Computational Models for Predicting Outcomes of Neuroprosthesis Implantation: the Case of Cochlear Implants

Accuracy of Preoperative Cochlear Duct Length Estimation and Angular Insertion Depth Prediction

OBJECTIVE

In cochlear implantation with flexible lateral wall electrodes, a cochlear coverage of 70% to 80% is assumed to yield an optimal speech perception. Therefore, fitting the cochlear implant (CI) to the patient's individual anatomy has gained importance in recent years. For these reasons, the optimal angular insertion depth (AID) has to be calculated before cochlear implantation. One CI manufacturer offers a software that allows to visualize the AID of different electrode arrays. Here, it is hypothesized that these preoperative AID models overestimate the postoperatively measured insertion angle. This study aims to investigate the agreement between preoperatively estimated and postoperatively measured AID.

STUDY DESIGN

Retrospective cross-sectional study.

SETTING

Single-center tertiary referral center.

PATIENTS

Patients undergoing cochlear implantation.

INTERVENTION

Preoperative and postoperative high-resolution computed tomography (HRCT).

MAIN OUTCOME MEASURES

The cochlear duct length was estimated by determining cochlear parameters ( A value and B value), and the AID for the chosen electrode was (i) estimated by elliptic circular approximation by the software and (ii) measured manually postoperatively by detecting the electrode contacts after insertion.

RESULTS

A total of 80 HRCT imaging data sets from 69 patients were analyzed. The mean preoperative AID estimation was 662.0° (standard deviation [SD], 61.5°), and the mean postoperatively measured AID was 583.9° (SD, 73.6°). In all cases (100%), preoperative AID estimation significantly overestimated the postoperative determined insertion angle (mean difference, 38.1°). A correcting factor of 5% on preoperative AID estimation dissolves these differences.

CONCLUSIONS

The use of an electrode visualization tool may lead to shorter electrode array choices because of an overestimation of the insertion angle. Applying a correction factor of 0.95 on preoperative AID estimation is recommended.

Activation region overlap visualization for image-guided cochlear implant programming

Objective. The cochlear implant is a neural prosthesis designed to directly stimulate auditory nerve fibers to induce the sensation of hearing in those experiencing severe-to-profound hearing loss. After surgical implantation, audiologists program the implant's external processor with settings intended to produce optimal hearing outcomes. The likelihood of achieving optimal outcomes increases when audiologists have access to tools that objectively present information related to the patient's own anatomy and surgical outcomes. This includes visualizations like the one presented here, termed the activation region overlap image, which is designed to decrease subjectivity when determining amounts of overlapping stimulation between implant electrodes.Approach. This visualization uses estimates of electric field strength to indicate spread of neural excitation due to each electrode. Unlike prior visualizations, this method explicitly defines regions of nerves receiving substantial stimulation from each electrode to help clinicians assess the presence of significant overlapping stimulation. A multi-reviewer study compared this and an existing technique on the consistency, efficiency, and optimality of plans generated from each method. Statistical significance was evaluated using the two-sided Wilcoxon rank sum test.Main results. The study showed statistically significant improvements in consistency (p < 10-12), efficiency (p < 10-15), and optimality (p < 10-5) when generating plans using the proposed method versus the existing method.Significance. This visualization addresses subjectivity in assessing overlapping stimulation between implant electrodes, which currently relies on reviewer estimates. The results of the evaluation indicate the provision of such objective information during programming sessions would likely benefit clinicians in making programming decisions.

A phenomenological computational model of the evoked action potential fitted to human cochlear implant responses

There is a growing interest in biomedical engineering in developing procedures that provide accurate simulations of the neural response to electrical stimulus produced by implants. Moreover, recent research focuses on models that take into account individual patient characteristics.

We present a phenomenological computational model that is customized with the patient’s data provided by the electrically evoked compound action potential (ECAP) for simulating the neural response to electrical stimulus produced by the electrodes of cochlear implants (CIs). The model links the input currents of the electrodes to the simulated ECAP.

Potentials and currents are calculated by solving the quasi-static approximation of the Maxwell equations with the finite element method (FEM). In ECAPs recording, an active electrode generates a current that elicits action potentials in the surrounding auditory nerve fibers (ANFs). The sum of these action potentials is registered by other nearby electrode. Our computational model emulates this phenomenon introducing a set of line current sources replacing the ANFs by a set of virtual neurons (VNs). To fit the ECAP amplitudes we assign a suitable weight to each VN related with the probability of an ANF to be excited. This probability is expressed by a cumulative beta distribution parameterized by two shape parameters that are calculated by means of a differential evolution algorithm (DE). Being the weights function of the current density, any change in the design of the CI affecting the current density produces changes in the weights and, therefore, in the simulated ECAP, which confers to our model a predictive capacity.

