A Cochlear Implant Performance Prognostic Test Based on Electrical Field Interactions Evaluated by eABR (Electrical Auditory Brainstem Responses)

Abstract

Background: Cochlear implants (CIs) are neural prostheses that have been used routinely in the clinic over the past 25 years. They allow children who were born profoundly deaf, as well as adults affected by hearing loss for whom conventional hearing aids are insufficient, to attain a functional level of hearing. The "modern" CI (i.e., a multi-electrode implant using sequential coding strategies) has yielded good speech comprehension outcomes (recognition level for monosyllabic words about 50% to 60%, and sentence comprehension close to 90%). These good average results however hide a very important interindividual variability as scores in a given patients' population often vary from 5 to 95% in comparable testing conditions. Our aim was to develop a prognostic model for patients with unilateral CI. A novel method of objectively measuring electrical and neuronal interactions using electrical auditory brainstem responses (eABRs) is proposed.

Methods and findings: The method consists of two measurements: 1) eABR measurements with stimulation by a single electrode at 70% of the dynamic range (four electrodes distributed within the cochlea were tested), followed by a summation of these four eABRs; 2) Measurement of a single eABR with stimulation from all four electrodes at 70% of the dynamic range. A comparison of the eABRs obtained by these two measurements, defined as the monaural interaction component (MIC), indicated electrical and neural interactions between the stimulation channels. Speech recognition performance without lip reading was measured for each patient using a logatome test (64 "vowel-consonant-vowel"; VCV; by forced choice of 1 out of 16). eABRs were measured in 16 CI patients (CIs with 20 electrodes, Digisonic SP; Oticon Medical ®, Vallauris, France). Significant correlations were found between speech recognition performance and the ratio of the amplitude of the V wave of the eABRs obtained with the two measurements (Pearson's linear regression model, parametric correlation: r2 = 0.26, p<0.05).

Conclusions: This prognostic model allowed a substantial amount of the interindividual variance in speech recognition scores to be explained. The present study used measurements of electrical and neuronal interactions by eABR to assess patients' bio-electric capacity to use multiple information channels supplied by the implant. This type of prognostic information may be valuable in several ways. On the patient level, it allows customizing of individual treatments. ClinicalTrials.gov Identifier: NCT01805167.

Conflict of interest statement

Competing Interests: MH is an employee of Oticon Medical. This does not alter the authors' adherence to the PLOS ONE policies on sharing materials and data.

Figures

Fig 1. Schematic representation of the electrical interaction assessment method by eABR.

Left: low-interaction case. The sum of the four eABR measures obtained from individual stimulations (red) equals the eABR obtained with the multi-electrode stimulation (blue), the MIC value tends to 1. Right: high-interaction case: the eABR amplitude obtained from the multi-electrode stimulation tends to equal that of one eABR measure in the individual recording, the MIC tends to rise and constitutes a metric of the electrical interaction.

Fig 2. Grand-averaged eABRs obtained in the multi- and individual-electrode conditions.

Red: grand-averaged eABR (N = 16) obtained by summing the traces from the individual-electrodes recordings. Blue: grand-averaged eABR (N = 16) obtained from the same pulse-train, multi-electrodes stimulation. III: peak of wave-III. V: peak of wave-V.

Fig 3. Prognostic model of the speech recognition performance as a function of the electrical interaction measured by EABR.

Individually calculated MIC values compared to VCV scores. Triangles: the eight low speech performing patients (VCV score 40%). Orange curve: the decreasing exponential non-linear regression matching the data and following equation: MIC = 1 + 2.8e−0.04(VCVscore−6.25).