Into the deep
–
Capturing activity along the auditory nerve
Masterarbeit
An der Naturwissenschaftlichen Fakultät
der Paris-Lodron-Universität Salzburg
im Sommersemester 2018
Eingereicht von
Fabian Schmidt
Matrikelnummer: 1222361
Gutachter
Univ.-Prof. Dr. Nathan Weisz
Fachbereich
Psychologie
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Abstract
Early auditory evoked potentials occur within the first ten milliseconds after acoustic stimulation. The recording of these potentials usually consists of five to seven vertex positive waves, with Wave I & II being related to activity in the auditory nerve and the cochlear nucleus. The most common used methods of measuring these electrical signals spreading through the auditory pathway are Electrocochleography (ECochG) and the Auditory
Brainstem Response (ABR). As the recorded signals are typically weak in amplitude, averaging over a lot of trials, elicited by simple stimuli such as clicks or tone bursts, is required to obtain a reliable response. The large amount of repetitive trials presents a challenge to researchers trying to investigate auditory nerve activity during a more natural stimulation (e.g. listening to running speech). The present study shows, that by combining
ECochG, ABR and magnetoencephalography (MEG) using a forward/backward encoding modelling approach, a “pipeline” to the auditory nerve can be built. Results suggest that activity presumably generated by the auditory nerve, can be captured in the MEG.
Furthermore, it was shown that early auditory evoked potentials can be reconstructed and used to create a prediction model for the activity along the auditory pathway. This opens the gates to further investigate auditory nerve activity under more natural circumstances (e.g. listening to running speech).
keywords: auditory nerve; auditory pathway; cochlear nucleus; early auditory evoked potentials; electrocochleography; magnetoencephalography; auditory brainstem response
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Zusammenfassung
Frühe auditorische evozierte Potentiale ereignen sich in den ersten zehn
Millisekunden nach einem akustischen Reiz. Das gemessene Signal besteht in der Regel aus fünf bis sieben Vertex positiven Wellen. Die ersten beiden Wellen (I/II) sind assoziiert mit Aktivität im Hörnerv und den Schneckenkernen. Die am häufigsten verwendeten
Methoden zur Messung dieser elektrischen Signale, auf ihrem Weg durch das auditive
System, sind Elektrocochleographie (EcochG) und die Hirnstammaudiometrie (BERA). Da die aufgezeichneten Signale typischerweise eine geringe Amplitude haben, ist eine
Mittelwertbildung über viele „Trials“ (ausgelöst durch Klicks oder „tone bursts“) erforderlich, um ein deutliches Signal zu erkennen. Dies begrenzt die Fähigkeiten von Forschern, die versuchen, die Aktivität des Hörnervs unter einer natürlichen Stimulation (z. B. mit laufender
Sprache) zu untersuchen. Die vorliegende Studie zeigt, dass durch Kombination von
ECochG, BERA und Magnetoenzephalographie (MEG) mit einem Vorwärts/Rückwärts- codierungsmodells eine "Pipeline" zum Hörnerv aufgebaut werden kann. Die Ergebnisse dieser Studie deuten darauf hin, dass die Aktivität, die vermutlich vom Hörnerv generiert wird, im MEG erfasst werden kann. Darüber hinaus konnte gezeigt werden, dass frühe auditorisch evozierte Potentiale rekonstruiert und genutzt werden können, um ein
Vorhersagemodell für die Aktivität entlang des Hörnervs zu erstellen. Dies öffnet die Tore, um die Aktivität des Hörnervs während einer natürlicheren Stimulation (z. B. mit laufender
Sprache) weiter zu untersuchen.
Stichworte: Hörnerv; auditives System; Schneckenkerne; frühe auditorisch evozierte
Potentiale; Elektrocochleographie; Magnetenenzephalographie, Hirnstammaudiometrie
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Introduction
After a sound hits the eardrum, within 10 milliseconds, a series of sub-cortical potentials can be measured. The recording of these early auditory evoked potentials usually consists of five to seven vertex positive waves, denoted in roman numerals ranging from I to VII (Jewett & Williston, 1971). These waves are typically analyzed by clinicians to provide diagnostic value in patients with hearing disorders. In practice, when measuring these potentials, clinicians are usually only able to orient themselves on differences in latency and amplitude of the individual peaks, generated by clicks or tone bursts (Eggermont, 2017;
Ferraro, 2010). However, even among normal hearing subjects’ distinct peaks (especially for Wave II, IV and VI) remain sometimes undetectable (Levine et al., 1993). The absence of these peaks can be partially explained by the generally low amplitude of the early evoked potentials (Jewett & Williston, 1971; Zhang, McAllister, Scotney, McClean, & Houston,
2006). This makes it usually necessary to average over thousands of trials to extract the activity from the background electroencephalographic signal (Jewett & Williston, 1971;
Zhang et al., 2006).
