Baseline, retest, and post-injury profiles of auditory neural function in collegiate football players

Subconcussion revealed by sound processing in the brain

Introduction/Purpose

We tested the hypothesis that an objective measure of auditory processing reveals a history of head trauma that does not meet the clinical definition of concussion.

Methods

Division I collegiate student-athletes (n = 709) across 19 sports were divided into groups, based on their sport, using prevailing classifications of "contact" (317 males, 212 females) and "noncontact" (58 males, 122 females). Participants were evaluated using the frequency-following response (FFR) to speech. The amplitude of FFR activity in a frequency band corresponding to the fundamental frequency (F0)-the voice pitch-of the speech stimulus, an outcome reduced in individuals with concussions, was critically examined.

Results

We found main effects of contact level and sex. The FFR-F0 was smaller in contact athletes than noncontact athletes and larger in females than males. There was a contact by sex interaction, with the FFR-F0 of males in the contact group being smaller than the three other groups. Secondary analyses found a correlation between FFR-F0 and length of participation in contact sports in male athletes.

Conclusion

These findings suggest that the disruption of sensory processing in the brain can be observed in individuals without a concussion but whose sport features regular physical contact. This evidence identifies sound processing in the brain as an objective marker of subconcussion in athletes.

Athleticism and sex impact neural processing of sound

Biology and experience both influence the auditory brain. Sex is one biological factor with pervasive effects on auditory processing. Females process sounds faster and more robustly than males. These differences are linked to hormone differences between the sexes. Athleticism is an experiential factor known to reduce ongoing neural noise, but whether it influences how sounds are processed by the brain is unknown. Furthermore, it is unknown whether sports participation influences auditory processing differently in males and females, given the well-documented sex differences in auditory processing seen in the general population. We hypothesized that athleticism enhances auditory processing and that these enhancements are greater in females. To test these hypotheses, we measured auditory processing in collegiate Division I male and female student-athletes and their non-athlete peers (total n = 1012) using the frequency-following response (FFR). The FFR is a neurophysiological response to sound that reflects the processing of discrete sound features. We measured across-trial consistency of the response in addition to fundamental frequency (F0) and harmonic encoding. We found that athletes had enhanced encoding of the harmonics, which was greatest in the female athletes, and that athletes had more consistent responses than non-athletes. In contrast, F0 encoding was reduced in athletes. The harmonic-encoding advantage in female athletes aligns with previous work linking harmonic encoding strength to female hormone levels and studies showing estrogen as mediating athlete sex differences in other sensory domains. Lastly, persistent deficits in auditory processing from previous concussive and repetitive subconcussive head trauma may underlie the reduced F0 encoding in athletes, as poor F0 encoding is a hallmark of concussion injury.

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.

A Home-Based Approach to Auditory Brainstem Response Measurement: Proof-of-Concept and Practical Guidelines

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.

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.