Neural tracking of linguistic speech representations decreases with advancing age

Abstract

Background: Older adults process speech differently, but it is not yet clear how aging affects different levels of processing natural, continuous speech, both in terms of bottom-up acoustic analysis and top-down generation of linguistic-based predictions. We studied natural speech processing across the adult lifespan via EEG measurements of neural tracking. Goals: Our goals are to analyze the unique contribution of linguistic speech processing across the adult lifespan using natural speech, while controlling for the influence of acoustic processing. In particular, we focus on changes in spatial and temporal activation patterns in response to natural speech across the lifespan. Methods: 52 normal-hearing adults between 17 and 82 years of age listened to a naturally spoken story while the EEG signal was recorded. We investigated the effect of age on acoustic and linguistic processing of speech. Because age correlated with hearing capacity and measures of cognition, we investigated whether the observed age effect is mediated by these factors. Furthermore, we investigated whether there is an effect of age on hemisphere lateralization and on spatiotemporal patterns of the neural responses. Results: Our EEG results showed that linguistic speech processing declines with advancing age. Moreover, as age increased, the neural response latency to certain aspects of linguistic speech processing increased. Also acoustic neural tracking decreased with increasing age but in contrast to linguistic processing, older subjects showed shorter latencies for early acoustic responses to speech. No evidence was found for hemispheric lateralization in neither younger nor older adults during linguistic speech processing. Most of the observed aging effects on acoustic and linguistic processing were not explained by age-related decline in hearing capacity or cognition. However, our results suggest that the effect of increasing neural response latency with age for word-level linguistic processing is likely more due to an age-related decline in cognition than a robust effect of age. Conclusion: Spatial and temporal characteristics of the neural responses to continuous speech change across the adult lifespan for both acoustic and linguistic speech processing. These are most likely explained by compensation mechanisms that occur during healthy aging. Traces of this compensation mechanism are reported as modifications in the neural response to acoustic and linguistic speech characteristics in terms of spatial and temporal differences, suggesting structural and functional changes. Additionally, different frontal patterns were observed for linguistic processing, suggesting that older adults recruit additional frontal brain regions to compensate for the age-related decline.

Beyond Linear Neural Envelope Tracking: A Mutual Information Approach

“…Finally, we compare the linear forward model to MI in a group study. In a recent comparison, Gillis et al (2022) found higher neural envelope tracking for younger listeners compared to older adults. We replicated these results (Wilcoxon signed-rank test, W=95, p=0.039, Figure 6A).…”

“…A popular feature of the linear TRF is that it allows for peak latency analyses. In this dataset, a shift in the latency of the first peak was observed as age increased (Gillis et al, 2022). We plotted this trend for the parieto-occipital (positive peak) and central (negative peak) channels in Figure 6C (Pearson's r=-0.27, p=0.048; r=-0.39, p=0.004, respectively).…”

“…We refer to the original paper of Decruy et al (2019) for a detailed description of the demographics and the methods. The data were recently reanalyzed with the forward modeling approach showing higher neural tracking for younger adults compared to elderly (Gillis et al, 2022). Here, we apply MI to demonstrate certain interesting properties of this method for group comparisons.…”

“…Second, we investigate whether the MI analysis captures nonlinear relationships between the envelope and the neural response on the individual channel level. And third, we apply both measures to a group study (i.e., different age groups) for which we know significant group differences exist (Gillis et al, 2022), with the purpose of showing subtle yet important differences between both measures.…”

The effects of data quantity on performance of temporal response function analyses of natural speech processing

“…It is likely that for a model with fixed complexity, these paradigms require appreciably lower data quantities to achieve significant prediction accuracies, given their high speech intelligibility. Indeed, in the context of subject-specific models, several single-talker works have reported significant feature-specific contributions for higher-order features with as little as 12 minutes of data (e.g., Broderick et al, 2021; Gillis et al, 2022b). We chose to use a dual-talker paradigm in part because studies exploring neural correlates of speech perception difficulties are increasingly popular, making the present analyses more relevant to these investigators.…”