Cochlear implants restore hearing in patients with severe to profound deafness by delivering electrical stimuli inside the cochlea. Understanding stimulus current spread, and how it correlates to patient-dependent factors, is hampered by the poor accessibility of the inner ear and by the lack of clinically-relevant in vitro, in vivo or in silico models. Here, we present 3D printing-neural network co-modelling for interpreting electric field imaging profiles of cochlear implant patients. With tuneable electro-anatomy, the 3D printed cochleae can replicate clinical scenarios of electric field imaging profiles at the off-stimuli positions. The co-modelling framework demonstrated autonomous and robust predictions of patient profiles or cochlear geometry, unfolded the electro-anatomical factors causing current spread, assisted on-demand printing for implant testing, and inferred patients’ in vivo cochlear tissue resistivity (estimated mean = 6.6 kΩcm). We anticipate our framework will facilitate physical modelling and digital twin innovations for neuromodulation implants.

Objectives: Extracochlear electrodes in cochlear implants (CI), defined as individual electrodes on the electrode array located outside of the cochlea, are not a rare phenomenon. The presence of extracochlear electrodes frequently goes unnoticed and could result in them being assigned stimulation frequencies that are either not delivered to, or stimulating neurons that overlap with intracochlear electrodes, potentially reducing performance. The current gold-standard for detection of extracochlear electrodes is computed tomography (CT), which is time-intensive, costly and involves radiation. It is hypothesized that a collection of Stimulation-Current-Induced Non-Stimulating Electrode Voltage recordings (SCINSEVs), commonly referred to as “transimpedance measurements (TIMs)” or electric field imaging (EFI), could be utilized to detect extracochlear electrodes even when contact impedances are low. An automated analysis tool is introduced for detection and quantification of extracochlear electrodes. Design: Eight fresh-frozen human cadaveric heads were implanted with the Advanced Bionics HiRes90K with a HiFocus 1J lateral-wall electrode. The cochlea was flushed with 1.0% saline through the lateral semicircular canal. Contact impedances and SCINSEVs were recorded for complete insertion and for 1 to 5 extracochlear electrodes. Measured conditions included: air in the middle ear (to simulate electrodes situated in the middle ear), 1.0% saline in the middle ear (to simulate intraoperative conditions with saline or blood in the middle ear), and soft tissue (temporal muscle) wrapped around the extracochlear electrodes (to simulate postoperative soft-tissue encapsulation of the electrodes). Intraoperative SCINSEVs from patients were collected, for clinical purposes during slow insertion of the electrode array, as well as from a patient postoperatively with known extracochlear electrodes. Results: Full insertion of the cochlear implant in the fresh-frozen human cadaveric heads with a flushed cochlea resulted in contact impedances in the range of 6.06 ± 2.99 kΩ (mean ± 2SD). Contact impedances were high when the extracochlear electrodes were located in air, but remained similar to intracochlear contact impedances when in saline or soft tissue. SCINSEVs showed a change in shape for the extracochlear electrodes in air, saline, and soft tissue. The automated analysis tool showed a specificity and sensitivity of 100% for detection of two or more extracochlear electrodes in saline and soft tissue. The quantification of two or more extracochlear electrodes was correct for 84% and 81% of the saline and soft tissue measurements, respectively. Conclusions: Our analysis of SCINSEVs (specifically the EFIs from this manufacturer) shows good potential as a detection tool for extracochlear electrodes, even when contact impedances remain similar to intracochlear values. SCINSEVs could potentially replace CT in the initial screening for extracochlear electrodes. Detecting migration of the electrode array during the final stages of surgery could potentially prevent re-insertion surgery for some CI users. The automated detection tool could assist in detection and quantification of two or more extracochlear electrodes.

Variations in neural health along the cochlea can degrade the spectral and temporal representation of sounds conveyed by cochlear implants (CIs). We evaluated and compared one electrophysiological measure and two behavioural measures that have been proposed as estimates of neural health patterns, in order to explore the extent to which the different measures provide converging and consistent neural health estimates. All measures were obtained from the same 11 users of the Cochlear Corporation CI. The two behavioural measures were multipulse integration (MPI) and the polarity effect (PE), both measured on each of seven electrodes per subject. MPI was measured as the difference between thresholds at 80 pps and 1000 pps, and PE as the difference in thresholds between cathodic- and anodic-centred quadraphasic (QP) 80-pps pulse trains. It has been proposed that good neural health corresponds to a large MPI and to a large negative PE (lower thresholds for cathodic than anodic pulses). The electrophysiological measure was the effect of interphase gap (IPG) on the offset of the ECAP amplitude growth function (AGF), which has been correlated with spiral ganglion neuron density in guinea pigs. This ‘IPG offset’ was obtained on the same subset of electrodes used for the behavioural measures. Despite high test–retest reliability, there were no significant correlations between the neural health estimates for either within-subject comparisons across the electrode array, or between-subject comparisons of the means. A phenomenological model of a population of spiral ganglion neurons was then used to investigate physiological mechanisms that might underlie the different neural health estimates. The combined experimental and modelling results provide evidence that PE, MPI and IPG offset may reflect different characteristics of the electrode-neural interface.

