Frequency-following response among neonates with progressive moderate hyperbilirubinemia

The frequency-following response in late preterm neonates: a pilot study

Introduction

Infants born very early preterm are at high risk of language delays. However, less is known about the consequences of late prematurity. Hence, the aim of the present study is to characterize the neural encoding of speech sounds in late preterm neonates in comparison with those born at term.

Methods

The speech-evoked frequency-following response (FFR) was recorded to a consonant-vowel stimulus /da/ in 36 neonates in three different groups: 12 preterm neonates [mean gestational age (GA) 36.05 weeks], 12 "early term neonates" (mean GA 38.3 weeks), and "late term neonates" (mean GA 41.01 weeks).

Results

From the FFR recordings, a delayed neural response and a weaker stimulus F0 encoding in premature neonates compared to neonates born at term was observed. No differences in the response time onset nor in stimulus F0 encoding were observed between the two groups of neonates born at term. No differences between the three groups were observed in the neural encoding of the stimulus temporal fine structure.

Discussion

These results highlight alterations in the neural encoding of speech sounds related to prematurity, which were present for the stimulus F0 but not for its temporal fine structure.

Developmental Trajectory of the Frequency-Following Response During the First 6 Months of Life

PURPOSE

The aim of the present study is to characterize the maturational changes during the first 6 months of life in the neural encoding of two speech sound features relevant for early language acquisition: the stimulus fundamental frequency (fo), related to stimulus pitch, and the vowel formant composition, particularly F1. The frequency-following response (FFR) was used as a snapshot into the neural encoding of these two stimulus attributes.

METHOD

FFRs to a consonant-vowel stimulus /da/ were retrieved from electroencephalographic recordings in a sample of 80 healthy infants (45 at birth and 35 at the age of 1 month). Thirty-two infants (16 recorded at birth and 16 recorded at 1 month) returned for a second recording at 6 months of age.

RESULTS

Stimulus fo and F1 encoding showed improvements from birth to 6 months of age. Most remarkably, a significant improvement in the F1 neural encoding was observed during the first month of life.

CONCLUSION

Our results highlight the rapid and sustained maturation of the basic neural machinery necessary for the phoneme discrimination ability during the first 6 months of age.

Speech-Encoding Deficits in Neonates Born Large-for-Gestational Age as Revealed With the Envelope Frequency-Following Response

OBJECTIVES

The present envelope frequency-following response (FFRENV) study aimed at characterizing the neural encoding of the fundamental frequency of speech sounds in neonates born at the higher end of the birth weight continuum (>90th percentile), known as large-for-gestational age (LGA).

DESIGN

Twenty-five LGA newborns were recruited from the maternity unit of Sant Joan de Déu Barcelona Children's Hospital and paired by age and sex with 25 babies born adequate-for-gestational age (AGA), all from healthy mothers and normal pregnancies. FFRENVs were elicited to the/da/ syllable and recorded while the baby was sleeping in its cradle after a successful universal hearing screening. Neural encoding of the stimulus' envelope of the fundamental frequency (F0ENV) was characterized through the FFRENV spectral amplitude. Relationships between electrophysiological parameters and maternal/neonatal variables that may condition neonatal neurodevelopment were assessed, including pregestational body mass index (BMI), maternal gestational weight gain and neonatal BMI.

RESULTS

LGA newborns showed smaller spectral amplitudes at the F0ENV compared to the AGA group. Significant negative correlations were found between neonatal BMI and the spectral amplitude at the F0ENV.

CONCLUSIONS

Our results indicate that in spite of having a healthy pregnancy, LGA neonates' central auditory system is impaired in encoding a fundamental aspect of the speech sounds, namely their fundamental frequency. The negative correlation between the neonates' BMI and FFRENV indicates that this impaired encoding is independent of the pregnant woman BMI and weight gain during pregnancy, supporting the role of the neonatal BMI. We suggest that the higher adipose tissue observed in the LGA group may impair, via proinflammatory products, the fine-grained central auditory system microstructure required for the neural encoding of the fundamental frequency of speech sounds.

Neonatal Frequency-Following Responses: A Methodological Framework for Clinical Applications

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.

Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial

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.

Neuroaudiological Considerations for the Auditory Brainstem Response and Middle Latency Response Revisited: Back to the Future

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.

Non-negative matrix factorization improves the efficiency of recording frequency-following responses in normal-hearing adults and neonates

OBJECTIVE

One challenge in extracting the scalp-recorded frequency-following response (FFR) is related to its inherently small amplitude, which means that the response cannot be identified with confidence when only a relatively small number of recording sweeps are included in the averaging procedure.

DESIGN

This study examined how the non-negative matrix factorisation (NMF) algorithm with a source separation constraint could be applied to improve the efficiency of FFR recordings. Conventional FFRs elicited by an English vowel/i/with a rising frequency contour were collected. Study sample: Fifteen normal-hearing adults and 15 normal-hearing neonates were recruited.

RESULTS

The improvements of FFR recordings, defined as the correlation coefficient and root-mean-square differences across a sweep series of amplitude spectrograms before and after the application of the source separation NMF (SSNMF) algorithm, were characterised through an exponential curve fitting model. Statistical analysis of variance indicated that the SSNMF algorithm was able to enhance the FFRs recorded in both groups of participants.

CONCLUSIONS

Such improvements enabled FFR extractions in a relatively small number of recording sweeps, and opened a new window to better understand how speech sounds are processed in the human brain.

