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Related Experiment Video

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Proper reference selection and re-referencing to mitigate bias in single pulse electrical stimulation data.

Harvey Huang1, Joshua A Adkinson2, Michael A Jensen1

  • 1Medical Scientist Training Program, Mayo Clinic, Rochester, MN, USA.

Journal of Neuroscience Methods
|April 24, 2025
PubMed
Summary

Reference contamination in brain stimulation evoked potentials can skew results. This study introduces metrics and methods to detect and fix this issue, improving brain connectivity analysis.

Keywords:
Brain connectivityBrain stimulation evoked potentialsCortico-corticalEvoked potentialsIntracranial EEGRe-referencingSingle pulse electrical stimulationStereo-EEG

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Area of Science:

  • Neuroscience
  • Electrophysiology

Background:

  • Single pulse electrical stimulation generates brain stimulation evoked potentials (BEPs) crucial for inferring brain connectivity.
  • Non-standardized reference electrode selection in intracranial EEG can lead to significant data interpretation errors.
  • Reference contamination distorts BEPs across all electrodes, impacting findings.

Purpose of the Study:

  • To identify and address reference contamination in BEPs from intracranial EEG.
  • To provide practical solutions for mitigating reference electrode artifacts.
  • To enhance the accuracy of brain connectivity analysis using electrical stimulation.

Main Methods:

  • Utilized intracranial EEG datasets from two subjects recorded at different institutions.
  • Developed intuitive metrics to detect reference contamination based on inter-channel similarity.
  • Implemented strategies including switching reference electrodes and post hoc re-referencing to an adjusted common average.

Main Results:

  • Both reference electrode switching and adjusted common average re-referencing effectively reduced reference contamination.
  • Metrics demonstrated increased variability in BEP latencies and durations post-mitigation.
  • Re-referencing led to increased similarity between experimental runs, indicating improved data quality.

Conclusions:

  • Clear quality checks and preprocessing are essential for accurate interpretation of stimulation data.
  • The study provides validated statistics and tools for identifying and correcting reference contamination.
  • This work enhances the reliability of brain connectivity assessments derived from electrical stimulation.