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Simulation and evaluation of stimulation scenarios for targeted vestibular nerve excitation

  • Peter Schier EMAIL logo , Michael Handler , Daniel Baumgarten and Christian Baumgartner
Published/Copyright: September 30, 2016
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Current Directions in Biomedical Engineering
From the journal Current Directions in Biomedical Engineering Volume 2 Issue 1

Abstract

Recent studies show that vestibular implants have the potential to compensate the loss of functionality of the organ of equilibrium. The objective of this study was the development of a simulation framework which estimates the capability of various electrode arrangements and stimulation waveforms to selectively excite the ampullary nerves in the vestibular system. The choice of electrode configuration and stimulation waveform shows a significant influence in resulting selectivity, energy expenditure and injected charge in our simulations. This simulation environment could be beneficial in the development of safe and selective stimulation electrode designs.

Keywords: charge; electrode configuration; energy; neural stimulation; selectivity; vestibular implant; waveform

1 Introduction

A loss of functionality of the vestibular system due to injuries, diseases or toxins can cause a severe detriment to quality of life. Similar to cochlear implants, this functionality can be re-established with neurostimulation prostheses. For the development of these vestibular implants, electrode configurations need to be defined that allow for selective stimulation of the vestibular nerve bundles while not affecting the neighboring facial nerve and cochlear nerve.

This study focuses on the development of a framework for the realistic simulation of the neuronal responses of nerves located in and around the vestibular system during electrical stimulation. Various mono- and multipolar electrode arrangements, stimulation waveforms and stimulus pulse durations are evaluated to find optimal configurations for a selective and efficacious neural excitation of vestibular ampullary nerves. The results of these simulations are planned to be the foundation of innovative vestibular prosthesis designs.

2 Material and methods

2.1 Data pre-processing

Image data from μCT-scans of human inner ears was provided by the Medical University of Innsbruck. The well segmented image data was meshed by a tetrahedral meshing workflow developed at our institute using the free software library CGAL [1] (see Figure 1). As a simplification of the surrounding structure, the meshed geometry of the vestibular system was embedded into a bone sphere with a diameter of 5 cm. The mesh consisted of approximately 2.4 million elements. Stationary potential distributions of various mono- and bipolar electrode arrangements were computed using the finite element method (FEM) and used as an input for the nerve fiber model as previously described in [2].

Figure 1: Meshed vestibular system with the facial nerve (green), the posterior ampullary nerve (blue) and the adjoined anterior and lateral ampullary nerves (red). In the ampullae of the semicircular canals, several electrode configurations (black) are embedded.
Figure 1:

Meshed vestibular system with the facial nerve (green), the posterior ampullary nerve (blue) and the adjoined anterior and lateral ampullary nerves (red). In the ampullae of the semicircular canals, several electrode configurations (black) are embedded.

2.2 Generation of artificial nerve fibers

The nerve volumes had to be populated with a natural distribution of nerve fibers in order to model realistic neural behaviour during stimulation. A method based on the already available FEM-algorithms was developed. The method uses the volume mesh of a nerve and the surface where the nerve is connected to the sensory epithelium as input. The sensory epithelium is considered as the start surface for the fiber generation algorithm. A distance map to this start surface is calculated to find the distal-most portion of the nerve relative to the sensory epithelium. The surface of this portion is then declared as the target surface, that should be reached by the generated nerve fibers starting from the sensory epithelium. In the next step, a direction field is computed by considering the nerve volume as a volume conductor with constant isotropic conductivity. Constant negative and positive potentials are defined at the source and target surfaces, respectively. The potential distribution was calculated by the FEM and the gradient field of the resulting potential distribution serves as a direction field for the artificial nerve fiber growth (see Figure 2).

Figure 2: Adjoined anterior and lateral ampullary nerves. A low potential is applied at the start surfaces (blue) and a high potential at the end surface (red). Vectors represent the potential gradient field and are used as direction for neuron trajectory generation.
Figure 2:

Adjoined anterior and lateral ampullary nerves. A low potential is applied at the start surfaces (blue) and a high potential at the end surface (red). Vectors represent the potential gradient field and are used as direction for neuron trajectory generation.

Subsequently, 400 fiber start points are scattered across the start surface of every nerve and a tracing algorithm determines the paths through the direction fields. Each fiber is assigned a certain diameter depending on its location of origin on the sensory epithelium [3]. Fibers originating from the center of the sensory epithelium are generally thicker than fibers which start from the border regions. Nodes of Ranvier are then distributed along the neurons with respect to their diameters (see Figure 3).

