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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Functional similarities and distance properties.

Michael Muskulus1, Sanne Houweling, Sjoerd Verduyn-Lunel

  • 1Mathematical Institute, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands. muskulus@math.leidenuniv.nl

Journal of Neuroscience Methods
|July 11, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces Wasserstein distances for analyzing brain connectivity, offering a metric approach for neurophysiological data. This method reveals cortico-muscular synchrony and motor area crosstalk during bimanual tasks.

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Last Updated: Jun 21, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Published on: March 1, 2022

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

  • Neuroscience
  • Computational Biology
  • Signal Processing

Background:

  • Functional and effective brain connectivity analysis is crucial for understanding brain structure-function relationships and has clinical applications.
  • Many current connectivity measures lack metric properties (reflexivity, symmetry, triangle inequality), potentially hindering interpretation and analysis.
  • Metric connectivity indices can be treated as functional distances, enabling representation in Euclidean space via multidimensional scaling.

Purpose of the Study:

  • To explore classes of connectivity measures that allow for Euclidean space reconstruction, focusing on Wasserstein distances.
  • To discuss the advantages of Wasserstein distances for interpreting cortical activity from magnetoencephalography (MEG) data.
  • To apply these metric measures to real neurophysiological data for enhanced analysis.

Main Methods:

  • Investigated classes of metric measures, specifically Wasserstein distances, for brain connectivity analysis.
  • Utilized multidimensional scaling to represent connectivity indices in Euclidean space.
  • Applied Wasserstein distances to relative circular variances from magnetoencephalography (MEG) recordings.

Main Results:

  • Demonstrated that Wasserstein distances can be used to reconstruct connectivity in Euclidean space.
  • The application to MEG data during a bimanual task successfully identified cortico-muscular synchrony.
  • Revealed crosstalk between bilateral primary motor areas in the beta-band using Wasserstein distances.

Conclusions:

  • Wasserstein distances offer a mathematically sound approach to brain connectivity analysis, respecting metric properties.
  • This metric-based analysis provides valuable insights into neurophysiological data, such as cortico-muscular synchrony and inter-hemispheric motor communication.
  • The findings support the use of Wasserstein distances as a powerful tool for interpreting cortical activity and advancing brain modeling.