Goal: Advances in computational models of biological systems and artificial neural networks enable rapid virtual prototyping of neuroprostheses, accelerating innovation in the field. Here, we present an end-to-end computational model for predicting speech perception with cochlear implants (CI), the most widely-used neuroprosthesis. Methods: The model integrates CI signal processing, a finite element model of the electrically-stimulated cochlea, and an auditory nerve model to predict neural responses to speech stimuli. An automatic speech recognition neural network is then used to extract phoneme-level speech perception from these neural response patterns. Results: Compared to human CI listener data, the model predicts similar patterns of speech perception and misperception, captures between-phoneme differences in perceptibility, and replicates effects of stimulation parameters and noise on speech recognition. Information transmission analysis at different stages along the CI processing chain indicates that the bottleneck of information flow occurs at the electrode-neural interface, corroborating studies in CI listeners. Conclusion: An end-to-end model of CI speech perception replicated phoneme-level CI speech perception patterns, and was used to quantify information degradation through the CI processing chain. Significance: This type of model shows great promise for developing and optimizing new and existing neuroprostheses.

To establish outcomes following cochlear implantation (CI) in patients with postsynaptic auditory neuropathy (AN). Systematic review and narrative synthesis. Databases searched: MEDLINE, PubMed, EMBASE, Web of Science, Cochrane Collection and ClinicalTrials.gov. No limits placed on language or year of publication. Review conducted in accordance with the PRISMA statement. Searches identified 98 studies in total, of which 14 met the inclusion criteria reporting outcomes in 25 patients with at least 28 CIs. Of these, 4 studies focused on Charcot-Marie-Tooth disease (CMT), 3 on Brown-Vialetto-Van-Laere syndrome (BVVL), 2 on Friedreich Ataxia (FRDA), 2 on Syndromic dominant optic atrophy (DOA+), 2 on Cerebellar ataxia - areflexia - pes cavus - optic atrophy - sensorineural hearing loss (CAPOS) syndrome, and 1 on Deafness-dystonia-optic neuronopathy (DDON) syndrome. All studies were Oxford Centre for Evidence Based Medicine (OCEBM) grade IV. Overall trend was towards good post-CI outcomes with 22 of the total 25 patients displaying modest to significant benefit. Hearing outcomes following CI in postsynaptic ANs are variable but generally good with patients showing improvements in hearing thresholds and speech perception. In the future, development of a clearer stratification system into pre, post, and central AN would have clinical and academic benefits. Further research is required to understand AN pathophysiology and develop better diagnostic tools for more accurate identification of lesion sites. Multicenter longitudinal studies with standardized comprehensive outcome measures including health-related quality of life data will be key in establishing a better understanding of short and long-term post-CI outcomes. Cite this article as: Chaudhry D, Chaudhry A, Muzaffar J, Monksfield P, Bance M. Cochlear Implantation Outcomes in Post Synaptic Auditory Neuropathies: A Systematic Review and Narrative Synthesis. J Int Adv Otol 2020; 16(3): 411-31.

Summarise outcomes following cochlear implantation (CI) in patients with post-meningitis deafness. Systematic review and narrative synthesis. Databases searched: Medline, Pubmed, Embase, Web of Science, Cochrane Collection and ClinicalTrials.gov. No limits placed on language or year of publication. Studies with a minimum of 20 individuals with post-meningitis deafness were included. Review conducted in accordance with the PRISMA statement. Searches identified 906 abstracts and 291 full texts. Of these, 19 studies met the inclusion criteria, reporting outcomes in 610 patients with 650 implants. Audiological outcomes improved across all studies following cochlear implantation. 7 studies demonstrated a statistically significant difference between pre and post-CI outcomes. Patients with no cochlear ossification, full electrode insertion, shorter duration of deafness and no neurological sequelae generally appeared to perform best. A total of 31 minor and 19 major complications were reported, with 15 cases of reimplantation. The methodological quality of the included studies was sufficient, predominantly consisting of cohort studies. 15 studies were OCEBM grade III and 4 studies were OCEBM grade IV. All studies had a minimum of 20 individuals with post-meningitic deafness and used multi-channel cochlear implant devices. Audiological outcomes following cochlear implantation in meningitis are satisfactory, providing functional levels of speech perception and intelligibility. Improvement in hearing is dependent on the amount of cochlear ossification, duration of deafness prior to implantation, electrode insertion depth and presence of neurological sequalae. Cochlear implantation in meningitis patients can be challenging due to the presence of ossification and inaccuracies of pre-operative imaging. Therefore, early and bilateral implantation is recommended in all patients with post-meningitis hearing loss to improve the likelihood of full electrode insertion. Cite this article as: Singhal K, Singhal J, Muzaffar J, Monksfield P, Bance M. Outcomes of Cochlear Implantation in Patients with Post-Meningitis Deafness: A Systematic Review and Narrative Synthesis. J Int Adv Otol 2020; 16(3): 395-410.