Computational modeling of the auditory brainstem response to continuous speech

Abstract

Objective. The auditory brainstem response can be recorded non-invasively from scalp electrodes and serves as an important clinical measure of hearing function. We have recently shown how the brainstem response at the fundamental frequency of continuous, non-repetitive speech can be measured, and have used this measure to demonstrate that the response is modulated by selective attention. However, different parts of the speech signal as well as several parts of the brainstem contribute to this response. Here we employ a computational model of the brainstem to elucidate the influence of these different factors. Approach. We developed a computational model of the auditory brainstem by combining a model of the middle and inner ear with a model of globular bushy cells in the cochlear nuclei and with a phenomenological model of the inferior colliculus. We then employed the model to investigate the neural response to continuous speech at different stages in the brainstem, following the methodology developed recently by ourselves for detecting the brainstem response to running speech from scalp recordings. We compared the simulations with recordings from healthy volunteers. Main results. We found that the auditory-nerve fibers, the cochlear nuclei and the inferior colliculus all contributed to the speech-evoked brainstem response, although the dominant contribution came from the inferior colliculus. The delay of the response corresponded to that observed in experiments. We further found that a broad range of harmonics of the fundamental frequency, up to about 8 kHz, contributed to the brainstem response. The response declined with increasing fundamental frequency, although the signal-to-noise ratio was largely unaffected. Significance. Our results suggest that the scalp-recorded brainstem response at the fundamental frequency of speech originates predominantly in the inferior colliculus. They further show that the response is shaped by a large number of higher harmonics of the fundamental frequency, reflecting highly nonlinear processing in the auditory periphery and illustrating the complexity of the response.

Neural tracking of the fundamental frequency of the voice: the effect of voice characteristics

“…For the responses to female voices, hTRF analysis indicated a peak-latency of 7-12 ms, which is similar to the latencies reported by and likely corresponds to a brainstem source (Langner and Schreiner, 1988). These results follow the well-researched notion that f0-evoked responses are predominantly generated by the inferior colliculus (Bidelman, 2015;Saiz-Alia and Reichenbach, 2020). It is important to note that these conclusions are drawn based on the limited amount of subjects who had significant backward correlations for the female narratedstories.…”

Exposing distinct subcortical components of the auditory brainstem response evoked by continuous naturalistic speech

“…For the purpose of investigating responses from different subcortical structures, we accomplished our goal of creating a stimulus paradigm that overcame some of the limitations of current methods using natural speech. Methods that do not use re-synthesized impulse-like speech generate responses characterized by a broad peak between 6–9 ms (Forte et al, 2017; Maddox and Lee, 2018), with contributions predominantly from the inferior colliculus (Saiz-Alia and Reichenbach, 2020). In contrast, for the majority of our subjects, peaky speech evoked responses with canonical morphology comprised of waves I, III, V, P 0 , N a , P a (Figure 1), reflecting neural activity from distinct stages of the auditory system from the auditory nerve to thalamus and primary auditory cortex (e.g., Picton et al, 1974).…”

“…The responses to embedded chirps elicited waves with larger mean amplitude than those to our broadband peaky speech (~0.4 versus ~0.2 μV, respectively), although a similar proportion of subjects had identifiable waves and several other factors may contribute to amplitude differences. For example, higher click rates (e.g., Burkard et al, 1990; Burkard and Hecox, 1983; Chiappa et al, 1979; Don et al, 1977; Jiang et al, 2009) and higher fundamental frequencies (Maddox and Lee, 2018; Saiz-Alía et al, 2019; Saiz-Alia and Reichenbach, 2020) reduce the brainstem response amplitude, and dynamic changes in rate may create interactions across neural populations that lead to smaller amplitudes. Our stimuli kept the dynamic changes in pitch across all frequencies (instead of alternate octave bands of chirps and speech) and created impulses at every glottal pulse, with an average pitch of ~115 Hz and ~198 Hz for the male and female narrators respectively.…”

“…One such technique extracts the fundamental waveform from the speech stimulus and finds the envelope of the cross-correlation between that waveform and the recorded EEG data (Forte et al, 2017). The response has an average peak time of about 9 ms, with contributions primarily from the inferior colliculus (Saiz-Alia and Reichenbach, 2020). A second technique considers the rectified broadband speech waveform as the input to a linear system and the EEG data as the output, and uses deconvolution to compute the ABR waveform as the impulse response of the system (Maddox and Lee, 2018).…”

Enhanced neural tracking of the fundamental frequency of the voice

“…Therefore, the second strategy focussed on auditory processing higherup the auditory pathway. Auditory models of brainstem processing already exist (Nelson and Carney, 2004;Verhulst et al, 2018;Carney et al, 2015;Saiz-Alia and Reichenbach, 2020), but we chose to design a new model that is simple, yet highly effective for our purpose, by focussing on the spectrum of the response. It is known that the frequency limit for phase-locking decreases along the auditory pathway, causing cortical sources to contribute more strongly for stimuli with low f0.…”