Figure 3: Start points of different fiber types scattered across the sensory epithelium (A) and artificially generated neural trajectories originating from these start points in the posterior ampullary nerve (B). Black dots along the neurons indicate nodes of Ranvier.
Figure 3:

Start points of different fiber types scattered across the sensory epithelium (A) and artificially generated neural trajectories originating from these start points in the posterior ampullary nerve (B). Black dots along the neurons indicate nodes of Ranvier.

2.3 Mathematical neuron model

A mathematical neuron model is used to predict the behavioural pattern of nerve fibers during electrical stimulation. Hayden’s modified SENN model [2] was used in a first attempt and further elaborated to fit human anatomy. Morphologic data of human nerve fibers [4], [5] were introduced into the nerve model and parameters for afterhyperpolarization effects were adapted accordingly. Different types of neurons based on clinical studies [3] are considered in the simulations. The previously computed potential distribution of a given electrode configuration is scaled proportionally to the stimulus amplitude and interpolated at the nodes of Ranvier for every nerve fiber and serves as an input for the neuron model as described in [6].

2.4 Electrode configurations and stimulation protocols

One monopolar and four bipolar electrode arrangements were simulated for all ampullary nerves using the electrode configurations as shown in Figure 4. In bipolar arrangements one electrode is set active with a current outflow of 1 A and the other is set as reference with a constant potential of 0 V. Since active and reference electrode are also switched, each bipolar configuration has to be computed twice. For the monopolar configuration, the boundary surface of the model was set to a constant reference potential of 0 V.

Figure 4: Spherical electrode configurations in the posterior ampulla. Nerve axial (magenta), nerve tip parallel (cyan) and perpendicular (red) arrangements are oriented orthogonally to each other. The ampullary axial configuration (yellow and brown) is roughly oriented along the axis of the semicircular canal. In case of monopolar stimulation only the yellow electrode is active.
Figure 4:

Spherical electrode configurations in the posterior ampulla. Nerve axial (magenta), nerve tip parallel (cyan) and perpendicular (red) arrangements are oriented orthogonally to each other. The ampullary axial configuration (yellow and brown) is roughly oriented along the axis of the semicircular canal. In case of monopolar stimulation only the yellow electrode is active.

Since it is known that stimulation of proximal neurons mostly depends on the cathodic phase while anodic pulses affect distal parts of the nerve fibers [7], a number of clinical neurostimulation devices targeting tissue in close proximity use pseudo-monophasic, cathodic stimulation. These pulses consist of a short cathodic phase with a high amplitude and a long refractory anodic phase with low amplitude. To avoid tissue damage, it is required that the stimulus is charge balanced [8].

The influence of stimulus shape variation of the cathodic phase was tested by exciting the nerves with the same rectangular, sinusoidal, linearly increasing and decreasing as well as exponentially increasing and decreasing pulses as described by Sahin [9]. Instead of a gaussian pulse a centered triangular pulse was used. Stimulus durations were initially short (10 μs) and systematically increased up to 500 μs to evaluate the influence of different pulse spans.

2.5 Evaluation

A binary search algorithm is used to determine the excitation current strength threshold. The scaling factor of the potential distribution is varied until the lower and upper search boundary differ < 0.1% from each other. The selectivity of electrode configurations is determined via receiver operating characteristic (ROC) curves. The true positive rate is defined as the ratio between excited targeted nerve fibers and all targeted nerve fibers. The false positive rate is computed correspondingly for non-targeted neurons. The area under the ROC-curve (AUC) is considered as evaluation criterion for selectivity efficiency. Higher AUC-values indicate a better selectivity.

3 Results

Variations regarding electrode configurations and stimulation protocols were simulated in order to maximize fiber recruitment in the targeted nerve while minimizing the excitation of non-targeted nerves.

3.1 Influence of different electrode arrangements

In a first step, the ampullary nerve with the lowest overall selectivity was determined. It was assumed that the effect of an alteration of the electrode arrangement would be easier to detect in this nerve. Therefore, improvements in selectivity would also be more visible. Fiber recruitment patterns and their corresponding AUC were computed using monopolar stimulation in each ampulla. The interpolated external potential in each node was scaled by a biphasic, symmetric, cathodic-phase-first stimulus protocol with 200 μs phase duration. The lateral ampullary nerve exhibited the lowest AUC in these simulations. Because of the central position of the lateral ampulla and the proximity to the facial nerve, this outcome was expected.