In the last four decades a vast amount of research ranging from animal models (Melcher &
Kiang, 1996) to direct measurements of the auditory nerve and selected brainstem areas
(Hashimoto, Ishiyama, Yoshimoto, & Nemoto, 1981; Møller & Jannetta, 1985; Møller, Jho,
Yokota & Jannetta, 1995; Rattay & Danner, 2014; Tait, Miller, Cycowicz & Sohmer, 1987) was conducted to identify the generators of the early evoked potentials. Human intracranial nearfield recordings provide convincing evidence, that Wave I and Wave II are generated by the auditory nerve (Møller et al., 1995) with a possible contribution from the cochlear nucleus for wave II (Hashimoto et al., 1981). Estimating the origins of Wave III and IV poses a bigger challenge. It is not yet clear whether Wave III is produced by the ipsilateral cochlear nucleus or the superior olivary complex and if Wave IV originates in the cochlear nucleus and/or in
INTO THE DEEP 5 the nuclei of the superior olivary complex (Møller & Jannetta, 1985; Møller et al., 1995).
Intracranial recordings show that Wave V, the most prominent wave detected in humans, is primarily attributed to activity in the lateral leminiscus and the inferior colliculi (Møller et al.
1995). For Wave VI & VII only, sparse evidence exists in terms of their generation sites, as they usually only appear in few subjects. According to Brugge et al. (2009) the earliest cortical responses to acoustic stimulation occur at 9-12ms. One could hypothesize that with a latency of 9-10ms Wave VII could thus be generated by the primary auditory cortex. Here it should be noted, that all investigators focused on the identification of the evoked responses in humans used invasive techniques, making them not applicable in most clinical settings (e.g. hearing screenings).
Due to their distance to the sensors it has been disputable, if signals originating in deep sub- cortical structures such as the lateral leminiscus (Wave V) can be detected by using magnetoencephalography (MEG). So far, only Parkkonen, Fujiki & Mäkelä (2009) managed to estimate the source locations of Wave I, II, and V using MEG. This is attributed to the difficulties in recording and modeling these signals, without the use of advanced analysis techniques (Simpson & Prendergast, 2013). In their study, Parkonnen et al. (2009) used only surface-EEG electrodes as a reference signal to the activity measured by the MEG. Whilst getting a good resolution for responses related to late brainstem activity (Wave V), their design was not optimized for the measurement of the early evoked activity originating in the auditory nerve and the cochlear nucleus (Wave I & II).
Electrocochleography (ECochG) is one of the most common used methods by audiologists to measure electrical activity produced in the cochlea after acoustic stimulation. ECochG enables the investigators to follow the electrical signals generated in the cochlea as they spread through the auditory pathway across the brainstem into the auditory cortex.
Clinicians usually apply ECochG for the diagnosis of Ménière’s disease by investigating
INTO THE DEEP 6 abnormalities of the summating potential (SP) and the compound action potential (CAP)
(Eggermont, 2017). The CAP is corresponding to the first wave captured when measuring the early auditory evoked potentials (Minaya & Etcherson, 2015). In general, it can be differentiated between two major ways of measuring ECochG (extra-tympanic (ET) and trans-tympanic (TT) ECochG (Ferraro, 2010). Whilst ET-ECochG is non-invasively recorded from the external ear canal close to the tympanic membrane, TT-ECochG involves piercing a needle electrode through the tympanic membrane measuring the activity at the promontory close to the round window (Bonucci & Hyppolito, 2009). Both methods do not show different results regarding the latency of the measured peaks (Eggermont, 2017). However, due to the proximity of the electrode to the cochlea, the amplitudes of the signals are approximately four times larger in TT-ECochG compared to ET-ECochG (Ferraro & Durrant, 2006). As a downside, due to its invasive nature, TT-ECochG always involves a clinician to be present for electrode placements and subjects willing getting their tympanic membrane pierced, making it in general harder to implement in a non-clinical setting (Bonucci & Hyppolito,
2009).
