Exaggerated Cortical Representation of Speech in Older Listeners: Mutual Information Analysis

Zan, Peng; Presacco, Alessandro; Anderson, Samira; Simon, Jonathan Z.

doi:10.1101/2019.12.18.881334

Cited by 5 publications

(13 citation statements)

References 67 publications

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“…Note that the mutual information method does not need to be applied between stimulus and EEG necessarily, but can also be applied between different EEG signals to get additional insights. This technique has been used by Gross et al (2014), Zan et al (2020), and Kaufeld et al (2020).…”

Section: Methods To Measure Neural Trackingmentioning

confidence: 99%

“…The most common approach to do so is based on linear encoding/decoding models. Other response analysis methods exist, including inter-trial coherence (ITC) (Zion Golumbic et al, 2013; Bourguignon et al, 2020), cross-correlation (Kong et al, 2014; Aiken and Picton, 2008; Petersen et al, 2016), mutual information (Gross et al, 2014; Zan et al, 2020; Kaufeld et al, 2020) and neural networks (Katthi et al, 2020; Accou et al, 2021), but these will not be discussed further.…”

Section: The Neural Tracking Frameworkmentioning

confidence: 99%

“…The copyright holder for this this version posted November 27, 2021. ; https://doi.org/10. 1101/2021.11.26.470129 doi: bioRxiv preprint Zan et al, 2020;Kaufeld et al, 2020) and neural networks (Katthi et al, 2020;Accou et al, 2021), but these will not be discussed further.…”

Section: Introductionmentioning

confidence: 99%

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Neural tracking as a diagnostic tool to assess the auditory pathway

Canneyt

Gillis

Vanthornhout

et al. 2021

Preprint

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The neural tracking framework enables the analysis of neural responses (EEG) to continuous natural speech, e.g., a story or a podcast. This allows for objective investigation of a range of auditory and linguistic processes in the brain during natural speech perception. This approach is more ecologically valid than traditional auditory evoked responses and has great potential for both research and clinical applications. In this article, we review the neural tracking framework and highlight three prominent examples of neural tracking analyses. This includes the neural tracking of the fundamental frequency of the voice (f0), the speech envelope and linguistic features. Each of these analyses provides a unique point of view into the hierarchical stages of speech processing in the human brain. f0-tracking assesses the encoding of fine temporal information in the early stages of the auditory pathway, i.e. from the auditory periphery up to early processing in the primary auditory cortex. This fundamental processing in (mostly) subcortical stages forms the foundation of speech perception in the cortex. Envelope tracking reflects bottom-up and top-down speech-related processes in the auditory cortex, and is likely necessary but not sufficient for speech intelligibility. To study neural processes more directly related to speech intelligibility, neural tracking of linguistic features can be used. This analysis focuses on the encoding of linguistic features (e.g. word or phoneme surprisal) in the brain. Together these analyses form a multi-faceted and time-effective objective assessment of the auditory and linguistic processing of an individual.

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Section: Methods To Measure Neural Trackingmentioning

confidence: 99%

Section: The Neural Tracking Frameworkmentioning

confidence: 99%

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Neural tracking as a diagnostic tool to assess the auditory pathway

Canneyt

Gillis

Vanthornhout

et al. 2021

Preprint

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show abstract

“…The enhanced M50 TRF in older adults also agrees with the integration window results above, that the exaggerated representation starts even at early cortical processing stages. Larger early cortical peaks (~50 ms latency) in older adults have been reported using both EEG (McCullagh and Shinn 2013; Roque et al 2019b) and MEG (Brodbeck et al 2018; Zan et al 2020), for both speech in quiet and in noisy conditions. Alain et al (2014) suggested that this increased neural activity may be caused by excitatory and inhibitory imbalance, which is further in agreement with animal studies (McCullagh and Shinn 2013), and is consistent with other studies (Brodbeck et al 2018).…”

