Neural generators of the frequency-following response elicited to stimuli of low and high frequency: A magnetoencephalographic (MEG) study

Rupp

Llanos

et al. 2021

eNeuro

Section: Cortical Ffrs Emerge In the Thalamo-recipient Layers Of The Primary Auditory Cortexmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Frequency-Following Responses to Speech Sounds Are Highly Conserved across Species and Contain Cortical Contributions

Rupp

Llanos

et al. 2021

eNeuro

“…Topography, i.e., the spatial distribution of electric or magnetic fields across the scalp, can then be analyzed using source modeling approaches to further narrow down the exact spatial location of the underlying neural generators. Recent work has shown that source modeling can also be leveraged to better understand the neural generators of the FFR (Gerken et al, 1975;Bidelman, 2015;Coffey et al, 2016;Gorina-Careta et al, 2021). However, because of its dependence on high channel-count EEG and/or MEG recordings, source modeling is often not feasible for clinical FFR data which is typically recorded with a 3-electrode montage.…”

Section: Introductionmentioning

confidence: 99%

Linear superposition of responses evoked by individual glottal pulses explain over 80% of the frequency following response to human speech in the macaque monkey

Teichert

Sadagopan

et al. 2021

Preprint

The frequency-following response (FFR) is a scalp-recorded electrophysiological potential that closely follows the periodicity of complex sounds such as speech. It has been suggested that FFRs reflect the linear superposition of responses that are triggered by the glottal pulse in each cycle of the fundamental frequency (F0 responses) and sequentially propagate through auditory processing stages in brainstem, midbrain, and cortex. However, this conceptualization of the FFR is debated, and it remains unclear if and how well a simple linear superposition can capture the spectro-temporal complexity of FFRs that are generated within the highly recurrent and non-linear auditory system. To address this question, we used a deconvolution approach to compute the hypothetical F0 responses that best explain the FFRs in rhesus monkeys to human speech and click trains with time-varying pitch patterns. The linear superposition of F0 responses explained well over 90% of the variance of click train steady state FFRs and well over 80% of mandarin tone steady state FFRs. The F0 responses could be measured with high signal-to-noise ratio and featured several spectro-temporally and topographically distinct components that likely reflect the activation of brainstem (<5ms; 200-1000 Hz), midbrain (5-15 ms; 100-250 Hz) and cortex (15-35 ms; ~90 Hz). In summary, our results in the monkey support the notion that FFRs arise as the superposition of F0 responses by showing for the first time that they can capture the bulk of the variance and spectro-temporal complexity of FFRs to human speech with time-varying pitch. These findings identify F0 responses as a potential diagnostic tool that may be useful to reliably link altered FFRs in speech and language disorders to altered F0 responses and thus to specific latencies, frequency bands and ultimately processing stages.

“…Furthermore, periodicity code of pitch is thought to be transformed into a rate or rate-place code in the upper brainstem (Plack et al, 2014). Recent studies using magnetoencephalography (MEG) and EEG have challenged these accounts and shown substantial cortical contributions to the scalp FFRs, with a distinct rightward cortical asymmetry (Coffey et al, 2016(Coffey et al, , 2021Hartmann and Weisz, 2019;Gorina-Careta et al, 2021). However, noninvasive MEG and EEG studies require inferences based on distributed source modelling approaches which are relatively less sensitive to deep brain sources.…”

Section: Introductionmentioning

confidence: 99%

Deconstructing the Cortical Sources of Frequency Following Responses to Speech: A Cross-species Approach

Rupp

Llanos

et al. 2021

Preprint

Time-varying pitch is a vital cue in the processing of speech signals. Neural processing of time-varying pitch cues in speech has been extensively assayed using scalp-recorded frequency-following responses (FFRs), which are thought to reflect integrated phase-locked activity from neural ensembles exclusively along the subcortical auditory pathway. Emerging evidence however suggests that the auditory cortex contributes to the FFRs as well. However, the response properties and the relative cortical contribution to the scalp-recorded FFR are only beginning to be explored. Here we used direct intracortical recordings from human subjects and animal models (macaque, guinea pig) to deconstruct the cortical sources of FFRs and leveraged representational similarity analysis as a translational bridge to characterize similarities between the human and animal models. We found robust FFRs in the auditory cortex that emerged from the thalamorecepient layers of the auditory cortex and contributed to the scalp-recorded FFRs via volume conduction.