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Stimulus evoked causality estimation in stereo-EEG.

Andrea Cometa1, Piergiorgio D'Orio2,3, Martina Revay2,4

  • 1BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio, 34, Pontedera, 56025, Italy.

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Summary

We developed a new statistical test for event-related causality (ERC) in stereo-electroencephalography (SEEG) data. This method accurately identifies brain network connections and is robust against noise.

Keywords:
SEEGconnectivityevent related causalitynon-parametric testpartial directed coherence

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Stereo-electroencephalography (SEEG) is crucial for analyzing brain function due to its high temporal and spatial resolution.
  • Investigating stimulus-evoked brain network interactions requires robust methods for evaluating information flow.
  • Existing methods for SEEG data lack explicit validation for causality assessment.

Purpose of the Study:

  • To introduce a novel non-parametric statistical test for event-related causality (ERC) specifically designed for SEEG recordings.
  • To present a data surrogation method for evaluating causality estimation algorithms.
  • To validate the proposed pipeline using surrogate SEEG data and compare it with existing effective connectivity measures.

Main Methods:

  • Development of a non-parametric statistical test for event-related causality (ERC) assessment.
  • Implementation of a data surrogation technique to evaluate causality estimation performance.
  • Validation using surrogate SEEG data and comparison with Geweke-Granger causality framework measures.

Main Results:

  • The developed workflow successfully identified all directed connections within the response window (RW) in surrogate SEEG data.
  • The procedure demonstrated robustness against synthetic noise levels exceeding physiological values.
  • Partial directed coherence was identified as the most effective causality measure among those tested.

Conclusions:

  • This study presents the first non-parametric statistical test for ERC estimation tailored for SEEG datasets.
  • The validated pipeline offers a reliable approach for analyzing directed brain network interactions.
  • The methodology is adaptable for other time-varying estimators with clearly defined response windows.