Section: Discussionmentioning

confidence: 94%

Effects of Aging on Cortical Representations of Continuous Speech

Karunathilake

Dunlap

Perera

et al. 2022

Preprint

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Understanding speech in a noisy environment is crucial in day-to-day interactions, and yet becomes more challenging with age, even for healthy aging. Age-related changes in the neural mechanisms that enable speech-in-noise listening have been investigated previously; however, the extent to which age affects the timing and fidelity of encoding of target and interfering speech streams are not well understood. Using magnetoencephalography (MEG), we investigated how continuous speech is represented in auditory cortex in the presence of interfering speech, in younger and older adults. Cortical representations were obtained from neural responses that time-locked to the speech envelopes using speech envelope reconstruction and temporal response functions (TRFs). TRFs showed three prominent peaks corresponding to auditory cortical processing stages: early (~50 ms), middle (~100 ms) and late (~200 ms). Older adults showed exaggerated speech envelope representations compared to younger adults. Temporal analysis revealed both that the age-related exaggeration starts as early as ~50 ms, and that older adults needed a substantially longer integration time window to achieve their better reconstruction of the speech envelope. As expected, with increased speech masking, envelope reconstruction for the attended talker decreased and all three TRF peaks were delayed, with aging contributing additionally to the reduction. Interestingly, for older adults the late peak was delayed, suggesting that this late peak may receive contributions from multiple sources. Together these results suggest that there are several mechanisms at play compensating for age-related temporal processing deficits at several stages, but which are not able to fully reestablish unimpaired speech perception.NEW & NOTEWORTHYOlder adults’ difficulty understanding speech in noise may be related to age-related changes in cortical temporal processing. Using magnetoencephalography to record responses of listeners under different noise conditions, we investigated both timing and strength of the cortical representation of continuous speech at several cortical processing stages. The representation at each stage depends differently on noise level and selective attention, and in different ways for older listeners, even with normal hearing, than younger.

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“…One interesting use case for measures that overcome the problems outlined above is the heavily studied phenomenon of speech envelope tracking as observed in magneto-and electroencephalography (henceforth MEEG to denote both modalities) recordings (Ahissar et al, 2001;Hertrich et al, 2012;Gross et al, 2013;Ding & Simon, 2012;O'Sullivan et al, 2015;Di Liberto et al, 2015;Wöstmann et al, 2017;Brodbeck et al, 2018;Daube et al, 2019;Obleser & Kayser, 2019;Zan et al, 2020;Donhauser & Baillet, 2020). In short, the low-frequency portion of MEEG signals is reliably related to the time varying energy of the speech signal at a certain delay.…”

Section: Introductionmentioning

confidence: 99%

A whitening approach for Transfer Entropy permits the application to narrow-band signals

Daube¹,

Groß²,

Ince³

2022

Preprint

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Transfer Entropy, a generalisation of Granger Causality, promises to measure "information transfer" from a source to a target signal by ignoring self-predictability of a target signal when quantifying the source-target relationship. A simple example for signals with such selfpredictability are narrowband signals. These are both thought to be intrinsically generated by the brain as well as commonly dealt with in analyses of brain signals, where band-pass filters are used to separate responses from noise. However, the use of Transfer Entropy is usually discouraged in such cases. We simulate simplistic examples where we confirm the failure of classic implementations of Transfer Entropy when applied to narrow-band signals, as made evident by a flawed recovery of effect sizes and interaction delays. We propose an alternative approach based on a whitening of the input signals before computing a bivariate measure of directional time-lagged dependency. This approach solves the problems found in the simple simulated systems. Finally, we explore the behaviour of our measure when applied to delta and theta response components in Magnetoencephalography (MEG) responses to continuous speech. The small effects that our measure attributes to a directed interaction from the stimulus to the neuronal responses are stronger in the theta than in the delta band. This suggests that the delta band reflects a more predictive coupling, while the theta band is stronger involved in bottom-up, reactive processing. Taken together, we hope to increase the interest in directed perspectives on frequency-specific dependencies.

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Exaggerated Cortical Representation of Speech in Older Listeners: Mutual Information Analysis

Cited by 5 publications

References 67 publications

Neural tracking as a diagnostic tool to assess the auditory pathway

Neural tracking as a diagnostic tool to assess the auditory pathway

Effects of Aging on Cortical Representations of Continuous Speech

A whitening approach for Transfer Entropy permits the application to narrow-band signals

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