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Description
The Auditory Brainstem Response (ABR) is a far-field EEG signal recorded from scalp electrodes to assess the integrity of the auditory pathway. Elicited by brief acoustic stimuli, the ABR reflects the activation of successive auditory brainstem nuclei and constitutes one of the most widely used clinical tools for hearing assessment. Despite its extensive clinical use, the relationship between the characteristic ABR waveform and its underlying neural generators remains incompletely understood.
In this work, we present a multi-scale computational framework that reconstructs the complete transformation from acoustic stimulation to scalp potentials by coupling a physiologically realistic spiking neural network of the human auditory brainstem with a biophysically detailed forward model. Acoustic stimuli are first converted into auditory nerve activity through a realistic cochlear model and propagated through a large-scale point-neuron network reproducing binaural processing and the tonotopic organization of the auditory brainstem. The resulting spiking activity of each neuronal population is projected onto morphologically detailed multicompartment neuron models using the HybridLFPy framework.
HybridLFPy exploits the linearity of extracellular field generation by replaying presynaptic spike trains onto passive multicompartment reconstructions, allowing the computation of the transmembrane currents generated throughout the neuronal morphology. These distributed membrane currents constitute the physical sources of extracellular potentials and are used to estimate the current dipole moments of each brainstem nucleus. The resulting dipole contributions are subsequently combined and projected through an analytical multilayer spherical head model to reconstruct scalp EEG potentials directly comparable to clinically recorded ABR waveforms.
This hybrid approach combines the computational efficiency of large-scale point-neuron simulations with the biophysical accuracy of multicompartment current-source modeling, overcoming the limitations of either methodology alone. By integrating realistic auditory processing and extracellular forward modeling within a unified simulation pipeline, this work establishes a foundation for interpreting ABR waveforms and relating them to their underlying neural generators.
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