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

Lemos, Fabiana Aparecida; Nunes, Aryelly Dayane da Silva; Evangelista, Carolina Karla de Souza; Escera, Carles; Taveira, Karinna Veríssimo Meira; Balen, Sheila Andreoli

doi:10.1044/2021_jslhr-20-00639

Cited by 12 publications

(8 citation statements)

References 66 publications

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“…To explore if our results could be related to maternal pregestational BMI and/or maternal GWG, as previous studies showed a relationship between variables and the offspring’s cognitive outcomes, 53,54 we carried out two matrices of correlations, one for each maternal variable (maternal pregestational BMI and maternal GWG) and all the FFR ENV parameters. These analyses yielded no significant correlations.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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Objectives:The present envelope frequency-following response (FFR ENV ) 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. FFR ENV s 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 (F 0ENV ) was characterized through the FFR ENV 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 F 0ENV compared to the AGA group. Significant negative correlations were found between neonatal BMI and the spectral amplitude at the F 0ENV . 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 FFR ENV 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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2023

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“…In later stages of data analysis, these thousands of repetitions will be averaged to obtain a clear and visible FFR. Certain signal acquisition and preprocessing parameters have been established as the most appropriate for obtaining the neonatal FFR (for a systematic review of the acquisition parameters of FFR in neonates and infants, see Lemos et al 44 ). The best time window to record the FFR is when the newborn is between 1 and 3 days old.…”

Section: Technical Aspects For Obtaining a Neonatal Ffr: Stimulation ...mentioning

confidence: 99%

“…The auditory stimulus most used in the study of the FFR is the syllable /da/, 4 44 45 especially in infant and adult populations. Consonant–vowel syllables are sounds considered acoustically complex and, in particular, the syllable /da/ is relatively universal, since it is included in the phonetic inventories of most languages.…”

Section: Technical Aspects For Obtaining a Neonatal Ffr: Stimulation ...mentioning

confidence: 99%

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

et al. 2022

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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.

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“…For a thorough review of the FFR technology, see Skoe and Kraus, 7 Krizman and Kraus, 19 and Lemos and colleagues. 63…”

Section: Frequency-following Responsementioning

confidence: 99%

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

Jeng

2022

Semin Hear

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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.

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

Cited by 12 publications

References 66 publications

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

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

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

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

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