A backward encoding approach to recover subcortical auditory activity

Fabian Schmidt; Gianpaolo Demarchi; Florian Geyer; Nathan Weisz

A backward encoding approach to recover subcortical auditory activity

Fabian Schmidt, Gianpaolo Demarchi, Florian Geyer, Nathan Weisz

Publikation: Konferenzbeitrag › Poster

Abstract

Several subcortical nuclei along the auditory pathway are involved in the processing of sounds. One of the most commonly used methods of measuring the activity of these nuclei is the auditory brainstem response (ABR). Due to its low signal-to-noise ratio, ABRs have to be derived by averaging activity evoked by a high number (several thousand) of repetitions of e.g. clicks or tone bursts. To date no approach exists that can be used to non-invasively investigate both auditory brainstem activity following natural sounds (e.g. speech, music) and silent periods, for example, within selective attention tasks. For several cognitive neuroscientific questions this is a severe limitation. We propose that by training a backward encoding model to reconstruct evoked ABRs from electrophysiological data, spatial filters (channel weights) can be obtained that are tuned to auditory brainstem activity. Since these filters can be applied to any other dataset (i.e. generalized) using the same spatial coverage, this could allow for the estimation of auditory brainstem activity from any continuous sensor level data. In this study, we established a proof-of-concept that by employing a backward encoding model generated using a click stimulation rate of 30 Hz we could predict the expected ABR activity recorded via electroencephalography (EEG) from an independent measurement, using a stimulation rate of 9 Hz. By showing that the individually predicted and measured ABRs are highly correlated (r ~ 0.67), we laid the necessary foundation to use this paradigm in more naturalistic listening situations.

Originalsprache	Englisch
Publikationsstatus	Veröffentlicht - 21 Okt 2019
Veranstaltung	49th Annual Meeting of the Society for Neuroscience (SfN) - McCormick Place, Chicago, USA/Vereinigte Staaten Dauer: 19 Okt 2019 → 23 Okt 2019

Konferenz

Konferenz	49th Annual Meeting of the Society for Neuroscience (SfN)
Kurztitel	Neuroscience 2019
Land	USA/Vereinigte Staaten
Ort	Chicago
Zeitraum	19/10/19 → 23/10/19

Systematik der Wissenschaftszweige 2012

501 Psychologie

Zitieren

Schmidt, F., Demarchi, G., Geyer, F., & Weisz, N. (2019). A backward encoding approach to recover subcortical auditory activity. Postersitzung präsentiert bei 49th Annual Meeting of the Society for Neuroscience (SfN), Chicago, USA/Vereinigte Staaten.

@conference{9404a437cfec4f9baaca3b6d384ac176,

title = "A backward encoding approach to recover subcortical auditory activity",

abstract = "Several subcortical nuclei along the auditory pathway are involved in the processing of sounds. One of the most commonly used methods of measuring the activity of these nuclei is the auditory brainstem response (ABR). Due to its low signal-to-noise ratio, ABRs have to be derived by averaging activity evoked by a high number (several thousand) of repetitions of e.g. clicks or tone bursts. To date no approach exists that can be used to non-invasively investigate both auditory brainstem activity following natural sounds (e.g. speech, music) and silent periods, for example, within selective attention tasks. For several cognitive neuroscientific questions this is a severe limitation. We propose that by training a backward encoding model to reconstruct evoked ABRs from electrophysiological data, spatial filters (channel weights) can be obtained that are tuned to auditory brainstem activity. Since these filters can be applied to any other dataset (i.e. generalized) using the same spatial coverage, this could allow for the estimation of auditory brainstem activity from any continuous sensor level data. In this study, we established a proof-of-concept that by employing a backward encoding model generated using a click stimulation rate of 30 Hz we could predict the expected ABR activity recorded via electroencephalography (EEG) from an independent measurement, using a stimulation rate of 9 Hz. By showing that the individually predicted and measured ABRs are highly correlated (r ~ 0.67), we laid the necessary foundation to use this paradigm in more naturalistic listening situations.",

author = "Fabian Schmidt and Gianpaolo Demarchi and Florian Geyer and Nathan Weisz",

year = "2019",

month = "10",

day = "21",

language = "English",

note = "49th Annual Meeting of the Society for Neuroscience (SfN), Neuroscience 2019 ; Conference date: 19-10-2019 Through 23-10-2019",

}

TY - CONF

T1 - A backward encoding approach to recover subcortical auditory activity

AU - Schmidt, Fabian

AU - Demarchi, Gianpaolo

AU - Geyer, Florian

AU - Weisz, Nathan

PY - 2019/10/21

Y1 - 2019/10/21

N2 - Several subcortical nuclei along the auditory pathway are involved in the processing of sounds. One of the most commonly used methods of measuring the activity of these nuclei is the auditory brainstem response (ABR). Due to its low signal-to-noise ratio, ABRs have to be derived by averaging activity evoked by a high number (several thousand) of repetitions of e.g. clicks or tone bursts. To date no approach exists that can be used to non-invasively investigate both auditory brainstem activity following natural sounds (e.g. speech, music) and silent periods, for example, within selective attention tasks. For several cognitive neuroscientific questions this is a severe limitation. We propose that by training a backward encoding model to reconstruct evoked ABRs from electrophysiological data, spatial filters (channel weights) can be obtained that are tuned to auditory brainstem activity. Since these filters can be applied to any other dataset (i.e. generalized) using the same spatial coverage, this could allow for the estimation of auditory brainstem activity from any continuous sensor level data. In this study, we established a proof-of-concept that by employing a backward encoding model generated using a click stimulation rate of 30 Hz we could predict the expected ABR activity recorded via electroencephalography (EEG) from an independent measurement, using a stimulation rate of 9 Hz. By showing that the individually predicted and measured ABRs are highly correlated (r ~ 0.67), we laid the necessary foundation to use this paradigm in more naturalistic listening situations.

AB - Several subcortical nuclei along the auditory pathway are involved in the processing of sounds. One of the most commonly used methods of measuring the activity of these nuclei is the auditory brainstem response (ABR). Due to its low signal-to-noise ratio, ABRs have to be derived by averaging activity evoked by a high number (several thousand) of repetitions of e.g. clicks or tone bursts. To date no approach exists that can be used to non-invasively investigate both auditory brainstem activity following natural sounds (e.g. speech, music) and silent periods, for example, within selective attention tasks. For several cognitive neuroscientific questions this is a severe limitation. We propose that by training a backward encoding model to reconstruct evoked ABRs from electrophysiological data, spatial filters (channel weights) can be obtained that are tuned to auditory brainstem activity. Since these filters can be applied to any other dataset (i.e. generalized) using the same spatial coverage, this could allow for the estimation of auditory brainstem activity from any continuous sensor level data. In this study, we established a proof-of-concept that by employing a backward encoding model generated using a click stimulation rate of 30 Hz we could predict the expected ABR activity recorded via electroencephalography (EEG) from an independent measurement, using a stimulation rate of 9 Hz. By showing that the individually predicted and measured ABRs are highly correlated (r ~ 0.67), we laid the necessary foundation to use this paradigm in more naturalistic listening situations.

M3 - Poster

ER -