Afferent-efferent connectivity between auditory brainstem and cortex accounts for poorer speech-in-noise comprehension in older adults

Bidelman, Gavin M.; Price, Caitlin N.; Shen, Dawei; Arnott, Stephen R.; Alain, Claude

doi:10.1016/j.heares.2019.107795

Cited by 45 publications

(71 citation statements)

References 113 publications

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“…Our data corroborate previous studies showing speech-ERPs are higher in amplitude for clear compared to noise-degraded speech detection and HI compared to NH listeners, consistent with the effects of hearing loss and background noise on auditory cortical responses Bidelman et al, 2014Bidelman et al, , 2019b. Extending previous work, we used these ERP attributes in SVM classification to assess the time-course of the brain's response to speech and their ability to segregate normal and hearing-impaired listeners.…”

Section: Hearing Status Is Decoded Early Within the Time-course Of Spsupporting

confidence: 87%

“…In both conditions (clear and noise-degraded) HI generally showed higher ERPs than NH (Figure 2). A detailed analysis of the ERPs is reported elsewhere (Bidelman et al, 2019b).…”

Section: Erps In Hi Vs Nh Older Adultsmentioning

confidence: 99%

“…Speech processing in the elderly relies on a complex network of interacting brain regions (Hickok andPoeppel, 2004, 2007;Rauschecker and Scott, 2009;Bidelman et al, 2019a). Age-related HI is thought to occur due to a myriad of changes in both the central auditory pathways (Bidelman et al, 2014(Bidelman et al, , 2019b and widespread areas of both cerebral hemispheres (Gates and Mills, 2005). For example, studies have shown aged-related declines in the temporal precision (Roque et al, 2019) of (subcortical) neural encoding (Anderson et al, 2012;Konrad-Martin et al, 2012;Bidelman et al, 2014;Schoof and Rosen, 2016) and functional magnetic resonance imaging (fMRI) has shown older adults have greater activation than younger adults in widespread cortical brain regions (Du et al, 2016;Mudar and Husain, 2016;Diaz et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Evidence suggests that older adults incorporate more attentional resources than younger adults in auditory perceptual tasks (Alain et al, 2004;Grady, 2008). This could account for some of the age-related increases in ERP amplitudes reported in HI vs. normal hearing (NH) listeners Bidelman et al, 2019b). Prior neuroimaging studies (Peelle et al, 2009;Wong et al, 2009;Erb and Obleser, 2013;Vaden et al, 2015) have also demonstrated increased activity in prefrontal regions related to cognitive control, attention, and working memory when older listeners process speech under challenging situations.…”

Section: Introductionmentioning

confidence: 99%

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Decoding Hearing-Related Changes in Older Adults’ Spatiotemporal Neural Processing of Speech Using Machine Learning

et al. 2020

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Decoding Hearing Loss via EEG (clear speech); whereas 16 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (78.7% accuracy). Our results identify critical time-courses and brain regions that distinguish mild hearing loss from normal hearing in older adults and confirm a larger number of active areas, particularly in RH, when processing noise-degraded speech information.

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Section: Hearing Status Is Decoded Early Within the Time-course Of Spsupporting

confidence: 87%

“…In both conditions (clear and noise-degraded) HI generally showed higher ERPs than NH (Figure 2). A detailed analysis of the ERPs is reported elsewhere (Bidelman et al, 2019b).…”

Section: Erps In Hi Vs Nh Older Adultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Decoding Hearing-Related Changes in Older Adults’ Spatiotemporal Neural Processing of Speech Using Machine Learning

et al. 2020

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“…However, as they are correlations based on data across different participants, it is not known whether such correlations reflect cortical-subcortical connectivity within individuals, whether connectivity is afferent or efferent, and how the connectivity is associated with age or SPiN. It is necessary in the upcoming work to develop effective methods to quantify the strength and direction of cortical-subcortical interactions (directional phase relations between cortical responses and FFRs, e.g., Bidelman et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Relationship between neural phase-locked responses to speech and perception of speech in noise in young and older adults

Mai

Howell

2019

Preprint

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11Auditory phase-locked responses are affected by aging and it has been proposed that this increases 12 the challenges experienced during speech perception in noise (SPiN). However, the proposal lacks 13 direct support. This issue was addressed by measuring speech-evoked phase-locked responses at 14 subcortical (frequency-following responses, FFRs) and cortical (theta-band phase-locking, θ-PLV) 15 levels, and studying the relationship between the phase-locked responses and SPiN (word report 16 accuracies in spoken sentences in noise) in adults across a wide age-range (19-75 years old). It was 17 found that: (1) FFR magnitudes declined with age after hearing loss was controlled for; (2) θ-PLV 18 increased with age, suggesting cortical hyperexcitability in audition; (3) SPiN correlated positively 19 with FFR magnitudes obtained in quiet and with θ-PLV obtained in noise, suggesting that impacts 20 of age effects (smaller FFR magnitudes and greater θ-PLV) on SPiN perception differ at subcortical 21 and cortical levels. The current study thus provides evidence for different mechanisms at subcortical 22 and cortical levels by which age affects speech-evoked phase-locked activities and SPiN.23

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Age-related hearing loss increases full-brain connectivity while reversing directed signaling within the dorsal–ventral pathway for speech

et al. 2019

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Speech comprehension difficulties are ubiquitous to aging and hearing loss, particularly in noisy environments. Older adults' poorer speech-in-noise (SIN) comprehension has been related to abnormal neural representations within various nodes (regions) of the speech network, but how senescent changes in hearing alter the transmission of brain signals remains unspecified. We measured electroencephalograms in older adults with and without mild hearing loss during a SIN identification task. Using functional connectivity and graph-theoretic analyses, we show that hearing-impaired (HI) listeners have more extended (less integrated) communication pathways and less efficient information exchange among widespread brain regions (larger network eccentricity) than their normal-hearing (NH) peers. Parameter optimized support vector machine classifiers applied to EEG connectivity data showed hearing status could be decoded (> 85% accuracy) solely using network-level descriptions of brain activity, but classification was particularly robust using left hemisphere connections. Notably, we found a reversal in directed neural signaling in left hemisphere dependent on hearing status among specific connections within the dorsalventral speech pathways. NH listeners showed an overall net "bottom-up" signaling directed from auditory cortex (A1) to inferior frontal gyrus (IFG; Broca's area), whereas the HI group showed the reverse signal (i.e., "top-down" Broca's → A1). A similar flow reversal was noted between

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Afferent-efferent connectivity between auditory brainstem and cortex accounts for poorer speech-in-noise comprehension in older adults

Cited by 45 publications

References 113 publications

Decoding Hearing-Related Changes in Older Adults’ Spatiotemporal Neural Processing of Speech Using Machine Learning

Decoding Hearing-Related Changes in Older Adults’ Spatiotemporal Neural Processing of Speech Using Machine Learning

Relationship between neural phase-locked responses to speech and perception of speech in noise in young and older adults

Age-related hearing loss increases full-brain connectivity while reversing directed signaling within the dorsal–ventral pathway for speech

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