4.5 Article

Age-related changes in neuronal receptive fields of primary auditory cortex in frequency, amplitude, and temporal domains

Journal

HEARING RESEARCH
Volume 420, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.heares.2022.108504

Keywords

Age-related hearing loss; Receptive field; 3D rendering; Auditory cortex; Electrophysiology; Mouse

Funding

  1. Campbell McLaurin Chair for Hearing Deficiencies at the University of Calgary [274494]
  2. Canadian Institutes of Health Research [DG261338-2009]
  3. Natural Sciences and Engineering Research Council of Canada
  4. Markin Undergraduate Student Re-search Program in Health Wellness
  5. Campbell McLaurin Chair for Hearing Deficiencies at the University of Calgary
  6. Canadian Institutes of Health Research [274494]
  7. Natural Sciences and Engineering Research Council of Canada [DG261338-2009]
  8. Markin Undergraduate Student Re-search Program in Health Wellness

Ask authors/readers for more resources

Aging populations often experience age-related hearing loss, which is associated with decreases in hearing sensitivity and pathophysiological changes in the central auditory system. This study compared the receptive fields of young and old mice AI neurons in frequency, amplitude, and temporal domains. The results showed fundamental changes in the response properties of AI neurons in old mice, including lower frequency tuning, higher amplitude preference, broader frequency tuning, shorter dB range, longer response duration, and higher firing rate. The study suggests that age-related hearing loss has less impact on auditory information processing within optimal frequency and amplitude ranges.
Aging populations often experience difficulties hearing. This phenomenon, known as age-related hear-ing loss, is associated with decreases in hearing sensitivity and pathophysiological changes in the central auditory system, including the primary auditory cortex (AI). To better understand changes in the cen-tral auditory system, this study is designed to compare the receptive fields of AI neurons of young and old C57 mice in frequency, amplitude, and temporal domains by using random-delivery (various inter-val) of multi-frequency gamma-shaped tone pips and a novel data processing approach known as the intensity-stacked spectrotemporal receptive field (iSTRF). We show that the fundamental changes in the response properties of AI neurons in old mice were in line with the literature. Compared to young mice, AI neurons in old mice tuned to lower frequency and higher amplitude of tones with broader frequency tuning, shorter dB (dynamic) range, longer response duration, as well as a higher firing rate. We further compared the entire iSTRF and core iSTRF (above 50% maximal firing rate) between young and old mice; the core iSTRF represented the AI neuronal responses to sound that fall into the central frequency and amplitude ranges from the auditory periphery. It is remarkable that the difference was significant in the entire iSTRF while little difference was observed in the core iSTRFs between young and old mice. How-ever, the lower best frequency and higher best amplitude were obvious in both iSTRFs that were mostly determined by the deficits in the inner ears of old mice. In the frequency domain, the tuning quality factor of entire iSTRFs was significantly greater in old mice, while that of core iSTRFs was not compared to young mice. In the amplitude domain, significantly shorter dB range in old mice were observed in the entire iSTRF, but not in the core iSTRF. In the temporal domain, the response duration of old AI neurons was significantly longer in entire iSTRFs, but not in core iSTRFs, when compared to young AI neurons. Furthermore, the average firing rate of old AI neurons was significantly higher in entire iSTRF, but not in core iSTRF. It is also interesting that the ratio of the firing rate to background firing rate (signal-noise ra -tio) were similar between young and old AI neurons. Our data suggests that age-related hearing loss has relatively less impact on auditory information processing when sounds fall into the optimal frequency and amplitude ranges of given AI neurons. (c) 2022 Elsevier B.V. All rights reserved.

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