Effects of aging on cortical representations of continuous speech

Aging effects on the neural representation and perception of consonant transition cues

Older listeners have difficulty processing temporal cues that are important for word discrimination, and deficient processing may limit their ability to benefit from these cues. Here, we investigated aging effects on perception and neural representation of the consonant transition and the factors that contribute to successful perception. To further understand the neural mechanisms underlying the changes in processing from brainstem to cortex, we also examined the factors that contribute to exaggerated amplitudes in cortex. We enrolled 30 younger normal-hearing and 30 older normal-hearing participants who met the criteria of clinically normal hearing. Perceptual identification functions were obtained for the words BEAT and WHEAT on a 7-step continuum of consonant-transition duration. Auditory brainstem responses (ABRs) were recorded to click stimuli and frequency-following responses (FFRs) and cortical auditory-evoked potentials were recorded to the endpoints of the BEAT-WHEAT continuum. Perceptual performance for identification of BEAT vs. WHEAT did not differ between younger and older listeners. However, both subcortical and cortical measures of neural representation showed age group differences, such that FFR phase locking was lower but cortical amplitudes (P1 and N1) were higher in older compared to younger listeners. ABR Wave I amplitude and FFR phase locking, but not audiometric thresholds, predicted early cortical amplitudes. Phase locking to the transition region and early cortical peak amplitudes (P1) predicted performance on the perceptual identification function. Overall, results suggest that the neural representation of transition durations and cortical overcompensation may contribute to the ability to perceive transition duration contrasts. Cortical overcompensation appears to be a maladaptive response to decreased neural firing/synchrony.

Monitoring Disease Severity of Mild Cognitive Impairment from Single-Channel EEG Data Using Regression Analysis

A deviation in the soundness of cognitive health is known as mild cognitive impairment (MCI), and it is important to monitor it early to prevent complicated diseases such as dementia, Alzheimer's disease (AD), and Parkinson's disease (PD). Traditionally, MCI severity is monitored with manual scoring using the Montreal Cognitive Assessment (MoCA). In this study, we propose a new MCI severity monitoring algorithm with regression analysis of extracted features of single-channel electro-encephalography (EEG) data by automatically generating severity scores equivalent to MoCA scores. We evaluated both multi-trial and single-trail analysis for the algorithm development. For multi-trial analysis, 590 features were extracted from the prominent event-related potential (ERP) points and corresponding time domain characteristics, and we utilized the lasso regression technique to select the best feature set. The 13 best features were used in the classical regression techniques: multivariate regression (MR), ensemble regression (ER), support vector regression (SVR), and ridge regression (RR). The best results were observed for ER with an RMSE of 1.6 and residual analysis. In single-trial analysis, we extracted a time-frequency plot image from each trial and fed it as an input to the constructed convolutional deep neural network (CNN). This deep CNN model resulted an RMSE of 2.76. To our knowledge, this is the first attempt to generate automated scores for MCI severity equivalent to MoCA from single-channel EEG data with multi-trial and single data.

Attention Mobilization as a Modulator of Listening Effort: Evidence From Pupillometry

Listening to speech in noise can require substantial mental effort, even among younger normal-hearing adults. The task-evoked pupil response (TEPR) has been shown to track the increased effort exerted to recognize words or sentences in increasing noise. However, few studies have examined the trajectory of listening effort across longer, more natural, stretches of speech, or the extent to which expectations about upcoming listening difficulty modulate the TEPR. Seventeen younger normal-hearing adults listened to 60-s-long audiobook passages, repeated three times in a row, at two different signal-to-noise ratios (SNRs) while pupil size was recorded. There was a significant interaction between SNR, repetition, and baseline pupil size on sustained listening effort. At lower baseline pupil sizes, potentially reflecting lower attention mobilization, TEPRs were more sustained in the harder SNR condition, particularly when attention mobilization remained low by the third presentation. At intermediate baseline pupil sizes, differences between conditions were largely absent, suggesting these listeners had optimally mobilized their attention for both SNRs. Lastly, at higher baseline pupil sizes, potentially reflecting overmobilization of attention, the effect of SNR was initially reversed for the second and third presentations: participants initially appeared to disengage in the harder SNR condition, resulting in reduced TEPRs that recovered in the second half of the story. Together, these findings suggest that the unfolding of listening effort over time depends critically on the extent to which individuals have successfully mobilized their attention in anticipation of difficult listening conditions.