Biology subjects

Brunelle, D. L.

Publications and source records attributed to Brunelle, D. L..

2 recordsLinked to original sources

bioRxiv2026

Age-related hyperexcitability in the mouse inferior colliculus: evidence from sound-evoked local field potentials

The inferior colliculus (IC) is a major midbrain convergence site critical for processing complex sounds and undergoes fundamental changes with age-related hearing loss (ARHL). Local field potentials (LFPs) represent pre-synaptic integration of excitatory and inhibitory signals in local neural populations. Here, we assessed age-related changes in sound-evoked IC LFPs in the CBA/CaJ mouse model of ARHL across the tonotopic axis. We recorded from 495 sites across four age groups (young: 4-6 months; middle: 8-14 months; old: 24-25 months; oldest old: 27-31 months), examining LFP responses from dorsal (low-frequency), medial (mid-frequency), and ventral (high-frequency) IC regions. Analysis of the onset depolarization (N1 component) in response to broadband noise bursts revealed significant age-related hyperexcitability in medial and dorsal regions, with oldest old animals showing enhanced responses while ventral regions were unaffected. Amplitude-intensity functions demonstrated significant age x level interactions across all regions, with oldest old animals exhibiting level-dependent hyperexcitability most pronounced at suprathreshold intensities. Time-frequency analysis of LFP spectral content (30-100 Hz) revealed a crossover pattern across medial and dorsal regions, with oldest old animals showing reduced power at low-to-moderate stimulus levels but enhanced power at high stimulus levels. Ventral regions showed a low-level reduction without suprathreshold enhancement. These findings reveal paradoxical enhancement of neural activity in the aging IC, with the most pronounced changes in dorsal and medial regions rather than ventral regions most affected by peripheral hearing loss, suggesting that central hyperexcitability depends on residual afferent drive and implicating region-specific alterations in excitatory-inhibitory balance underlying central presbycusis.

neuroscience↗

bioRxiv2026

Validating the Gap-Startle Paradigm for Tinnitus Detection: A Machine Learning Approach in CBA/CaJ Mice

Tinnitus is one of the most common hearing disorders affecting one-third of Americans and is defined as the buzzing or ringing sound one perceives in one or both ears in the absence of an acoustic stimulus. One objective method for tinnitus screening in rodents is gap prepulse inhibition of the acoustic startle reflex (GPIAS), a reduction in the abrupt motor response elicited by an intense auditory stimulus following a silent gap in noise. Reduced inhibition by gaps embedded in narrow-band noise is hypothesized to reflect the primary tinnitus pitch, as the tinnitus "fills-in" the gap. However, Lobarinas et al. (2013) identified a critical limitation: after acoustic trauma or hearing loss, rodents often exhibit markedly diminished acoustic startle reflexes, creating a "floor effect" where further suppression becomes undetectable even if gap perception remains intact, leading to false-positive tinnitus screening results. To address this limitation, we utilized the CBA/CaJ mouse model to assess tactile airpuff modification efficacy and employed a machine learning algorithm to classify startle responses, achieving 98% accuracy in differentiating startles from non-startles. We induced unilateral conductive hearing loss via ear plugging and found enhanced gap detection ability, contrasting with false-positive tinnitus indicators observed in rats by Lobarinas and colleagues. We also pharmacologically induced tinnitus via sodium salicylate, revealing frequency-specific alterations in gap detection patterns. Our findings suggest that differences in data analysis methodology, specifically using a machine learning algorithm to filter non-startle responses, may explain species-specific differences between mouse and rat models and significantly improve GPIAS validity as a tinnitus screening assessment tool.

animal behavior and cognition↗