Seminars in Hearing最新文献

The influence of male and female vowel characteristics on the envelope-following responses (EFRs) is not well understood. This study explored the role of vowel characteristics on the EFR at the fundamental frequency (f0) in response to the vowel /ε/ (as in "head"). Vowel tokens were spoken by five males and five females and EFRs were measured in 25 young adults (21 females). An auditory model was used to estimate changes in auditory processing that might account for talker effects on EFR amplitude. There were several differences between male and female vowels in relation to the EFR. For male talkers, EFR amplitudes were correlated with the bandwidth and harmonic count of the first formant, and the amplitude of the trough below the second formant. For female talkers, EFR amplitudes were correlated with the range of f0 frequencies and the amplitude of the trough above the second formant. The model suggested that the f0 EFR reflects a wide distribution of energy in speech, with primary contributions from high-frequency harmonics mediated from cochlear regions basal to the peaks of the first and second formants, not from low-frequency harmonics with energy near f0. Vowels produced by female talkers tend to produce lower-amplitude EFR, likely because they depend on higher-frequency harmonics where speech sound levels tend to be lower. This work advances auditory electrophysiology by showing how the EFR evoked by speech relates to the acoustics of speech, for both male and female voices.

The frequency-following response (FFR) to periodic complex sounds is a noninvasive scalp-recorded auditory evoked potential that reflects synchronous phase-locked neural activity to the spectrotemporal components of the acoustic signal along the ascending auditory hierarchy. The FFR has gained recent interest in the fields of audiology and auditory cognitive neuroscience, as it has great potential to answer both basic and applied questions about processes involved in sound encoding, language development, and communication. Specifically, it has become a promising tool in neonates, as its study may allow both early identification of future language disorders and the opportunity to leverage brain plasticity during the first 2 years of life, as well as enable early interventions to prevent and/or ameliorate sound and language encoding disorders. Throughout the present review, we summarize the state of the art of the neonatal FFR and, based on our own extensive experience, present methodological approaches to record it in a clinical environment. Overall, the present review is the first one that comprehensively focuses on the neonatal FFRs applications, thus supporting the feasibility to record the FFR during the first days of life and the predictive potential of the neonatal FFR on detecting short- and long-term language abilities and disruptions.

The frequency-following response (FFR) provides enriched information on how acoustic stimuli are processed in the human brain. Based on recent studies, machine learning techniques have demonstrated great utility in modeling human FFRs. This tutorial focuses on the fundamental principles, algorithmic designs, and custom implementations of several supervised models (linear regression, logistic regression, k -nearest neighbors, support vector machines) and an unsupervised model ( k -means clustering). Other useful machine learning tools (Markov chains, dimensionality reduction, principal components analysis, nonnegative matrix factorization, and neural networks) are discussed as well. Each model's applicability and its pros and cons are explained. The choice of a suitable model is highly dependent on the research question, FFR recordings, target variables, extracted features, and their data types. To promote understanding, an example project implemented in Python is provided, which demonstrates practical usage of several of the discussed models on a sample dataset of six FFR features and a target response label.

This article provides a brief overview of auditory evoked potentials (AEPs) and their application in the areas of research and clinics within the field of communication disorders. The article begins with providing a historical perspective within the context of the key scientific developments that led to the emergence of numerous types of AEPs. Furthermore, the article discusses the different AEP techniques in the light of their feasibility in clinics. As AEPs, because of their versatility, find their use across disciplines, this article also discusses some of the research questions that are currently being addressed using AEP techniques in the field of communication disorders and beyond. At the end, this article summarizes the shortcomings of the existing AEP techniques and provides a general perspective toward the future directions. The article is aimed at a broad readership including (but not limited to) students, clinicians, and researchers. Overall, this article may act as a brief primer for the new AEP users, and as an overview of the progress in the field of AEPs along with future directions, for those who already use AEPs on a routine basis.

Little is known about objective classifying of noise exposure risk levels in personal listening device (PLD) users and electrophysiologic evidence of cochlear synaptopathy at very fast click rates. The aim of the study was to objectively classify noise exposure risk using iPhone Health app and identify signs of cochlear synaptopathy using behavioral and electrophysiologic measures. Thirty normal-hearing females (aged 18-26 years) were grouped based on their iPhone Health app's 6-month listening level and noise exposure data into low-risk and high-risk groups. They were assessed using a questionnaire, extended high-frequency (EHF) audiometry, QuickSIN test, distortion-product otoacoustic emission (DPOAE), and simultaneous recording of electrocochleography (ECochG) and auditory brainstem response (ABR) at three click rates (19.5/s, 97.7/s, 234.4/s). A series of ANOVAs and independent samples t -test were conducted for group comparison. Both groups had within-normal EHF hearing thresholds and DPOAEs. However, the high-risk participants were over twice as likely to suffer from tinnitus, had abnormally large summating potential to action potential amplitude and area ratios at fast rates, and had slightly smaller waves I and V amplitudes. The high-risk group demonstrated a profile of behavioral and objective signs of cochlear synaptopathy based on ECochG and ABR recordings at fast click rates. The findings in this study suggest that the iPhone Health app may be a useful tool for further investigation into cochlear synaptopathy in PLD users.