Deficient neural encoding of speech sounds in term neonates born after fetal growth restriction

Infants born after fetal growth restriction (FGR)-an obstetric condition defined as the failure to achieve the genetic growth potential-are prone to neurodevelopmental delays, with language being one of the major affected areas. Yet, while verbal comprehension and expressive language impairments have been observed in FGR infants, children and even adults, specific related impairments at birth, such as in the ability to encode the sounds of speech, necessary for language acquisition, remain to be disclosed. Here, we used the frequency-following response (FFR), a brain potential correlate of the neural phase locking to complex auditory stimuli, to explore the encoding of speech sounds in FGR neonates. Fifty-three neonates born with FGR and 48 controls born with weight adequate-for-gestational age (AGA) were recruited. The FFR was recorded to the consonant-vowel stimulus (/da/) during sleep and quantified as the spectral amplitude to the fundamental frequency of the syllable and its signal-to-noise ratio (SNR). The outcome was available in 45 AGA and 51 FGR neonates, yielding no differences for spectral amplitudes. However, SNR was strongly attenuated in the FGR group compared to the AGA group at the vowel region of the stimulus. These findings suggest that FGR population present a deficit in the neural pitch tracking of speech sounds already present at birth. Our results pave the way for future research on the potential clinical use of the FFR in this population, so that if confirmed, a disrupted FFR recorded at birth may help deriving FGR neonates at risk for postnatal follow-ups.

Frequency-Following Response in Newborns and Infants: A Systematic Review of Acquisition Parameters

Purpose The purpose of this study is to characterize parameters used for frequency-following response (FFR) acquisition in children up to 24 months of age through a systematic review. Method The study was registered in PROSPERO and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses' recommendations. Search was performed in six databases (LILACS, LIVIVO, PsycINFO, PubMed, Scopus, and Web of Science) and gray literature (Google Scholar, OpenGrey, ProQuest)as well as via manual searches in bibliographic references. Observational studies using speech stimuli to elicit the FFR in infants with normal hearing on the age range from 0 until 24 months were included. No restrictions regarding language and year of publication were applied. Risk of bias was assessed with the Joanna Briggs Institute Critical Appraisal Checklist. Data on stimulus, presentation rate, time window for analysis, number of sweeps, artifact rejection, online filters, stimulated ear, and examination condition were extracted. Results Four hundred fifty-nine studies were identified. After removing duplicates and reading titles and abstracts, 15 articles were included. Seven studies were classified as low risk of bias, seven as moderate risk, and one as high risk. Conclusions There is a consensus in the use of some acquisition parameters of the FFR with speech stimulus, such as the vertical mounting, the use of alternating polarity, a sampling rate of 20000 Hz, and the /da/ synthesized syllable of 40 ms in duration as the preferred stimulus. Although these parameters show some consensus, the results disclosed lack of a single established protocol for FFR acquisition with speech stimulus in infants in the investigated age range.

Factors influencing classification of frequency following responses to speech and music stimuli

Successful mapping of meaningful labels to sound input requires accurate representation of that sound's acoustic variances in time and spectrum. For some individuals, such as children or those with hearing loss, having an objective measure of the integrity of this representation could be useful. Classification is a promising machine learning approach which can be used to objectively predict a stimulus label from the brain response. This approach has been previously used with auditory evoked potentials (AEP) such as the frequency following response (FFR), but a number of key issues remain unresolved before classification can be translated into clinical practice. Specifically, past efforts at FFR classification have used data from a given subject for both training and testing the classifier. It is also unclear which components of the FFR elicit optimal classification accuracy. To address these issues, we recorded FFRs from 13 adults with normal hearing in response to speech and music stimuli. We compared labeling accuracy of two cross-validation classification approaches using FFR data: (1) a more traditional method combining subject data in both the training and testing set, and (2) a "leave-one-out" approach, in which subject data is classified based on a model built exclusively from the data of other individuals. We also examined classification accuracy on decomposed and time-segmented FFRs. Our results indicate that the accuracy of leave-one-subject-out cross validation approaches that obtained in the more conventional cross-validation classifications while allowing a subject's results to be analysed with respect to normative data pooled from a separate population. In addition, we demonstrate that classification accuracy is highest when the entire FFR is used to train the classifier. Taken together, these efforts contribute key steps toward translation of classification-based machine learning approaches into clinical practice.

Analyzing the FFR: A tutorial for decoding the richness of auditory function

The frequency-following response, or FFR, is a neurophysiological response to sound that precisely reflects the ongoing dynamics of sound. It can be used to study the integrity and malleability of neural encoding of sound across the lifespan. Sound processing in the brain can be impaired with pathology and enhanced through expertise. The FFR can index linguistic deprivation, autism, concussion, and reading impairment, and can reflect the impact of enrichment with short-term training, bilingualism, and musicianship. Because of this vast potential, interest in the FFR has grown considerably in the decade since our first tutorial. Despite its widespread adoption, there remains a gap in the current knowledge of its analytical potential. This tutorial aims to bridge this gap. Using recording methods we have employed for the last 20 + years, we have explored many analysis strategies. In this tutorial, we review what we have learned and what we think constitutes the most effective ways of capturing what the FFR can tell us. The tutorial covers FFR components (timing, fundamental frequency, harmonics) and factors that influence FFR (stimulus polarity, response averaging, and stimulus presentation/recording jitter). The spotlight is on FFR analyses, including ways to analyze FFR timing (peaks, autocorrelation, phase consistency, cross-phaseogram), magnitude (RMS, SNR, FFT), and fidelity (stimulus-response correlations, response-to-response correlations and response consistency). The wealth of information contained within an FFR recording brings us closer to understanding how the brain reconstructs our sonic world.