Another common method applied by audiologists for the measurements of early acoustic activity is the auditory brainstem response (ABR). The ABR is a far-field recording typically measured by placing a single electrode on the scalp at either FpZ or Cz (Crumley, 2011). As no special in-ear electrodes are needed to measure the ABR it is usually easier to implement then ECochG. Nonetheless, due to the proximity of the electrodes to the cochlea ECochG produces higher amplitudes for the first wave or CAP associated with activity in the auditory nerve compared to the ABR (Minaya & Etcherson, 2015).
Electrical signals also have magnetic properties, thus making it possible to measure a magnetic auditory brainstem response (mABR). Parkonnen et al. (2009) achieved this feat by computing the root-mean-squared signal across all magnetometer channels. The
INTO THE DEEP 7 resulting signal had comparable properties to the ABR (Parkonnen et al., 2009).
Here, click evoked early auditory potentials were measured with the ABR, ET-ECochG and
MEG at the same time. Thereupon, the information gathered by the ECochG and the ABR was taken as a reference signal to extract the early auditory activity from the MEG. This was done to construct a temporal response function and a backward encoding model. The temporal response function was used in combination with the MEG data to create a spatial filter to directly access the sub-cortical structures associated with early auditory processing, while the backward encoding model was used to reconstruct the signal from the MEG data.
This backward encoding model may have important applications in both auditory neuroscience and clinical settings alike. As mentioned above auditory nerve activity is usually measured under highly artificial circumstances (by averaging over thousands of simple trials). An encoding model for the auditory nerve could be helpful in reconstructing auditory nerve activity under more natural circumstances (e.g. listening to running speech).
This opens the gates to study the early auditory evoked potentials and auditory nerve activity during a more natural stimulation.
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Hypotheses
I presume that ECochG will be the best method to measure Wave I & II due to the proximity of the electrodes to the cochlea, compared to the other methods. Measurement quality is here defined as a strong deviation from the baseline at the respective time course.
The method that fares the best in measuring Wave I & II, will then be used to construct a spatial filter and an encoding model. Using said filter, I expect to capture an activation in the area around the auditory nerve and the cochlear nucleus for Wave I & II.
In the future, the backwards encoding model should be used to reconstruct auditory nerve activity measured under different conditions (e.g. listening to running speech). Here, as a proof of principal, I am testing the encoding model on the same dataset to reconstruct the signal of the early auditory evoked activity. A high correlation between the original and the reconstructed activity is expected.
Materials & Methods
Participants
The data, collected of 18 healthy volunteers (13 males, M = 26.17 years; SD = 4.09 years) without psychiatric or neurological disorders, was analyzed for this master thesis. The experimental protocol was approved by the ethics committee of the University of Salzburg and all participants gave written informed consent before the beginning of the experiment.
This study was part of a larger experimental protocol spanning ~ 2 hours including participant preparation. Thus, as a reward for taking part in this study, participants could choose between 20 Euros and two hours credit for being a test subject in a psychological experiment.
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Stimuli and Procedure
Participants listened to a total of 10000 rarefaction and condensation clicks at a sound pressure level of 60dB. Stimuli were presented binaurally at a rate of 30Hz and each stimulus had a duration of 80μs. Auditory triggers were recorded with the SOUNDPixx system (Vpixx technologies, Canada). The trigger to stimulation delay took exactly 16.5ms and was measured with an oscilloscope (Rigol DS 1074Z). This delay was compensated by shifting the time axis during the data analysis accordingly. The measurement took exactly 5 minutes and 48 seconds. The experimental procedure was programmed with the open- source Psychophysics Toolbox 3 (Brainard, 1997) in Matlab.
Data acquisition
Cerebral activity was measured using a 306-channel whole head MEG system from
Neuromag, Elekta (Finland) with a sampling rate of 10000 Hz. The system consists of 204 planar gradiometers and 102 magnetometers. Before entering the magnetically shielded room (AK3B, Vakuumschmelze, Hanau, Germany), the head shape of each participant was acquired with about 300 digitized points on the scalp, including fiducials (nasion, left and right pre-auricular points) with a Polhemus Fastrak system (Polhemus, Vermont, USA).