Further simulations using various bipolar electrode arrangements (see Figure 4) with the same stimulus protocol were conducted in the lateral ampulla. Every bipolar configuration yielded at least one active-reference electrode combination which was superior to monopolar stimulation in terms of selectivity (see Table 1). However, the current strength threshold required for excitation of targeted neurons was significantly higher in all bipolar cases. The best selectivity was achieved by the nerve tip perpendicular arrangement with its active electrode located further inside the SCC than the reference electrode. This is in accordance with the simulation results performed by Hayden et al. [2].

Table 1:

AUC values for monopolar and bipolar electrode arrangements. For bipolar arrangements only the better active-reference electrode combination is depicted.

ArrangementLocation of active electrodeAUC
Monopolar0.680
Ampullary axialProximal to target nerve0.687
Nerve axialProximal to target nerve0.785
Nerve tip perpendicularMedial to target ampulla0.810
Nerve tip parallelDistal to anterior ampulla0.764

3.2 Influence of stimulus shape variation

After determining the best electrode arrangement, the shape and duration of stimulus protocols were varied (see section 2.4). Although there were no significant differences between the pulse shapes in terms of selectivity, it was apparent throughout all stimulus patterns that shorter pulses exhibit better selectivity than longer pulses.

Injected charge and expended energy are indicators for tissue damage and battery life. In order to calculate these two parameters for each waveform, the current strength required for excitation of 80% of targeted nerve fibers was computed for every simulation. Charge and energy are plotted against pulse duration and against each other to determine optimal waveform shapes (see Figure 5). The simulation results exhibit high accordance with similar studies from existing literature [9], [10]. Injected charge and energy consumption show identical behaviour for the same waveforms.

Figure 5: Injected charge and required energy strongly depend on the choice of stimulus shape and -duration. For short pulse durations, the least energy is expended by the centered triangular waveform. For longer pulses the exponentially increasing and decreasing waveforms are a reasonable choice.
Figure 5:

Injected charge and required energy strongly depend on the choice of stimulus shape and -duration. For short pulse durations, the least energy is expended by the centered triangular waveform. For longer pulses the exponentially increasing and decreasing waveforms are a reasonable choice.

4 Discussion

A computer simulation framework for the generation of artificial nerve fibers and the estimation of their excitation thresholds was developed. Based on image data of the human inner ear, several electrode arrangements and stimulation pulse waveforms were tested to evaluate their selectivity, injected charge and energy expenditure.

Bipolar electrode configurations yielded a better selectivity at the cost of higher energy expenditure than monopolar stimulation. In terms of waveform shapes, sinusoidal and especially centered triangular pulses showed promising results with regards to reduced charge injection and lower required energy thresholds.

5 Outlook

As further progress of the study, the current spherical electrodes will be replaced by an elaborated design. Utricular and saccular nerves are planned to be embedded in the model to simulate undesirable side effects on the otolith organs. An implementation of the auditory nerve is considered as well. In our future studies the tool will contribute to the analysis of novel electrode configurations and stimulus patterns. This could eventually pave the way for a safer and more selective stimulation electrode design.

Acknowledgement

This work was supported by the Standortagentur Tirol within the K-Regio project VAMEL. The authors would like to thank MED-EL and the K-Regio Project partners for their support as well as the Medical University of Innsbruck for their highly detailed image data.

Author’s Statement

Research funding: The author state no funding involved. Conflict of interest: Authors state no conflict of interest. Material and Methods: Informed consent: Informed consent has been obtained from all individuals included in this study. Ethical approval: The conducted research is not related to either human or animal use.

References

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Published Online: 2016-9-30
Published in Print: 2016-9-1

