Auditory brainstem response abnormalities in autism spectrum disorder: A deep learning approach to characterize...

While Auditory Brainstem Response (ABR) provides a non-invasive window into auditory brainstem function, prior studies of ASD have primarily focused on localized waveform features (e.g., waves I, III, and V), potentially overlooking subtle but informative patterns in the full-band signal. This study introduces a deep learning framework to comprehensively characterize time-frequency signatures of auditory brainstem activity in ASD, with the goal of identifying neurophysiologically meaningful ABR features associated with ASD.

We analyzed a clinical dataset of 1209 ABR recordings (ASD: 961; Typically Developing Controls: 248). A dual-branch Time-Frequency Fusion and Transformer-Based Network (TF-TBN) was developed. The temporal branch utilizes a Transformer-enhanced 1D-CNN to analyze raw ABR waveforms, while the frequency branch employs a Vision Transformer to analyze spectrograms generated via Continuous Wavelet Transform. A fusion module integrates these features for final classification. Model interpretability was analyzed to identify critical ABR features.

The TF-TBN model achieved a classification accuracy of 96.62%, significantly outperforming conventional deep learning baselines. Interpretability analysis revealed that the model's decision was heavily influenced by prolonged absolute latencies of waves III and V, and interpeak latencies of I-III and I-V, which were confirmed as statistically significant in the ASD cohort. This suggests that the model successfully learned biologically plausible biomarkers of auditory pathway dysfunction.

This study provides the first comprehensive characterization of full-band ABR abnormalities in ASD using a deep learning framework. The TF-TBN model identifies prolonged wave III- and wave V-related timing features as prominent contributors to ASD-TD discrimination, with wave III-related delay emerging as an important component of the observed ABR abnormality. By linking AI-driven feature discovery to interpretable neurophysiological biomarkers, our work advances the analytical framework for ABR and contributes to understanding the neural basis of auditory processing deficits in ASD.