Unravelling the temporal properties of human eCAPs through an iterative deconvolution model

The electrically evoked compound action potential (eCAP) has been widely studied for its clinical value in evaluating cochlear implants (CIs). However, to date, single-fiber recordings have not been recorded from the human auditory nerve, and many unknowns remain about the firing properties that underlie the eCAP in patients with CIs. In particular, the temporal properties of auditory nerve fiber firing might contain valuable information that may be used to estimate the condition of the surviving auditory nerve fibers. This study aimed to evaluate the temporal properties of neural firing underlying human eCAPs with a new deconvolution model.

Assuming that each auditory nerve fiber produces the same unitary response (UR), the eCAP can be seen as a convolution of a UR with a compound discharge latency distribution (CDLD). We developed an iterative deconvolution model that derived a two-component Gaussian CDLD and a UR from recorded eCAPs. The choices were based on a deconvolution fitting error minimization routine (DMR). The DMR iteratively minimized the error between the recorded human eCAPs and the eCAPs simulated by the convolution of a parameterised UR and CDLD model (instead of directly deconvolving recorded eCAPs). Our new deconvolution model included two separate steps. In step one, the underlying URs of all eCAPs were derived, and the average of these URs was called the human UR. In step two, the CDLD was obtained by using the DMR in combination with the estimated human UR. With this model, we investigated the temporal firing properties of eCAPs by analysing the CDLDs, including the amplitudes, widths, peak latencies, and areas of CDLDs. The differences of the temporal properties in eCAPs between children and adults were explored. Finally, we validated the two-Gaussian component CDLD model with a multiple-Gaussian component CDLD model.

The estimated human UR contained a sharper, narrower negative component and a wider positive phase, compared to the previously described guinea pig UR. Furthermore, the eCAPs from humans could be predicted by the convolution of the human UR with a two-Gaussian component CDLD. The areas under CDLD (AUCD) reflected the number of excited nerve fibers over time. Both the CDLD magnitudes and AUCDs were significantly correlated with the eCAP amplitudes. Furthermore, different eCAPs with the same amplitude could lead to greatly different AUCDs. Significant differences of the temporal properties of eCAPs between children and adults were found. At last, the two-Gaussian component CDLD model was validated as the most optimal CDLD model.

This study described an iterative method that deconvolved human eCAPs into CDLDs, under the assumption that auditory nerve fibers had the same electrically evoked UR. Based on human eCAPs, we found a human UR that was different from the guinea pig UR. Furthermore, we found that CDLD characteristics revealed age-related temporal differences between human eCAPs. This temporal information may contain valuable clinical information on the survival and function of auditory nerve fibers. In turn, the surviving nerve condition might have prognostic value for speech outcomes in patients with CIs.