data from a unilateral right-hand steady-state force task.

Main Results:

  • MSMVTE effectively identified direct interactions and avoided spurious indirect relationships in simulations, outperforming MSTE and MuTE.
  • Analysis of FCCC revealed significantly higher connectivity from the left to the right premotor/sensorimotor cortex compared to the opposite direction.
  • Stronger connectivities were observed from central motor areas to bilateral premotor/sensorimotor areas, with maximum coupling strength at a scale of 3-10.

Conclusions:

  • MSMVTE is an effective tool for characterizing direct relationships and multiscale properties in complex systems, including brain networks.
  • Enhanced FCCC during motor tasks indicates more extensive activation and interaction within cortical motor regions.
  • Brain neurodynamics are influenced by both emergent small-scale activity and large-scale constraining effects, necessitating multiscale analysis.