The results of the validation with ECAP data from two patients are presented, achieving a satisfactory fit of the experimental data with those provided by the proposed computational model.

The cochlea, found in the inner ear, is the organ where the sound is transformed into an electrical pulse to be transmitted by the neurons to the auditory cortex. Hearing loss can be caused by damage to the hair cells, in which case neuronal excitation is impaired. CIs are devices that replace the normal function of the impaired/damaged Organ of Corti. Computational models allow a better understanding of the mechanisms involved in the electrical stimulation of the auditory nerve. These models can help biomedical engineers to develop new CIs with improved auditory performance. One important aspect of our model is its customization with the patient’s data provided by the recording of the evoked compound action potential (the synchronous firing of a population of electrically stimulated auditory nerve fibers). This phenomenological model allows us to predict the registers of neural stimulation produced when the auditory nerve is stimulated with the CIs. We have validated the proposed model with real data obtained from two patients with CIs.

The combination of brain-computer interfaces and artificial intelligence: applications and challenges

Brain-computer interfaces (BCIs) have shown great prospects as real-time bidirectional links between living brains and actuators. Artificial intelligence (AI), which can advance the analysis and decoding of neural activity, has turbocharged the field of BCIs. Over the past decade, a wide range of BCI applications with AI assistance have emerged. These "smart" BCIs including motor and sensory BCIs have shown notable clinical success, improved the quality of paralyzed patients' lives, expanded the athletic ability of common people and accelerated the evolution of robots and neurophysiological discoveries. However, despite technological improvements, challenges remain with regard to the long training periods, real-time feedback, and monitoring of BCIs. In this article, the authors review the current state of AI as applied to BCIs and describe advances in BCI applications, their challenges and where they could be headed in the future.

Computational Evaluation of Cochlear Implant Surgery Outcomes Accounting for Uncertainty and Parameter Variability

Cochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process.

Patient-specific estimation of detailed cochlear shape from clinical CT images

PURPOSE

A personalized estimation of the cochlear shape can be used to create computational anatomical models to aid cochlear implant (CI) surgery and CI audio processor programming ultimately resulting in improved hearing restoration. The purpose of this work is to develop and test a method for estimation of the detailed patient-specific cochlear shape from CT images.

METHODS

From a collection of temporal bone [Formula: see text]CT images, we build a cochlear statistical deformation model (SDM), which is a description of how a human cochlea deforms to represent the observed anatomical variability. The model is used for regularization of a non-rigid image registration procedure between a patient CT scan and a [Formula: see text]CT image, allowing us to estimate the detailed patient-specific cochlear shape.

RESULTS

We test the accuracy and precision of the predicted cochlear shape using both [Formula: see text]CT and CT images. The evaluation is based on classic generic metrics, where we achieve competitive accuracy with the state-of-the-art methods for the task. Additionally, we expand the evaluation with a few anatomically specific scores.

CONCLUSIONS

The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method.

RNA Binding Protein as an Emerging Therapeutic Target for Cancer Prevention and Treatment

After transcription, RNAs are always associated with RNA binding proteins (RBPs) to perform biological activities. RBPs can interact with target RNAs in sequence- and structure-dependent manner through their unique RNA binding domains. In development and progression of carcinogenesis, RBPs are aberrantly dysregulated in many human cancers with various mechanisms, such as genetic alteration, epigenetic change, noncoding RNA-mediated regulation, and post-translational modifications. Upon deregulation in cancers, RBPs influence every step in the development and progression of cancer, including sustained cell proliferation, evasion of apoptosis, avoiding immune surveillance, inducing angiogenesis, and activating metastasis. To develop therapeutic strategies targeting RBPs, RNA interference-based oligonucleotides or small molecule inhibitors have been screened based on reduced RBP-RNA interaction and changed level of target RNAs. Identification of binding RNAs with high-throughput techniques and integral analysis of multiple datasets will help us develop new therapeutic drugs or prognostic biomarkers for human cancers.

