Speaker
Description
Although auditory evoked potentials (AEP) are widely used in clinical settings, the complex relationship between AEP morphology and cochlear status limits their diagnostic specificity. Computational models of AEPs grounded in cochlear mechanics can help link AEP morphology to underlying pathology. However, no validated framework has yet demonstrated this systematically across clinically relevant stimuli and pathologies.We developed an AEP modeling framework that combines a state-of-the-art computational model of the human auditory nerve (AN) with a convolution stage using a unitary response (UR) to simulate population-level responses. Simulated AEPs in healthy ears were validated against existing experimental datasets using transient and periodic stimuli presented across multiple stimulus intensities. We then simulated AEPs to standard audiological stimuli for four isolated pathology types – inner hair cell loss, outer hair cell loss, auditory nerve neuropathy, and myelinopathy – as well as for realistic profiles derived from human post-mortem histopathological data.This framework successfully simulated a broad range of AEPs across electrode configurations and captured stimulus- and level-dependent patterns observed experimentally in healthy peripheral auditory systems, despite some limitations in reproducing latency-level functions. The modeling approach thus enables systematic investigation of how different cochlear pathologies, such as neural or outer hair cell loss, manifest as distinct AEP patterns. These large simulated datasets can, in turn, provide a foundation for training machine-learning models to predict individual cochlear damage profiles from clinical AEP recordings.Together, these tools represent a step toward model-informed, electrophysiology-based precision diagnostics for cochlear hearing loss.