28 research outputs found
High Fidelity Bioelectric Modelling of the Implanted Cochlea
Cochlear implants are medical devices that can restore sound perception in individuals with sensorineural hearing loss (SHL). Since their inception, improvements in performance have largely been driven by advances in signal processing, but progress has plateaued for almost a decade. This suggests that there is a bottleneck at the electrode-tissue interface, which is responsible for enacting the biophysical changes that govern neuronal recruitment. Understanding this interface is difficult because the cochlea is small, intricate, and difficult to access. As such, researchers have turned to modelling techniques to provide new insights. The state-of-the-art involves calculating the electric field using a volume conduction model of the implanted cochlea and coupling it with a neural excitation model to predict the response. However, many models are unable to predict patient outcomes consistently. This thesis aims to improve the reliability of these models by creating high fidelity reconstructions of the inner ear and critically assessing the validity of the underlying and hitherto untested assumptions. Regarding boundary conditions, the evidence suggests that the unmodelled monopolar return path should be accounted for, perhaps by applying a voltage offset at a boundary surface. Regarding vasculature, the models show that large modiolar vessels like the vein of the scala tympani have a strong local effect near the stimulating electrode. Finally, it appears that the oft-cited quasi-static assumption is not valid due to the high permittivity of neural tissue. It is hoped that the study improves the trustworthiness of all bioelectric models of the cochlea, either by validating the claims of existing models, or by prompting improvements in future work. Developing our understanding of the underlying physics will pave the way for advancing future electrode array designs as well as patient-specific simulations, ultimately improving the quality of life for those with SHL
Three-dimensional models of cochlear implants : a review of their development and how they could support management and maintenance of cochlear implant performance
Three-dimensional (3D) computational modelling of the auditory periphery forms an
integral part of modern-day research in cochlear implants (CIs). These models consist
of a volume conduction description of implanted stimulation electrodes and the current
distribution around these, coupled to auditory nerve fibre models. Cochlear neural
activation patterns can then be predicted for a given input stimulus. The objective of
this article is to present the context of 3D modelling within the field of CIs, the
different models and approaches to models that have been developed over the years, as
well as the applications and potential applications of these models. The process of
development of 3D models is discussed, and the article places specific emphasis on the
complementary roles of generic models and user-specific models, as the latter is
important for translation of these models into clinical application.http://tandfonline.com/toc/inet202017-05-31hb2016Electrical, Electronic and Computer Engineerin
Electrochemical Safety Studies of Cochlear Implant Electrodes Using the Finite Element Method
Cochlear implants, amongst other neural prostheses, utilise platinum electrodes as an interface between the synthetic implant and the biological tissue environment. If excessive electrical charge is injected via these electrodes, injury to the tissue may result. Empirically derived stimulation limits have been defined to prevent tissue damage, however the injurious mechanisms are still unclear. Evidence suggests that the non-uniform distribution of charge on electrodes influences the electrochemical generation of toxic by-products. However, in vivo and in vitro techniques are limited in their ability to systematically explore the factors and mechanisms that contribute to stimulation-induced tissue injury. To this end, an in silico approach was used to develop a time-domain model of cochlear implant stimulation electrodes. A constant phase angle impedance was used to model the reversible processes on the electrode surface, and Butler-Volmer reaction kinetics were used to define the behaviour of the water window irreversible electrochemical reactions. The resulting model provided time-domain responses of the current density distributions, and net charge consumed by the hydrolysis reactions. This model was then used to perform systematic evaluations of various electrode geometries and stimulation parameters. The modelling results showed the current associated with irreversible reactions was non-uniform and tended towards the periphery of the electrode. A comparison of electrode geometries revealed interactions between electrode size, shape and recess depth. Stimulation mode, electrode position, and electrolyte conductivity were found to impact the shape of the electric field and the extent of irreversible reactions. This emphasised the influence of the physiological environment on the stimulation safety. In vitro experiments were conducted to validate the model. The implications of the results described in this thesis can be used to inform the design of safer electrodes
3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients.
