Linearized encoding models are increasingly employed to model cortical responses to running speech. Recent extensions to subcortical responses suggest clinical perspectives, potentially complementing auditory brainstem responses (ABRs) or frequency-following responses (FFRs) that are current clinical standards. However, while it is well-known that the auditory brainstem responds both to transient amplitude variations and the stimulus periodicity that gives rise to pitch, these features co-vary in running speech. Here, we discuss challenges in disentangling the features that drive the subcortical response to running speech. Cortical and subcortical electroencephalographic (EEG) responses to running speech from 19 normal-hearing listeners (12 female) were analyzed. Using forward regression models, we confirm that responses to the rectified broadband speech signal yield temporal response functions consistent with wave V of the ABR, as shown in previous work. Peak latency and amplitude of the speech-evoked brainstem response were correlated with standard click-evoked ABRs recorded at the vertex electrode (Cz). Similar responses could be obtained using the fundamental frequency (F0) of the speech signal as model predictor. However, simulations indicated that dissociating responses to temporal fine structure at the F0 from broadband amplitude variations is not possible given the high co-variance of the features and the poor signal-to-noise ratio (SNR) of subcortical EEG responses. In cortex, both simulations and data replicated previous findings indicating that envelope tracking on frontal electrodes can be dissociated from responses to slow variations in F0 (relative pitch). Yet, no association between subcortical F0-tracking and cortical responses to relative pitch could be detected. These results indicate that while subcortical speech responses are comparable to click-evoked ABRs, dissociating pitch-related processing in the auditory brainstem may be challenging with natural speech stimuli.
Perception of sounds and speech involves structures in the auditory brainstem that rapidly process ongoing auditory stimuli. The role of these structures in speech understanding can be investigated by measuring their electrical activity using scalpmounted electrodes. Typical analysis methods involve averaging responses to many short repetitive stimuli. Recently, responses to more ecologically relevant continuous speech were detected using linear encoding models called temporal response functions (TRFs). Non-linear predictors derived from complex auditory models may improve TRFs. Here, we compare predictors from both simple and complex auditory models for estimating brainstem TRFs on electroencephalography (EEG) data from 24 subjects listening to continuous speech. Predictors from simple models result in comparable TRFs to those from complex models, and are much faster to compute. We also discuss the effect of data length on TRF peaks for efficient estimation of subcortical TRFs.
Purpose Older adults seeking audiologic rehabilitation often present with medical comorbidities, yet these realities of practice are poorly understood. Study aims were to examine (a) the frequency of identification of selected comorbidities in clients of a geriatric audiology clinic, (b) the influence of comorbidities on audiology practice, and (c) the effect of comorbidities on rehabilitation outcomes. Method The records of 135 clients ( M age = 86 years) were examined. Information about comorbidities came from audiology charts (physical paper files) and hospital electronic health records (EHRs). Data about rehabilitation recommendations and outcomes came from the charts. Focus groups with audiologists probed their views of how comorbidities influenced their practice. Results The frequency of identification was 68% for visual, 50% for cognitive, and 42% for manual dexterity issues; 84% had more than one comorbidity. Also noted were hypertension (43%), falls (33%), diabetes (13%), and depression (16%). Integrating information from the audiology chart and EHR provided a more complete understanding of comorbidities. Information about hearing in the EHR included logs of outpatient audiology visits (75% of 135 cases), audiologists' care notes for inpatients and long-term care residents (25%), and entries by other health professionals (60%). Modifications to audiology practice were common and varied depending on comorbidity. High rates of success were achieved regardless of comorbidities. Conclusions In this clinic, successful outcomes were achieved by modifying audiology practice for clients with comorbidities. Increased interprofessional communication among clinicians in the circle of care could improve care planning and outcomes for older adults with hearing loss.
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