Broad-scale neuroscientific investigations of diverse human populations are difficult to implement. This is because the primary neuroimaging methods (magnetic resonance imaging, electroencephalography [EEG]) historically have not been portable, and participants may be unable or unwilling to travel to test sites. Miniaturization of EEG technologies has now opened the door to neuroscientific fieldwork, allowing for easier access to under-represented populations. Recent efforts to conduct auditory neuroscience outside a laboratory setting are reviewed and then an in-home technique for recording auditory brainstem responses (ABRs) and frequency-following responses (FFRs) in a home setting is introduced. As a proof of concept, we have conducted two in-home electrophysiological studies: one in 27 children aged 6 to 16 years (13 with autism spectrum disorder) and another in 12 young adults aged 18 to 27 years, using portable electrophysiological equipment to record ABRs and FFRs to click and speech stimuli, spanning rural and urban and multiple homes and testers. We validate our fieldwork approach by presenting waveforms and data on latencies and signal-to-noise ratio. Our findings demonstrate the feasibility and utility of home-based ABR/FFR techniques, paving the course for larger fieldwork investigations of populations that are difficult to test or recruit. We conclude this tutorial with practical tips and guidelines for recording ABRs and FFRs in the field and discuss possible clinical and research applications of this approach.

The auditory brainstem response (ABR) and middle latency response (MLR) are two sets of evoked potentials that have made major contributions to the field of diagnostic audiology. Many of these contributions were guided by clinical research audiologists. Though many of these auditory evoked potentials (AEPs) are still being used diagnostically by audiologists, there has been a steep decline in their popularity both clinically and in the research laboratory. This is indeed most unfortunate because these AEPs could and should be advancing our field and benefitting many patients. In this article, some critical research is overviewed that addresses some of the reasons why these AEPs (ABR and MLR) are not being utilized as frequently as they should be for neuroauditory assessments. Reflecting on our past when ABR and MLR were more commonly used can serve as a model for our future. Multiple applications and the diagnostic value of these AEPs are presented in an effort to convince audiologists that these electrophysiologic procedures should be revisited and reapplied in the clinic and research settings. It is argued that the dwindling use of ABR and MLR (and AEPs in general) in the field of audiology is not only remarkably premature but also lacks good scientific grounding. While on the other hand, if applied clinically, the value of these AEPs is both substantial and promising.

Machine learning (ML) is transforming nearly every aspect of modern life including medicine and its subfields, such as hearing science. This article presents a brief conceptual overview of selected ML approaches and describes how these techniques are being applied to outstanding problems in hearing science, with a particular focus on auditory evoked potentials (AEPs). Two vignettes are presented in which ML is used to analyze subcortical AEP data. The first vignette demonstrates how ML can be used to determine if auditory learning has influenced auditory neurophysiologic function. The second vignette demonstrates how ML analysis of AEPs may be useful in determining whether hearing devices are optimized for discriminating speech sounds.

Many barriers to accessibility exist for Medicare beneficiaries seeking hearing and balance care such as availability of providers, coverage for services, and ability to pay. Other statutory and administrative barriers exist including the need for physician orders to have audiology services covered, the classification of audiologists as suppliers of "other diagnostic tests" under Medicare payer policy, and non-coverage of certain audiologic management and treatment services. Nearly two decades of legislative efforts have not resulted in any substantial changes to U.S. health policy, while the need for audiology services has increased due to a growing demographic of older adults. The Medicare Audiologist Access and Services Act (MAASA) has been introduced in the 116th and 117th Congress and proposes amendments to the Social Security Act that would address statutory barriers to accessing hearing and balance care among Medicare beneficiaries and would recognize audiologists for their scope of practice within the Medicare program. Objectives of the present review are to provide a summary of statutes in the Social Security Act and Standard Occupational Classification system which affect audiologists, audiology services, and Medicare beneficiaries and to discuss previous and current legislative health policy efforts to address these statutory barriers to hearing and balance care access.