ECochG was recorded binaurally with a unipolar setup by means of extra-tympanic (ET)
TIPtrode electrodes (TTE25, 13mm). Nuprep® Electrode gel was applied over the gold-foil of each TIPtrode before insertion to ensure low impedance values. The auditory brainstem response (ABR) was measured with a single electrode located on FpZ based on the elec- trode placement of the international 10–20-System. In order to account for the different measurement methods used in our approach, a ground was placed on the forehead at mid-
INTO THE DEEP 10 line and a reference on the neck of the participants. Artifacts caused by eye-blinks and heart- beats were identified by recording vertical/horizontal eye movements and electrocardio- graphic activity.
Data analysis
The acquired data was Maxwell filtered by applying the Signal Space Separation
(SSS) algorithm of the MaxFilterTM software (Elekta Oy, Finland), to suppress external disturbing magnetic interference. The filtered data was then further analyzed using the open- source toolbox FieldTrip (Oostenveld, Fries, Maris, & Schoffelen, 2011) embedded in Matlab
(Mathworks, Natick, USA). A high-pass butterworth filter (8th order) at 150Hz and a low-pass butterworth filter (8th order) at 2500Hz were applied to the continuous data. Residual artifacts were rejected by visual inspection based on the variance, kurtosis and z-values of individual trials (clicks). The total MEG activity – or the so-called mABR (Parkonnen et al. 2009) – was assessed using the global mean field power (GMFP) as described by Esser, Huber,
Massimini, Peterson, Ferrarelli & Tononi (2006). The data collected by the individual
ECochG channels and the (m)ABR were averaged, z-transformed and used to construct a temporal response function and a backward encoding model utilizing the mTRF toolbox
(Crosse, Di Liberto, Bednar & Lalor; 2016). In detail, this was achieved by performing a ridge regression on the aforementioned data channels (ECochG & (m)ABR) and the averaged and z-transformed MEG data to solve for their linear mapping function. Mapping was performed in a forward direction to compute a temporal response function and in a backward direction for the backward encoding model used for signal reconstruction.
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Source analysis
The structural MR-images of 7 participants were acquired by a Siemens
MAGNETOM TIM Trio 3-Tesla MRI with 12-channel and 32-channel head coils. The individual brains were brought into a common space by co-registering the individual brains, based on the three anatomical landmarks (nasion, left and right preauricular points), with a standard brain from the Montreal Neurological Institute (MNI, Montreal, Canada). For the remaining participants without structural MR image formerly computed single-shell head models (Nolte, 2003) warped to the individual head shape were used instead. A grid with 1 cm resolution based on a template brain was morphed into the brain volume of each participant. A mask consisting of cortical and sub-cortical voxels (14196 voxels) was created and used for source reconstruction.
Figure 1: A mask covering both cortical and sub-cortical areas plotted on an MNI volume for visualization.
Linearly constrained minimum variance (LCMV) beamformer analysis (Veen, Drongelen,
Yuchtman, & Suzuki, 1997) was applied to project the data from sensor- into source-level.
Spatial filters were created for each subject individually by multiplying the weights of the
INTO THE DEEP 12 forward mapped temporal response function with the spatial filters of the beamformer. The data was then baseline corrected and averaged across participants.
Wave analysis
The waves of the early auditory responses are typically identified by visual inspection and marked accordingly. Here, the Matlab function findpeaks, typically used to find local maxima, was applied to get a researcher independent wave identification. Only peaks with a z-score above 0.5 were considered identifiable waves and used for further analysis. Labeling the waves in I-VII was decided based on the time course of each peak.
Dependent-samples t-tests were then computed separately for the left ECochG, the right
ECochG, the ABR and the mABR by comparing the earlier identified peaks, to an averaged baseline window before stimulus onset.
Results
ECochG, ABR & mABR
Averaged and filtered ECochG and (m)ABR data revealed a sequence of responses peaking within the first 10ms after acoustic stimulation. Figure 2 shows a display of these peaks averaged across all subjects.
Three peaks were extracted from the signal of the left ECochG at 2.2ms (Wave I), 4ms
(Wave III) and 7.3ms (Wave V). Dependent sample Bonferroni corrected t-tests revealed, that only Wave III and Wave V differed significantly from the Baseline (p<0.005).
For the right EcochG three peaks were extracted at 2.4ms (Wave I), 4.3ms (Wave III) and
7.3ms (Wave V) and labeled accordingly. Here none of the waves differed significantly from the baseline.
The ABR revealed two peaks at 4.1ms (Wave III) and 7.3ms (Wave V). Here only the second peak at 7.3ms revealed a significant difference from the baseline (p<0.005).
