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Assessing the Electrode-Neuron Interface with the Electrically Evoked Compound Action Potential, Electrode Position,

Lindsay DeVries1, Rachel Scheperle2, Julie Arenberg Bierer3

  • 1Department of Speech and Hearing Sciences, University of Washington, 4131 15th Ave NE, Seattle, WA, 98105, USA. lindsdev@uw.edu.

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Summary

Electrically evoked compound action potentials (ECAPs) can objectively assess cochlear implant channel function. ECAP measures, combined with electrode position, correlate with speech perception, potentially improving outcomes.

Keywords:
cochlear implantselectrically evoked compound action potentialelectrode configurationimagingpsychophysics

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Audiology

Background:

  • Speech perception in cochlear implant (CI) users varies due to electrode efficacy in stimulating the auditory nerve.
  • Understanding the electrode-neuron interface is crucial for optimizing CI performance.

Purpose of the Study:

  • To evaluate the electrically evoked compound action potential (ECAP) as a site-specific measure of the electrode-neuron interface.
  • To investigate the influence of electrode-to-modiolus distance and neural density on CI channel function and speech perception.

Main Methods:

  • Assessed CI channel quality using ECAP, CT-based electrode position, and behavioral thresholds in 10 adult CI users.
  • Measured ECAP channel interaction functions (CIF) and peak amplitudes.
  • Correlated ECAP measures with electrode-modiolus distance, scalar location, and speech perception scores.

Main Results:

  • ECAP CIF width positively correlated with electrode-to-modiolus distance and scalar position.
  • ECAP peak amplitude negatively correlated with behavioral thresholds.
  • Higher speech perception scores were associated with lower behavioral thresholds and larger ECAP amplitudes.

Conclusions:

  • ECAP shows potential as an objective clinical tool for assessing individual CI channels.
  • ECAP measurements, considering peripheral factors, may help predict and improve speech perception outcomes in CI users.