A rapid, machine-learning readout of cochlear neural degeneration that transfers from animals to humans

Abstract

Standard hearing tests miss cochlear neural degeneration (CND), the loss of synapses between inner hair cells and auditory nerve fibers that may underlie unexplained hearing difficulty in roughly one in ten adults. Envelope following responses (EFRs) can index this neural loss noninvasively, but conventional protocols are too slow for clinical use. We tested a dynamic amplitude-modulated (dAM) EFR that sweeps the full modulation spectrum in a single brief stimulus. In Mongolian gerbils with histologically verified CND, dAM EFRs revealed selective deficits at fast modulation rates without threshold elevation. A machine-learning classifier distinguished young from middle-aged animals with high accuracy; additionally, its most informative feature, power near 400-500 Hz, tracked synapse counts, indicating a readout of cochlear neural health rather than age. Applied without retraining to 56 human listeners, the gerbil-trained classifier separated age groups above chance. dAM EFRs thus offer a rapid, objective and clinically viable marker of CND.