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A backward encoding approach to recover subcortical auditory activity.

Fabian Schmidt1, Gianpaolo Demarchi1, Florian Geyer1

  • 1Centre for Cognitive Neuroscience and Department of Psychology, University of Salzburg, Austria.

Neuroimage
|May 23, 2020
PubMed
Summary

Backward encoding models can reconstruct auditory brainstem responses (ABRs) from electrophysiological data. This method allows for the estimation of ABRs in natural listening situations, overcoming limitations of traditional averaging techniques.

Keywords:
Auditory brainstem responseBackward modelingElectroencephalographyMagnetoencephalographySignal reconstruction

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Area of Science:

  • Auditory Neuroscience
  • Computational Neuroscience
  • Electrophysiology

Background:

  • Subcortical auditory nuclei process sound, with Auditory Brainstem Response (ABR) measuring their activity.
  • Traditional ABR requires averaging thousands of artificial sounds due to low signal-to-noise ratio, limiting natural sound analysis.
  • Current methods restrict auditory cognitive neuroscience studies to cortical processes, neglecting subcortical contributions in complex listening.

Purpose of the Study:

  • To propose and validate a novel method using backward encoding models to reconstruct evoked ABRs from high-density electrophysiological data.
  • To enable the estimation of auditory brainstem activity from continuous sensor-level data, applicable to natural listening scenarios.
  • To establish a proof-of-concept for individualized spatial filters tuned to auditory brainstem activity.

Main Methods:

  • Trained individualized backward encoding models to reconstruct ABRs from electrophysiological data.
  • Utilized models trained with a 30 Hz click stimulation rate to predict ABR activity from an independent 9 Hz stimulation rate measurement.
  • Applied trained models to datasets with varying numbers of trials to assess stability under low signal-to-noise conditions.

Main Results:

  • Individually predicted and measured ABRs showed high correlation (r ≈ 0.7).
  • Predictions remained stable even with a low number of trials, indicating robustness in unfavorable signal-to-noise ratios.
  • Demonstrated the generalizability of trained spatial filters across different stimulation rates and datasets.

Conclusions:

  • Backward encoding models offer a viable method for reconstructing ABRs, overcoming the limitations of traditional averaging.
  • This approach facilitates the study of auditory brainstem activity in naturalistic listening conditions, such as speech and music.
  • The findings lay the groundwork for applying this technique in advanced auditory cognitive neuroscience research.