A Model-Based Approach for Separating the Cochlear Microphonic from the Auditory Nerve Neurophonic in the Ongoing Response Using Electrocochleography
Electrocochleography (ECochG) is a potential clinically valuable technique for predicting speech perception outcomes in cochlear implant (CI) recipients, among other uses. Current analysis is limited by an inability to quantify hair cell and neural contributions which are mixed in the ongoing part o...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fnins.2017.00592/full |
| _version_ | 1817976412502491136 |
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| author | Tatyana E. Fontenot Christopher K. Giardina Douglas C. Fitzpatrick Douglas C. Fitzpatrick |
| author_facet | Tatyana E. Fontenot Christopher K. Giardina Douglas C. Fitzpatrick Douglas C. Fitzpatrick |
| author_sort | Tatyana E. Fontenot |
| collection | DOAJ |
| description | Electrocochleography (ECochG) is a potential clinically valuable technique for predicting speech perception outcomes in cochlear implant (CI) recipients, among other uses. Current analysis is limited by an inability to quantify hair cell and neural contributions which are mixed in the ongoing part of the response to low frequency tones. Here, we used a model based on source properties to account for recorded waveform shapes and to separate the combined signal into its components. The model for the cochlear microphonic (CM) was a sinusoid with parameters for independent saturation of the peaks and the troughs of the responses. The model for the auditory nerve neurophonic (ANN) was the convolution of a unit potential and population cycle histogram with a parameter for spread of excitation. Phases of the ANN and CM were additional parameters. The average cycle from the ongoing response was the input, and adaptive fitting identified CM and ANN parameters that best reproduced the waveform shape. Test datasets were responses recorded from the round windows of CI recipients, from the round window of gerbils before and after application of neurotoxins, and with simulated signals where each parameter could be manipulated in isolation. Waveforms recorded from 284 CI recipients had a variety of morphologies that the model fit with an average r2 of 0.97 ± 0.058 (standard deviation). With simulated signals, small systematic differences between outputs and inputs were seen with some variable combinations, but in general there were limited interactions among the parameters. In gerbils, the CM reported was relatively unaffected by the neurotoxins. In contrast, the ANN was strongly reduced and the reduction was limited to frequencies of 1,000 Hz and lower, consistent with the range of strong neural phase-locking. Across human CI subjects, the ANN contribution was variable, ranging from nearly none to larger than the CM. Development of this model could provide a means to isolate hair cell and neural activity that are mixed in the ongoing response to low-frequency tones. This tool can help characterize the residual physiology across CI subjects, and can be useful in other clinical settings where a description of the cochlear physiology is desirable. |
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| id | doaj.art-a0868f4d88d54e3984ab7219e81a2764 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-13T22:03:11Z |
| publishDate | 2017-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-a0868f4d88d54e3984ab7219e81a27642022-12-22T02:28:02ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2017-10-011110.3389/fnins.2017.00592262795A Model-Based Approach for Separating the Cochlear Microphonic from the Auditory Nerve Neurophonic in the Ongoing Response Using ElectrocochleographyTatyana E. Fontenot0Christopher K. Giardina1Douglas C. Fitzpatrick2Douglas C. Fitzpatrick3Otolaryngology-Head and Neck Surgery, University of North Carolina, Chapel Hill, NC, United StatesSchool of Medicine, University of North Carolina, Chapel Hill, NC, United StatesOtolaryngology-Head and Neck Surgery, University of North Carolina, Chapel Hill, NC, United StatesSchool of Medicine, University of North Carolina, Chapel Hill, NC, United StatesElectrocochleography (ECochG) is a potential clinically valuable technique for predicting speech perception outcomes in cochlear implant (CI) recipients, among other uses. Current analysis is limited by an inability to quantify hair cell and neural contributions which are mixed in the ongoing part of the response to low frequency tones. Here, we used a model based on source properties to account for recorded waveform shapes and to separate the combined signal into its components. The model for the cochlear microphonic (CM) was a sinusoid with parameters for independent saturation of the peaks and the troughs of the responses. The model for the auditory nerve neurophonic (ANN) was the convolution of a unit potential and population cycle histogram with a parameter for spread of excitation. Phases of the ANN and CM were additional parameters. The average cycle from the ongoing response was the input, and adaptive fitting identified CM and ANN parameters that best reproduced the waveform shape. Test datasets were responses recorded from the round windows of CI recipients, from the round window of gerbils before and after application of neurotoxins, and with simulated signals where each parameter could be manipulated in isolation. Waveforms recorded from 284 CI recipients had a variety of morphologies that the model fit with an average r2 of 0.97 ± 0.058 (standard deviation). With simulated signals, small systematic differences between outputs and inputs were seen with some variable combinations, but in general there were limited interactions among the parameters. In gerbils, the CM reported was relatively unaffected by the neurotoxins. In contrast, the ANN was strongly reduced and the reduction was limited to frequencies of 1,000 Hz and lower, consistent with the range of strong neural phase-locking. Across human CI subjects, the ANN contribution was variable, ranging from nearly none to larger than the CM. Development of this model could provide a means to isolate hair cell and neural activity that are mixed in the ongoing response to low-frequency tones. This tool can help characterize the residual physiology across CI subjects, and can be useful in other clinical settings where a description of the cochlear physiology is desirable.http://journal.frontiersin.org/article/10.3389/fnins.2017.00592/fullcochlear physiologyelectrophysiologyauditory hair cellsauditory nerveauditory nerve modelcomputational modeling |
| spellingShingle | Tatyana E. Fontenot Christopher K. Giardina Douglas C. Fitzpatrick Douglas C. Fitzpatrick A Model-Based Approach for Separating the Cochlear Microphonic from the Auditory Nerve Neurophonic in the Ongoing Response Using Electrocochleography Frontiers in Neuroscience cochlear physiology electrophysiology auditory hair cells auditory nerve auditory nerve model computational modeling |
| title | A Model-Based Approach for Separating the Cochlear Microphonic from the Auditory Nerve Neurophonic in the Ongoing Response Using Electrocochleography |
| title_full | A Model-Based Approach for Separating the Cochlear Microphonic from the Auditory Nerve Neurophonic in the Ongoing Response Using Electrocochleography |
| title_fullStr | A Model-Based Approach for Separating the Cochlear Microphonic from the Auditory Nerve Neurophonic in the Ongoing Response Using Electrocochleography |
| title_full_unstemmed | A Model-Based Approach for Separating the Cochlear Microphonic from the Auditory Nerve Neurophonic in the Ongoing Response Using Electrocochleography |
| title_short | A Model-Based Approach for Separating the Cochlear Microphonic from the Auditory Nerve Neurophonic in the Ongoing Response Using Electrocochleography |
| title_sort | model based approach for separating the cochlear microphonic from the auditory nerve neurophonic in the ongoing response using electrocochleography |
| topic | cochlear physiology electrophysiology auditory hair cells auditory nerve auditory nerve model computational modeling |
| url | http://journal.frontiersin.org/article/10.3389/fnins.2017.00592/full |
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