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The mABR shows a series of four peaks at 2ms (Wave I), 2.6ms (Wave II), 5.6ms (Wave IV) and 7.3ms (Wave V). All peaks varied significantly from the baseline (p<0.005).
Figure 2: Comparison of ECochG and (m)ABR across the first ten milliseconds after acoustic stimulation.
Diamonds indicate Bonferroni corrected statistically significant differences at the respective timepoint compared to the individual baseline. Shaded error bars are displaying the standard error.
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Sources of Wave I & II
Only in the mABR Wave I & II were present, thus this method was used for the construction of the spatial filter. This was achieved by combining the weights of the temporal response function computed on the mABR with the spatial filters of the beamformer. Figure
3 shows the mABR with a localization of Wave I and II.
Figure 3: Source reconstruction at the timepoints of Wave I & II reveals an activation in the area ranging from the auditory nerve to the cochlear nucleus, only activity above an 80% cutoff is displayed.
At the timepoint of Wave I at 2ms the activity reconstructed from source shows an activation on the left hemisphere along the area of the auditory nerve (identified by visual inspection).
Furthermore, an activation on the right hemisphere around the area close to the right ear can be noted.
For Wave II at 2.6ms an even more pronounced and widespread activation on the left hemisphere compared to Wave I can be seen. Encapsulating an area stretching from the auditory nerve to the cochlear nucleus (identified by visual inspection).
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Reconstruction of the mABR
Finally, the early auditory evoked activity measured by the mABR was reconstructed from the MEG signal. This was done as a proof of principal, because the backward encoding model is to be used in the future to reconstruct and predict activity along the auditory pathway under a different paradigm.
The reconstruction was achieved by performing a ridge regression on the mABR mapped in a backward direction (from MEG towards mABR). This encoding model was used to create a prediction model on the same dataset.
Figure 4 shows the reconstruction of the waveform of the mABR compared to the original signal. It can be noted, that the reconstruction shows a close to perfect fit between the actually measured and the reconstructed mABR (r(99) = 0.996, p<0.001).
Figure 4: Reconstruction of the mABR by performing a convolution of the backward mapped model. The model was trained and tested on data generated by a 30Hz click stimulation.
Figure 5 displays the prediction of the model across time and channels. The highest values are corresponding to the time course of Wave I and II over all channels and timepoints.
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Figure 5: Prediction of the mABR across time and channels. The model was trained and tested on data generated by a 30Hz click stimulation.
Discussion
The goal of this study was to capture activity along the auditory nerve, by extracting this information from Wave I & II of the early auditory evoked potentials. This feat was accomplished utilizing the mABR, as this method was the only approach, that produced a clear Wave I & II during our experiment. Furthermore, using the backwards encoding model on the same dataset to reconstruct the mABR showed a high correlation between both signals, making a compelling case, that an application of the encoding model on a new dataset might deliver a promising outcome.
ECochG, ABR & mABR
The results of the present study show that Wave I & II were only distinguishable when measured with mABR. In left and right ECochG Wave I appeared but showed no
INTO THE DEEP 17 significant difference from the baseline. In the ABR the earliest detectable wave was Wave
IV.
In terms of ECochG these results are counterintuitive, in the sense that the ECochG electrodes are located closer to the auditory nerve then the MEG-sensors and should thus be more reliable in detecting Wave I & II. A possible explanation lies in the method used to capture the electrocochleographic activity in this experiment. As mentioned above, there are generally two different measurement approaches for ECochG (ET- and TT-ECochG).
However, there are also slightly different approaches for measuring ET-ECochG. Here,
TIPtrode electrodes were used, to measure the early potentials from the ear canal. Another more invasive, yet still extra-tympanic approach used by audiologists are tymptrodes (TM-
ECochG) (Ferraro, 2010). In TM-ECochG a wire covered by a silicon tubing with a cotton tip is inserted in the ear canal and pushed forward until it reaches the tympanic membrane.
When applying ET-ECochG in clinical measurements, TM electrodes are preferred by audiologists, as this measurement approach provides an amplitude approximately twice as high compared to recordings with TIPtrodes (Ferraro, 2010). However, due to its slightly more invasive nature, the use of a tymptrode requires more practice (to avoid damaging the eardrum of the subject). This made it impractical for the current experiment, as no trained personnel was available, but this approach should be kept in consideration for future experiments.
