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

Variation in Reported Human Head Tissue Electrical Conductivity Values

Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR

Techniques for imaging small impedance changes in the human head due to neuronal depolarisation

A new imaging modality is being developed, which may be capable of imaging small impedance changes in the human head due to neuronal depolarization. One way to do this would be by imaging the impedance changes associated with ion channels opening in neuronal membranes in the brain during activity. The results of previous modelling and experimental studies indicated that impedance changes between 0.6%and 1.7% locally in brain grey matter when recorded at DC. This reduces by a further of 10% if measured at the surface of the head, due to distance and the effect of the resistive skull. In principle, this could be measured using Electrical Impedance Tomography (ElT) but it is close to its threshold of detectability. With the inherent limitation in the use of electrodes, this work proposed two new schemes. The first is a magnetic measurement scheme based on recording the magnetic field with Superconducting Quantum Interference Devices (SQUIDs), used in Magnetoencephalography (MEG) as a result of a non-invasive injection of current into the head. This scheme assumes that the skull does not attenuate the magnetic field. The second scheme takes into consideration that the human skull is irregular in shape, with less and varying conductivity as compared to other head tissues. Therefore, a key issue is to know through which electrodes current can be injected in order to obtain high percentage changes in surface potential when there is local conductivity change in the head. This model will enable the prediction of the current density distribution at specific regions in the brain with respect to the varying skull and local conductivities. In the magnetic study, the head was modelled as concentric spheres, and realistic head shapes to mimic the scalp, skull, Cerebrospinal Auid (CSF) and brain using the Finite Element Method (FEM). An impedance change of 1 % in a 2cm-radius spherical volume depicting the physiological change in the brain was modelled as the region of depolarisation. The magnetic field, 1 cm away from the scalp, was estimated on injecting a constant current of 100 µA into the head from diametrically opposed electrodes. However, in the second scheme, only the realistic FEM of the head was used, which included a specific region of interest; the primary visual cortex (V1). The simulated physiological change was the variation in conductivity of V1 when neurons were assumed to be firing during a visual evoked response. A near DC current of 100 µA was driven through possible pairs of 31 electrodes using ElT techniques. For a fixed skull conductivity, the resulting surface potentials were calculated when the whole head remained unperturbed, or when the conductivity of V1 changed by 0.6%, 1 %, and 1.6%. The results of the magnetic measurement predicted that standing magnetic field was about 10pT and the field changed by about 3fT (0.03%) on depolarization. For the second scheme, the greatest mean current density through V1 was 0.020 ± 0.005 µAmm-2, and occurred with injection through two electrodes positioned near the occipital cortex. The corresponding maximum change in potential from baseline was 0.02%. Saline tank experiments confirmed the accuracy of the estimated standing potentials. As the noise density in a typical MEG system in the frequency band is about 7fT/√Hz, it places the change at the limit of detectability due to low signal to noise ratio. This is therefore similar to electrical recording, as in conventional ElT systems, but there may be advantages to MEG in that the magnetic field direcdy traverses the skull and instrumentation errors from the electrode-skin interface will be obviated. This has enabled the estimation of electrode positions most likely to permit recording of changes in human experiments and suggests that the changes, although tiny, may just be discernible from noise

EEG/MEG Source Imaging: Methods, Challenges, and Open Issues

We present the four key areas of research—preprocessing, the volume conductor, the forward problem, and the inverse problem—that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization

Translational Modeling of Non-Invasive Electrical Stimulation

Seminal work in the early 2000’s demonstrated the effect of low amplitude non-invasive electrical stimulation in people using neurophysiological measures (motor evoked potentials, MEPs). Clinical applications of transcranial Direct Current Stimulation (tDCS) have since proliferated, though the mechanisms are not fully understood. Efforts to refine the technique to improve results are on-going as are mechanistic studies both in vivo and in vitro. Volume conduction models are being applied to these areas of research, especially in the design and analysis of clinical montages. However, additional research on the parameterization of models remains. In this dissertation, Finite Element Method (FEM) models of current flow were developed for clinical applications. The first image-derived models of obese subjects were developed to assess the relative impact of fat delineation from skin. Body mass index and more broadly inter-individual differences were considered. The effect of incorporating the meninges was predicted from CAD-based (Computer Aided Design) models before being translated into image-derived head models as an “emulated” CSF conductivity. These predictions were tested in a recently validated database of head models. Multi-scale models of transcutaneous vagus nerve stimulation (tVNS) were developed by coupling image-derived volume conduction models with physiological compartment modeling. The impact of local tissue inhomogeneities on fiber activation were considered

Study of the Characteristics of Scalp Electroencephalography Sensing

Ph.DDOCTOR OF PHILOSOPH

Variation in reported human head tissue electrical conductivity values

Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR

Modeling Brain Using Parameters of Passive Electrical Circuits

Brain is the central and most complex organ in the human body. It controls most of the body functions, processing, integrating, and coordinating the information it receives from the organs, and sending decision instructions to the rest of the body. Brain injury may occur due to external environmental and/or internal influences. Timely diagnosing and differentiating the type of brain injury is critical. CT and MRI are often used for the diagnosis, which may not be available at a remote location or at an accident site or an emergency vehicle. This thesis contributes to the modeling of the brain from the electrical point of view, considering the structural complexity of the head composed of biological tissues with different dielectric properties. The goal of the thesis is to develop electrical models of the brain using parameters of passive electrical circuits. The models are developed for a normal brain and a brain with pathological conditions such as edema (swelling) and hemorrhage (bleeding). The circuit models are simulated at a range of frequencies from 1 Hz to 200 kHz. The experiment data is collected on a sheep brain surrounded by phantom tissue using bioimpedance analyzer at a range of frequencies from 1 Hz to 200 kHz. The simulation results are compared with experiment data

Functional Electrical Impedance Tomography of adult and neonatal brain function.

Electrical Impedance Tomography (EIT) is a fast, portable imaging technique that produces tomographic images of the internal impedance of an object from surface electrode measurements. This thesis reports the first use of EIT to image evoked brain activity in adults and neonates and determines whether accurate EIT images could be obtained from the adult and neonatal brain. In addition, a realistic head-tank phantom was developed to test the performance of EIT with known impedance changes placed within a real human skull. Two EIT systems were used. Images were obtained using 31 or 21 Ag/AgCl EEG scalp electrodes in adults and neonates, respectively, with either 256 or 187 individual impedance measurements from different electrode combinations: 2 applied a safe, alternating current and 2 measured the resultant scalp voltage. Imaging was performed using a block design with 6-15 stimulation periods of between 10-75s during either: 1) Visual, 2) Somatosensory or 3) Motor stimuli. Impedance changes were detected in 38/39 adults and 9/9 neonates within 0.6-5.8s after stimulus onset, and returned to baseline 7.6-36s after stimulus cessation. Reconstructed images were noisy: -20-70% images showed correct localisation to the expected area of cortex stimulated by the visual, motor or somatosensory paradigms. As EIT images from the head-tank localised changes within 10% of the impedance perturbation, this indicated that poor localisation in humans was not due to the head-shape or the skull, but may be related to unknown physiological factors. An improved EIT reconstruction algorithm, using a computerised finite-element model of the head, showed improved localisation for the adult images. This is the first demonstration that EIT can detect and image impedance changes in the head, probably due to increased regional cerebral blood volume in the activated cortex. Improvements may enable more accurate neuroimaging of the adult and neonatal brain for use in clinical practice