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
Characterization of Evoked Potentials During Deep Brain Stimulation in the Thalamus
<p>Deep brain stimulation (DBS) is an established surgical therapy for movement disorders. The mechanisms of action of DBS remain unclear, and selection of stimulation parameters is a clinical challenge and can result in sub-optimal outcomes. Closed-loop DBS systems would use a feedback control signal for automatic adjustment of DBS parameters and improved therapeutic effectiveness. We hypothesized that evoked compound action potentials (ECAPs), generated by activated neurons in the vicinity of the stimulating electrode, would reveal the type and spatial extent of neural activation, as well as provide signatures of clinical effectiveness. The objective of this dissertation was to record and characterize the ECAP during DBS to determine its suitability as a feedback signal in closed-loop systems. The ECAP was investigated using computer simulation and <italic>in vivo</italic> experiments, including the first preclinical and clinical ECAP recordings made from the same DBS electrode implanted for stimulation. </p><p>First, we developed DBS-ECAP recording instrumentation to reduce the stimulus artifact and enable high fidelity measurements of the ECAP at short latency. <italic>In vitro</italic> and <italic>in vivo</italic> validation experiments demonstrated the capability of the instrumentation to suppress the stimulus artifact, increase amplifier gain, and reduce distortion of short latency ECAP signals.</p><p>Second, we characterized ECAPs measured during thalamic DBS across stimulation parameters in anesthetized cats, and determined the neural origin of the ECAP using pharmacological interventions and a computer-based biophysical model of a thalamic network. This model simulated the ECAP response generated by a population of thalamic neurons, calculated ECAPs similar to experimental recordings, and indicated the relative contribution from different types of neural elements to the composite ECAP. Signal energy of the ECAP increased with DBS amplitude or pulse width, reflecting an increased extent of activation. Shorter latency, primary ECAP phases were generated by direct excitation of neural elements, whereas longer latency, secondary phases were generated by post-synaptic activation.</p><p>Third, intraoperative studies were conducted in human subjects with thalamic DBS for tremor, and the ECAP and tremor responses were measured across stimulation parameters. ECAP recording was technically challenging due to the presence of a wide range of stimulus artifact magnitudes across subjects, and an electrical circuit equivalent model and finite element method model both suggested that glial encapsulation around the DBS electrode increased the artifact size. Nevertheless, high fidelity ECAPs were recorded from acutely and chronically implanted DBS electrodes, and the energy of ECAP phases was correlated with changes in tremor. </p><p>Fourth, we used a computational model to understand how electrode design parameters influenced neural recording. Reducing the diameter or length of recording contacts increased the magnitude of single-unit responses, led to greater spatial sensitivity, and changed the relative contribution from local cells or passing axons. The effect of diameter or contact length varied across phases of population ECAPs, but ECAP signal energy increased with greater contact spacing, due to changes in the spatial sensitivity of the contacts. In addition, the signal increased with glial encapsulation in the peri-electrode space, decreased with local edema, and was unaffected by the physical presence of the highly conductive recording contacts.</p><p>It is feasible to record ECAP signals during DBS, and the correlation between ECAP characteristics and tremor suggests that this signal could be used in closed-loop DBS. This was demonstrated by implementation in simulation of a closed-loop system, in which a proportional-integral-derivative (PID) controller automatically adjusted DBS parameters to obtain a target ECAP energy value, and modified parameters in response to disturbances. The ECAP also provided insight into neural activation during DBS, with the dominant contribution to clinical ECAPs derived from excited cerebellothalamic fibers, suggesting that activation of these fibers is critical for DBS therapy.</p>Dissertatio
Auf einem menschlichen Gehörmodell basierende Elektrodenstimulationsstrategie für Cochleaimplantate
Cochleaimplantate (CI), verbunden mit einer professionellen Rehabilitation,
haben mehreren hunderttausenden Hörgeschädigten die verbale Kommunikation
wieder ermöglicht. Betrachtet man jedoch die Rehabilitationserfolge, so
haben CI-Systeme inzwischen ihre Grenzen erreicht. Die Tatsache, dass die
meisten CI-Träger nicht in der Lage sind, Musik zu genießen oder einer
Konversation in geräuschvoller Umgebung zu folgen, zeigt, dass es noch Raum
für Verbesserungen gibt.Diese Dissertation stellt die neue
CI-Signalverarbeitungsstrategie Stimulation based on Auditory Modeling
(SAM) vor, die vollständig auf einem Computermodell des menschlichen
peripheren Hörsystems beruht.Im Rahmen der vorliegenden Arbeit wurde die
SAM Strategie dreifach evaluiert: mit vereinfachten Wahrnehmungsmodellen
von CI-Nutzern, mit fünf CI-Nutzern, und mit 27 Normalhörenden mittels
eines akustischen Modells der CI-Wahrnehmung. Die Evaluationsergebnisse
wurden stets mit Ergebnissen, die durch die Verwendung der Advanced
Combination Encoder (ACE) Strategie ermittelt wurden, verglichen. ACE
stellt die zurzeit verbreitetste Strategie dar. Erste Simulationen zeigten,
dass die Sprachverständlichkeit mit SAM genauso gut wie mit ACE ist.
