Neuroplasticity following cochlear implants

Resilience and vulnerability of neural speech tracking after hearing restoration

The role of early auditory experience in the development of neural speech tracking remains an open question. To address this issue, we measured neural speech tracking in children with or without functional hearing during their first year of life after their hearing was restored with cochlear implants (CIs), as well as in hearing controls (HC). Neural tracking in children with CIs is unaffected by the absence of perinatal auditory experience. CI users and HC exhibit a similar neural tracking magnitude at short timescales of brain activity. However, neural tracking is delayed in CI users, and its timing depends on the age of hearing restoration. Conversely, at longer timescales, speech tracking is dampened in participants using CIs, thereby accounting for their speech comprehension deficits. These findings highlight the resilience of sensory processing in speech tracking while also demonstrating the vulnerability of higher-level processing to the lack of early auditory experience.

Machine-Learning Predictions of Cochlear Implant Functional Outcomes: A Systematic Review

OBJECTIVES

Cochlear implant (CI) user functional outcomes are challenging to predict because of the variability in individual anatomy, neural health, CI device characteristics, and linguistic and listening experience. Machine learning (ML) techniques are uniquely poised for this predictive challenge because they can analyze nonlinear interactions using large amounts of multidimensional data. The objective of this article is to systematically review the literature regarding ML models that predict functional CI outcomes, defined as sound perception and production. We analyze the potential strengths and weaknesses of various ML models, identify important features for favorable outcomes, and suggest potential future directions of ML applications for CI-related clinical and research purposes.

DESIGN

We conducted a systematic literature search with Web of Science, Scopus, MEDLINE, EMBASE, CENTRAL, and CINAHL from the date of inception through September 2024. We included studies with ML models predicting a CI functional outcome, defined as those pertaining to sound perception and production, and excluded simulation studies and those involving patients without CIs. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we extracted participant population, CI characteristics, ML model, and performance data. Sixteen studies examining 5058 pediatric and adult CI users (range: 4 to 2489) were included from an initial 1442 publications.

RESULTS

Studies predicted heterogeneous outcome measures pertaining to sound production (5 studies), sound perception (12 studies), and language (2 studies). ML models use a variety of prediction features, including demographic, audiological, imaging, and subjective measures. Some studies highlighted predictors beyond traditional CI audiometric outcomes, such as anatomical and imaging characteristics (e.g., vestibulocochlear nerve area, brain regions unaffected by auditory deprivation), health system factors (e.g., wait time to referral), and patient-reported measures (e.g., dizziness and tinnitus questionnaires). Used ML models were tree-based, kernel-based, instance-based, probabilistic, or neural networks, with validation and test methods most commonly being k-fold cross-validation and train-test split. Various statistical measures were used to evaluate model performance, however, for studies reporting accuracy, the best-performing models for each study ranged from 71.0% to 98.83%.

CONCLUSIONS

ML models demonstrate high predictive performance and illuminate factors that contribute to CI user functional outcomes. While many models showed favorable evaluation statistics, the majority were not adequately reported with regard to dataset characteristics, model creation, and validation. Furthermore, the extent of overfitting in these models is unclear and will likely result in poor generalization to new data. This suggests the need for more robust validation procedures and standardization in reporting, with the ultimate hope that the iterative improvement of these models will allow for their adoption as a future clinical tool.

Intracranial electrophysiology of spectrally degraded speech in the human cortex

Introduction

Cochlear implants (CIs) are the treatment of choice for severe to profound hearing loss. Variability in CI outcomes remains despite advances in technology and is attributed in part to differences in cortical processing. Studying these differences in CI users is technically challenging. Spectrally degraded stimuli presented to normal-hearing individuals approximate input to the central auditory system in CI users. This study used intracranial electroencephalography (iEEG) to investigate cortical processing of spectrally degraded speech.

Methods

Participants were adult neurosurgical epilepsy patients. Stimuli were utterances /aba/ and /ada/, spectrally degraded using a noise vocoder (1-4 bands) or presented without vocoding. The stimuli were presented in a two-alternative forced choice task. Cortical activity was recorded using depth and subdural iEEG electrodes. Electrode coverage included auditory core in posteromedial Heschl's gyrus (HGPM), superior temporal gyrus (STG), ventral and dorsal auditory-related areas, and prefrontal and sensorimotor cortex. Analysis focused on high gamma (70-150 Hz) power augmentation and alpha (8-14 Hz) suppression.

Results

Chance task performance occurred with 1-2 spectral bands and was near-ceiling for clear stimuli. Performance was variable with 3-4 bands, permitting identification of good and poor performers. There was no relationship between task performance and participants demographic, audiometric, neuropsychological, or clinical profiles. Several response patterns were identified based on magnitude and differences between stimulus conditions. HGPM responded strongly to all stimuli. A preference for clear speech emerged within non-core auditory cortex. Good performers typically had strong responses to all stimuli along the dorsal stream, including posterior STG, supramarginal, and precentral gyrus; a minority of sites in STG and supramarginal gyrus had a preference for vocoded stimuli. In poor performers, responses were typically restricted to clear speech. Alpha suppression was more pronounced in good performers. In contrast, poor performers exhibited a greater involvement of posterior middle temporal gyrus when listening to clear speech.

Discussion

Responses to noise-vocoded speech provide insights into potential factors underlying CI outcome variability. The results emphasize differences in the balance of neural processing along the dorsal and ventral stream between good and poor performers, identify specific cortical regions that may have diagnostic and prognostic utility, and suggest potential targets for neuromodulation-based CI rehabilitation strategies.