A multiscale imaging and modelling dataset of the human inner ear

Understanding the human inner ear anatomy and its internal structures is paramount to advance hearing implant technology. While the emergence of imaging devices allowed researchers to improve understanding of intracochlear structures, the difficulties to collect appropriate data has resulted in studies conducted with few samples. To assist the cochlear research community, a large collection of human temporal bone images is being made available. This data descriptor, therefore, describes a rich set of image volumes acquired using cone beam computed tomography and micro-CT modalities, accompanied by manual delineations of the cochlea and sub-compartments, a statistical shape model encoding its anatomical variability, and data for electrode insertion and electrical simulations. This data makes an important asset for future studies in need of high-resolution data and related statistical data objects of the cochlea used to leverage scientific hypotheses. It is of relevance to anatomists, audiologists, computer scientists in the different domains of image analysis, computer simulations, imaging formation, and for biomedical engineers designing new strategies for cochlear implantations, electrode design, and others.

Towards a Complete In Silico Assessment of the Outcome of Cochlear Implantation Surgery

Cochlear implantation (CI) surgery is a very successful technique, performed on more than 300,000 people worldwide. However, since the challenge resides in obtaining an accurate surgical planning, computational models are considered to provide such accurate tools. They allow us to plan and simulate beforehand surgical procedures in order to maximally optimize surgery outcomes, and consequently provide valuable information to guide pre-operative decisions. The aim of this work is to develop and validate computational tools to completely assess the patient-specific functional outcome of the CI surgery. A complete automatic framework was developed to create and assess computationally CI models, focusing on the neural response of the auditory nerve fibers (ANF) induced by the electrical stimulation of the implant. The framework was applied to evaluate the effects of ANF degeneration and electrode intra-cochlear position on nerve activation. Results indicate that the intra-cochlear positioning of the electrode has a strong effect on the global performance of the CI. Lateral insertion provides better neural responses in case of peripheral process degeneration, and it is recommended, together with optimized intensity levels, in order to preserve the internal structures. Overall, the developed automatic framework provides an insight into the global performance of the implant in a patient-specific way. This enables to further optimize the functional performance and helps to select the best CI configuration and treatment strategy for a given patient.

Random walks with shape prior for cochlea segmentation in ex vivo μCT

PURPOSE

Cochlear implantation is a safe and effective surgical procedure to restore hearing in deaf patients. However, the level of restoration achieved may vary due to differences in anatomy, implant type and surgical access. In order to reduce the variability of the surgical outcomes, we previously proposed the use of a high-resolution model built from [Formula: see text] images and then adapted to patient-specific clinical CT scans. As the accuracy of the model is dependent on the precision of the original segmentation, it is extremely important to have accurate [Formula: see text] segmentation algorithms.

METHODS

We propose a new framework for cochlea segmentation in ex vivo [Formula: see text] images using random walks where a distance-based shape prior is combined with a region term estimated by a Gaussian mixture model. The prior is also weighted by a confidence map to adjust its influence according to the strength of the image contour. Random walks is performed iteratively, and the prior mask is aligned in every iteration.

RESULTS

We tested the proposed approach in ten [Formula: see text] data sets and compared it with other random walks-based segmentation techniques such as guided random walks (Eslami et al. in Med Image Anal 17(2):236-253, 2013) and constrained random walks (Li et al. in Advances in image and video technology. Springer, Berlin, pp 215-226, 2012). Our approach demonstrated higher accuracy results due to the probability density model constituted by the region term and shape prior information weighed by a confidence map.

CONCLUSION

The weighted combination of the distance-based shape prior with a region term into random walks provides accurate segmentations of the cochlea. The experiments suggest that the proposed approach is robust for cochlea segmentation.

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 modeling 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 with auditory nerve fiber 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 modeling 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.

Automatic Model Generation Framework for Computational Simulation of Cochlear Implantation

Recent developments in computational modeling of cochlear implantation are promising to study in silico the performance of the implant before surgery. However, creating a complete computational model of the patient's anatomy while including an external device geometry remains challenging. To address such a challenge, we propose an automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea. First, a statistical shape model is constructed from high-resolution anatomical μCT images. Then, by fitting the statistical model to a patient's CT image, an accurate model of the patient-specific cochlea anatomy is obtained. An algorithm based on the parallel transport frame is employed to perform the virtual insertion of the cochlear implant. Our automatic framework also incorporates the surrounding bone and nerve fibers and assigns constitutive parameters to all components of the finite element model. This model can then be used to study in silico the effects of the electrical stimulation of the cochlear implant. Results are shown on a total of 25 models of patients. In all cases, a final mesh suitable for finite element simulations was obtained, in an average time of 94 s. The framework has proven to be fast and robust, and is promising for a detailed prognosis of the cochlear implantation surgery.