©2016 Peter Schier et al., licensee De Gruyter.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/cdbme-2016-0033
Keywords for this article
charge; electrode configuration; energy; neural stimulation; selectivity; vestibular implant; waveform
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  76. Surgical navigation with QR codes
  77. Investigation of the pressure gradient of embolic protection devices
  78. Computer assistance in femoral derotation osteotomy: a bottom-up approach
  79. Automatic depth scanning system for 3D infrared thermography
  80. A service for monitoring the quality of intraoperative cone beam CT images
  81. Resectoscope with an easy to use twist mechanism for improved handling
  82. In vitro simulation of distribution processes following intramuscular injection
  83. Adjusting inkjet printhead parameters to deposit drugs into micro-sized reservoirs
  84. A flexible standalone system with integrated sensor feedback for multi-pad electrode FES of the hand
  85. Smart control for functional electrical stimulation with optimal pulse intensity
  86. Tactile display on the remaining hand for unilateral hand amputees
  87. Effects of sustained electrical stimulation on spasticity assessed by the pendulum test
  88. An improved tracking framework for ultrasound probe localization in image-guided radiosurgery
  89. Improvement of a subviral particle tracker by the use of a LAP-Kalman-algorithm
  90. Learning discriminative classification models for grading anal intraepithelial neoplasia
  91. Regularization of EIT reconstruction based on multi-scales wavelet transforms
  92. Assessing MRI susceptibility artefact through an indicator of image distortion
  93. EyeGuidance – a computer controlled system to guide eye movements
  94. A framework for feedback-based segmentation of 3D image stacks
  95. Doppler optical coherence tomography as a promising tool for detecting fluid in the human middle ear
  96. 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
  97. Inside-Out access strategy using new trans-vascular catheter approach
  98. US/MRI fusion with new optical tracking and marker approach for interventional procedures inside the MRI suite
  99. Impact of different registration methods in MEG source analysis
  100. 3D segmentation of thyroid ultrasound images using active contours
  101. Designing a compact MRI motion phantom
  102. Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging
  103. Classification of indirect immunofluorescence images using thresholded local binary count features
  104. Analysis of muscle fatigue conditions using time-frequency images and GLCM features
  105. Numerical evaluation of image parameters of ETR-1
  106. Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation
  107. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography
  108. Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection
  109. Computer assisted assessment of progressing osteoradionecrosis of the jaw for clinical diagnosis and treatment
  110. Evaluation of reconstruction parameters of electrical impedance tomography on aorta detection during saline bolus injection
  111. Evaluation of open-source software for the lung segmentation
  112. Automatic determination of lung features of CF patients in CT scans
  113. Image analysis of self-organized multicellular patterns
  114. Effect of key parameters on synthesis of superparamagnetic nanoparticles (SPIONs)
  115. Radiopacity assessment of neurovascular implants
  116. Development of a desiccant based dielectric for monitoring humidity conditions in miniaturized hermetic implantable packages
  117. Development of an artifact-free aneurysm clip
  118. Enhancing the regeneration of bone defects by alkalizing the peri-implant zone – an in vitro approach
  119. Rapid prototyping of replica knee implants for in vitro testing
  120. Protecting ultra- and hyperhydrophilic implant surfaces in dry state from loss of wettability
  121. Advanced wettability analysis of implant surfaces
  122. Patient-specific hip prostheses designed by surgeons
  123. Plasma treatment on novel carbon fiber reinforced PEEK cages to enhance bioactivity
  124. Wear of a total intervertebral disc prosthesis
  125. Digital health and digital biomarkers – enabling value chains on health data
  126. Usability in the lifecycle of medical software development
  127. Influence of different test gases in a non-destructive 100% quality control system for medical devices
  128. Device development guided by user satisfaction survey on auricular vagus nerve stimulation
  129. Empirical assessment of the time course of innovation in biomedical engineering: first results of a comparative approach
  130. Effect of left atrial hypertrophy on P-wave morphology in a computational model
  131. Simulation of intracardiac electrograms around acute ablation lesions
  132. Parametrization of activation based cardiac electrophysiology models using bidomain model simulations
  133. Assessment of nasal resistance using computational fluid dynamics
  134. Resistance in a non-linear autoregressive model of pulmonary mechanics
  135. Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
  136. Determination of regional lung function in cystic fibrosis using electrical impedance tomography
  137. Development of parietal bone surrogates for parietal graft lift training
  138. Numerical simulation of mechanically stimulated bone remodelling
  139. Conversion of engineering stresses to Cauchy stresses in tensile and compression tests of thermoplastic polymers
  140. Numerical examinations of simplified spondylodesis models concerning energy absorption in magnetic resonance imaging
  141. Principle study on the signal connection at transabdominal fetal pulse oximetry
  142. Influence of Siluron® insertion on model drug distribution in the simulated vitreous body
  143. Evaluating different approaches to identify a three parameter gas exchange model
  144. Effects of fibrosis on the extracellular potential based on 3D reconstructions from histological sections of heart tissue
  145. From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms
  146. Flow optimised design of a novel point-of-care diagnostic device for the detection of disease specific biomarkers
  147. Improved FPGA controlled artificial vascular system for plethysmographic measurements
  148. Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation
  149. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment
  150. Control scheme selection in human-machine- interfaces by analysis of activity signals
  151. Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks
  152. Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach
  153. Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor
  154. Interactive monitoring system for visual respiratory biofeedback
  155. Development of a low-cost senor based aid for visually impaired people
  156. Patient assistive system for the shoulder joint
  157. A passive beating heart setup for interventional cardiology training
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