pupillometry-to-assess-auditory-sensation-in-guinea-pigs

Pupillometry to Assess Auditory Sensation in Guinea Pigs

Noise exposure is a leading cause of sensorineural hearing loss. Animal models of noise-induced hearing loss have generated mechanistic insight into the ...

University of Pittsburgh

Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs (GPs) on call categorization tasks using natural calls. We then tested categorization by the model and GPs using temporally and spectrally altered calls. Both the model and GPs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, the model predicted about 50% of the variance in GP behavior. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.

Vocalization categorization behavior explained by a feature-based auditory categorization model.

For gaining insight into general principles of auditory processing, it is critical to choose model organisms whose set of natural behaviors encompasses the processes being investigated. This reasoning has led to the development of a variety of animal models for auditory neuroscience research, such as guinea pigs, gerbils, chinchillas, rabbits, and ferrets; but in recent years, the availability of cutting-edge molecular tools and other methodologies in the mouse model have led to waning interest in these unique model species. As laboratories increasingly look to include in-vivo components in their research programs, a comprehensive description of procedures and techniques for applying some of these modern neuroscience tools to a non-mouse small animal model would enable researchers to leverage unique model species that may be best suited for testing their specific hypotheses. In this manuscript, we describe in detail the methods we have developed to apply these tools to the guinea pig animal model to answer questions regarding the neural processing of complex sounds, such as vocalizations. We describe techniques for vocalization acquisition, behavioral testing, recording of auditory brainstem responses and frequency-following responses, intracranial neural signals including local field potential and single unit activity, and the expression of transgenes allowing for optogenetic manipulation of neural activity, all in awake and head-fixed guinea pigs. We demonstrate the rich datasets at the behavioral and electrophysiological levels that can be obtained using these techniques, underscoring the guinea pig as a versatile animal model for studying complex auditory processing. More generally, the methods described here are applicable to a broad range of small mammals, enabling investigators to address specific auditory processing questions in model organisms that are best suited for answering them.

Updates to the guinea pig animal model for in-vivo auditory neuroscience in the low-frequency hearing range.

Time-varying pitch is a vital cue for human speech perception. Neural processing of time-varying pitch has been extensively assayed using scalp-recorded frequency-following responses (FFRs), an electrophysiological signal thought to reflect integrated phase-locked neural ensemble activity from subcortical auditory areas. Emerging evidence increasingly points to a putative contribution of auditory cortical ensembles to the scalp-recorded FFRs. However, the properties of cortical FFRs and precise characterization of laminar sources are still unclear. Here we used direct human intracortical recordings as well as extracranial and intracranial recordings from macaques and guinea pigs to characterize the properties of cortical sources of FFRs to time-varying pitch patterns. We found robust FFRs in the auditory cortex across all species. We leveraged representational similarity analysis as a translational bridge to characterize similarities between the human and animal models. Laminar recordings in animal models showed FFRs emerging primarily from the thalamorecipient layers of the auditory cortex. FFRs arising from these cortical sources significantly contributed to the scalp-recorded FFRs via volume conduction. Our research paves the way for a wide array of studies to investigate the role of cortical FFRs in auditory perception and plasticity.

Article	Year
Vocalization categorization behavior explained by a feature-based auditory categorization model. eLife\| PubMed ID: 36226815	2022
Updates to the guinea pig animal model for in-vivo auditory neuroscience in the low-frequency hearing range. Hearing research\| PubMed ID: 36099806	2022
Frequency-Following Responses to Speech Sounds Are Highly Conserved across Species and Contain Cortical Contributions. eNeuro\| PubMed ID: 34799409	2021

Marianny Pernia

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