Electrocochleography and cognition are important predictors of speech perception outcomes in noise for cochlear implant recipients

Abstract

Although significant progress has been made in understanding outcomes following cochlear implantation, predicting performance remains a challenge. Duration of hearing loss, age at implantation, and electrode positioning within the cochlea together explain ~ 25% of the variability in speech-perception scores in quiet using the cochlear implant (CI). Electrocochleography (ECochG) responses, prior to implantation, account for 47% of the variance in the same speech-perception measures. No study to date has explored CI performance in noise, a more realistic measure of natural listening. This study aimed to (1) validate ECochG total response (ECochG-TR) as a predictor of performance in quiet and (2) evaluate whether ECochG-TR explained variability in noise performance. Thirty-five adult CI recipients were enrolled with outcomes assessed at 3-months post-implantation. The results confirm previous studies showing a strong correlation of ECochG-TR with speech-perception in quiet (r = 0.77). ECochG-TR independently explained 34% of the variability in noise performance. Multivariate modeling using ECochG-TR and Montreal Cognitive Assessment (MoCA) scores explained 60% of the variability in speech-perception in noise. Thus, ECochG-TR, a measure of the cochlear substrate prior to implantation, is necessary but not sufficient for explaining performance in noise. Rather, a cognitive measure is also needed to improve prediction of noise performance.

Exploring neurocognitive factors and brain activation in adult cochlear implant recipients associated with speech perception outcomes—A scoping review

“…(3) The Montreal Cognitive Assessment (MoCA), which was included in a multivariate model explaining variance in sentence perception performance in noise. Together with neuronal health measures, MoCA explained 64.5% of this variance (β = 2.06, p < 0.05, df = 29) ( Walia et al, 2022 ). (4) The clock drawing test, a short subtest of SAGE, which did not show a significant relationship with word perception in quiet ( r 2 = 0.177, p = 0.058, N = 15) ( Zucca et al, 2022 ).…”

“…Measuring all four possible conditions might also be important to create a general classification system for better and poorer performers, which in turn can help to better generalize results. For example, it has been observed that poorer performers in quiet are poorer performers in noise, but better performers in quiet might be poorer performers in noise ( Walia et al, 2022 ). Understanding the underlying causes leading to either poor performance in quiet or noise is needed, as this might lead toward different treatment options.…”

Longitudinal Electrocochleography as an Objective Measure of Serial Behavioral Audiometry in Electro-Acoustic Stimulation Patients

“…Our present study provides a new application of post-operative ECoG in monitoring residual hearing and understanding the pathophysiology of delayed hearing loss. In a future study, we will explore further applications of post-operative ECoG, including the relationship between post-operative ECoG and speech perception, as most data in the literature focuses interoperative ECoG at time of surgery and correlating that with outcome measures (Fontenot et al, 2019, Canfarotta et al, 2021; Walia et al, 2022). In addition, while our measures were conducted using in-house custom-built equipment, cochlear implant companies are also designing and implementing hardware and software adaptations so that their own systems can be used for ECoG recordings.…”

“…CM and ANN thresholds correlate strongly with behavioral audiometric thresholds (Abbas et al, 2017; Koka et al, 2017), which can aid with programming the hearing aid portion of the EAS sound processor (Agrawal et al, 2021). Intraoperative measures of ECoG are used to guide cochlear implantation to minimize cochlear trauma (Bester et al, 2022; Lenarz et al, 2022) and have been shown to correlate with post-operative speech understanding (Fontenot et al, 2019; Canfarotta et al, 2021; Walia et al, 2022). One important aspect of ECoG that hasn’t been sufficiently validated is the long-term stability of these measures.…”

External Validation of Cochlear Implant Screening Tools Demonstrates Modest Generalizability

“…Such tools would enable more personalized care and empower greater shared decision making between patient and provider. We are also using data from a cloud-based CI registry that seamlessly integrates into clinical workflow to identify new prediction factors that may affect CI candidacy, like baseline cognition, into the screening tool (39). Using a cloud-based CI registry is advantageous because it has the potential to automate broad data collection over time from diverse patient populations, addressing issues of accuracy and generalizability that limit current screening tools.…”