Weiterhin lieferte SAM genauere binaurale Merkmale, was potentiell zu einer
Verbesserung der Schallquellenlokalisierungfähigkeit führen kann. Die
Simulationen zeigten ebenfalls einen erhöhten Anteil an zeitlichen
Pitchinformationen, welche von SAM bereitgestellt wurden. Die Ergebnisse
der nachfolgenden Pilotstudie mit fünf CI-Nutzern zeigten mehrere Vorteile
von SAM auf. Erstens war eine signifikante Verbesserung der
Tonhöhenunterscheidung bei Sinustönen und gesungenen Vokalen zu erkennen.
Zweitens bestätigten CI-Nutzer, die kontralateral mit einem Hörgerät
versorgt waren, eine natürlicheren Klangeindruck. Als ein sehr bedeutender
Vorteil stellte sich drittens heraus, dass sich alle Testpersonen in sehr
kurzer Zeit (ca. 10 bis 30 Minuten) an SAM gewöhnen konnten. Dies ist
besonders wichtig, da typischerweise Wochen oder Monate nötig sind. Tests
mit Normalhörenden lieferten weitere Nachweise für die verbesserte
Tonhöhenunterscheidung mit SAM.Obwohl SAM noch keine marktreife Alternative
ist, versucht sie den Weg für zukünftige Strategien, die auf Gehörmodellen
beruhen, zu ebnen und ist somit ein erfolgversprechender Kandidat für
weitere Forschungsarbeiten.Cochlear implants (CIs) combined with professional rehabilitation have
enabled several hundreds of thousands of hearing-impaired individuals to
re-enter the world of verbal communication. Though very successful, current
CI systems seem to have reached their peak potential. The fact that most
recipients claim not to enjoy listening to music and are not capable of
carrying on a conversation in noisy or reverberative environments shows
that there is still room for improvement.This dissertation presents a new
cochlear implant signal processing strategy called Stimulation based on
Auditory Modeling (SAM), which is completely based on a computational model
of the human peripheral auditory system.SAM has been evaluated through
simplified models of CI listeners, with five cochlear implant users, and
with 27 normal-hearing subjects using an acoustic model of CI perception.
Results have always been compared to those acquired using Advanced
Combination Encoder (ACE), which is today’s most prevalent CI strategy.
First simulations showed that speech intelligibility of CI users fitted
with SAM should be just as good as that of CI listeners fitted with ACE.
Furthermore, it has been shown that SAM provides more accurate binaural
cues, which can potentially enhance the sound source localization ability
of bilaterally fitted implantees. Simulations have also revealed an
increased amount of temporal pitch information provided by SAM. The
subsequent pilot study, which ran smoothly, revealed several benefits of
using SAM. First, there was a significant improvement in pitch
discrimination of pure tones and sung vowels. Second, CI users fitted with
a contralateral hearing aid reported a more natural sound of both speech
and music. Third, all subjects were accustomed to SAM in a very short
period of time (in the order of 10 to 30 minutes), which is particularly
important given that a successful CI strategy change typically takes weeks
to months. An additional test with 27 normal-hearing listeners using an
acoustic model of CI perception delivered further evidence for improved
pitch discrimination ability with SAM as compared to ACE.Although SAM is
not yet a market-ready alternative, it strives to pave the way for future
strategies based on auditory models and it is a promising candidate for
further research and investigation
Physiology, Psychoacoustics and Cognition in Normal and Impaired Hearing
The International Symposium on Hearing is a prestigious, triennial gathering where world-class scientists present and discuss the most recent advances in the field of human and animal hearing research. The 2015 edition will particularly focus on integrative approaches linking physiological, psychophysical and cognitive aspects of normal and impaired hearing. Like previous editions, the proceedings will contain about 50 chapters ranging from basic to applied research, and of interest to neuroscientists, psychologists, audiologists, engineers, otolaryngologists, and artificial intelligence researchers.