Another issue influencing the general detectability of Wave I and II is the sound pressure level of the stimulation. Eggermont (2017) described that with a declining sound pressure level also the compound action potential associated with Wave I decreased. Additionally, changes in sound pressure level also influence the latencies of the peaks. Exemplary for
Wave V, latency differences between 6ms (90dB SPL) and 12ms (20dB SPL) when
INTO THE DEEP 18 stimulating with tone bursts at 4kHz can be noticed (Lewis, Kopun, Neely, Schmid & Gorga,
2015).
In this experiment clicks were administered at a sound pressure level of 60dB, which was with our hardware as close as we could get to the clinical standard of 70 to 90 dB nHL (Hall,
2007). A higher sound pressure level might improve wave detectability and should be considered for future experiments.
Another issue likely influencing the distinctiveness of Wave I and II in our data can be found in digital high-pass filtering, as according to Tabachnik & Toscano (2016) high-pass filtering distorts the signal of the early auditory evoked potentials. However, digital high-pass filtering can – if applied reasonably – increase signal to noise ratio (Widman et al. 2015). Regarding the filtering of early auditory evoked potentials, one can find a diverse set of digital high- pass filters for ABR and ECochG in the literature. The typical filters for ECochG are ranging between 3Hz and 300Hz (Durrant & Ferraro, 1991; Gibson, 2017; Hall, 2007). For the ABR filters ranging between 0.1Hz to 200Hz can be found in the literature (Mason, 1984; Suzuki
& Horiuchi, 1977; Tabachnik & Toscano, 2016). On a randomly selected subject a set of high-pass filters ranging between 0.1Hz and 500Hz were applied, to find the best option for this dataset (displayed exemplarily for the ABR electrode in Figure 6). Here, the filters showed the best separation between signal and noise when high-pass filtering was applied at 150Hz. This setting was then used for this experiment. However, as this was just based on a single subject, it should be reaffirmed on a group level for validation.
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Figure 6: Display of different high-pass filters on a randomly selected single subject ranging from 0.1Hz to
500Hz for the ABR. The high-pass filter settings used in this experiment (150Hz) is marked in bright red.
Sources of Wave I & II
An activation in the area between the auditory nerve and the brainstem at the time course of Wave I & II was reconstructed from source. However, some adjustments are necessary to make a valid statement about the activation of selective subcortical structures.
Here the detection of the subcortical structures was done by visual inspection. This could be improved in the future by developing an atlas covering the structures along the auditory pathway. In this context, also the construction of a more fine-grained grid covering the area of the auditory pathway would be beneficial.
Reconstruction of the mABR
Furthermore, the mABR was reconstructed from the MEG signal. The reconstructed signal was highly correlated to the original mABR. However, this reconstruction should be
INTO THE DEEP 20 interpreted with care, as it can only serve as a general proof of principal. It should be reaffirmed on a different dataset, if this filter can really generate a reliable outcome usable to reconstruct early auditory evoked potentials in future experiments. First results of a signal reconstruction based on a training dataset with a stimulation of 30Hz clicks, tested on a dataset with a 10Hz stimulation already gives a promising outlook (Figure 7), as the original and reconstructed signals are highly correlated (r(99) = 0.672, p<0.001).
Figure 7: Reconstruction of the mABR (N = 1) by performing a convolution of the backward mapped model trained on data generated by a 30Hz click stimulation and tested on 10Hz.
This opens the gates to use this encoding model for the reconstruction of auditory nerve activity measured during a more natural stimulation (e.g. listening to running speech). This may be helpful in answering pending questions in the domain of auditory neuroscience, such as how early attentional modulation constitutes. Recently, Forte, Etard, & Reichenbach
(2017) demonstrated a modulation of brainstem activity by attention after acoustic stimulation. They achieved this by measuring the ABR to running speech. Using the
INTO THE DEEP 21 encoding model created in this experiment, a further investigation of these findings incorporating areas even earlier in the auditory pathway could be achieved.
Conclusion
Early auditory evoked potentials are measurable with MEG in both spatial and temporal domains. Especially the activity of the auditory nerve stands out of the MEG data.
Furthermore, it was shown that early auditory evoked potentials can be reconstructed from the MEG data, giving a promising outlook for a reconstruction of auditory nerve activity measured under a different paradigm. However, more time should be administered to improve the measurement approaches and filter settings used for the recording of the early auditory evoked potentials to enhance future measurements.
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