Feasibility of imaging evoked activity throughout the rat brain using electrical impedance tomography

Electrical Impedance Tomography (EIT) is an emerging technique which has been used to image evoked activity during whisker displacement in the cortex of an anaesthetised rat with a spatiotemporal resolution of 200 μm and 2 ms. The aim of this work was to extend EIT to image not only from the cortex but also from deeper structures active in somatosensory processing, specifically the ventral posterolateral (VPL) nucleus of the thalamus. The direct response in the cortex and VPL following 2 Hz forepaw stimulation were quantified using a 57-channel epicortical electrode array and a 16-channel depth electrode. Impedance changes of -0.16 ± 0.08% at 12.9 ± 1.4 ms and -0.41 ± 0.14% at 8.8±1.9 ms were recorded from the cortex and VPL respectively. For imaging purposes, two 57-channel epicortical electrode arrays were used with one placed on each hemisphere of the rat brain. Despite using parameters optimised toward measuring thalamic activity and undertaking extensive averaging, reconstructed activity was constrained to the cortical somatosensory forepaw region and no significant activity at a depth greater than 1.6 mm below the surface of the cortex could be reconstructed. An evaluation of the depth sensitivity of EIT was investigated in simulations using estimates of the conductivity change and noise levels derived from experiments. These indicate that EIT imaging with epicortical electrodes is limited to activity occurring 2.5 mm below the surface of the cortex. This depth includes the hippocampus and so EIT has the potential to image activity, such as epilepsy, originating from this structure. To image deeper activity, however, alternative methods such as the additional implementation of depth electrodes will be required to gain the necessary depth resolution

Imaging Physiological and Pathological Activity in the Brain using Electric Impedance Tomography

Electric Impedance Tomography (EIT) is a promising medical imaging technique that reconstructs the internal conductivity of an object from boundary measurements. EIT is currently being used to monitor the lung during ventilation clinically. Amongst other suggested uses for imaging it can also be used to image neuronal function. There are different ways on how EIT can image neuronal function and two of these are tested in this thesis. The overall aim of our work was to advance imaging of physiological and pathological neuronal activity using EIT and assess its potential for future clinical use. In Chapter 1, a general introduction into brain imaging techniques and EIT is given. In Chapter 2, the effect of different anaesthetics on the neuronal signal was assessed to prepare for EIT recordings under anaesthesia. In Chapter 3, we assessed the validity of two biophysical models regarding the behaviour of the impedance in response to alterations in the carrier frequency experimentally. This allowed an assessment of the ideal carrier frequency to image physiological neuronal activity. In Chapter 4, the source of the fast neural signal in EIT is discussed further. In Chapter 5, the possibility of imaging physiological neuronal activity throughout the brain is tested and its limitations are discussed. In Chapter 6, the impedance response to epileptiform activity is characterized and the potential use of EIT in imaging epileptic foci in epilepsy patients is discussed. In Chapter 7, imaging of epileptic foci in subcortical structures is tested using two different ways of imaging with EIT

Imaging fast neural activity in the brain during epilepsy with electrical impedance tomography

Electrical impedance tomography (EIT) is a medical imaging technique which reconstructs images of the internal conductivity of an object using boundary measurements obtained by applying current through pairs of non-penetrating surface electrodes. EIT is able to image impedance changes which arise during neural activity at a high spatiotemporal resolution through the rat cerebral cortex and therefore represents a novel method for understanding neuronal network dynamics in epilepsy. Additionally, it holds therapeutic potential for improving the presurgical localisation of epileptogenic foci in individuals with drug-resistant epilepsy. This thesis was aimed at developing EIT for imaging epileptiform activity in vivo and assessing its potential for clinical use. Chapter 1 is a review of existing functional neuroimaging modalities, the principles of EIT and previous studies that have used EIT for imaging epileptic events. In Chapter 2, the safety of continuous current application to the rat cortical surface at 10-100 μA and 1725 Hz, parameters that are representative of fast neural EIT protocols, was verified by histological evaluation. Chapter 3 details the development of two acute rat models of focal epilepsy, the cortical and hippocampal epileptic afterdischarges models, for assessing the feasibility of imaging epileptiform activity with fast neural EIT using epicortical electrode arrays. In Chapter 4, EIT was used to image the propagation of ictal spike-and-wave activity through the cerebral cortex at a resolution of 2 ms and ≤300 µm. In order to enable imaging of epileptiform discharges in deeper subcortical structures, the optimal carrier frequency for current application was determined in Chapter 5. Results demonstrated that the maximal signal-to-noise ratio of fast neural impedance changes during ictal discharges is obtained at 1355 Hz. Finally, in Chapter 6, epileptiform activity in the hippocampus was imaged, with a localisation accuracy of ≤400 µm, using epicortical impedance measurements obtained at this optimised carrier frequency

Imaging Circuit Activity in the Rat Brain with Fast Neural EIT and Depth Arrays

Few techniques are specialized for neuroscience at the 'mesoscopic' level of neural circuits. Fast neural electrical impedance tomography (fnEIT) is a novel imaging technique that offers affordability, portability, and high spatial (∼100 μm) and temporal (1 ms) resolution. fnEIT with depth arrays offers the opportunity to study the dynamics of circuits in the brains of animal models. However, current depth array geometries are not optimized for this imaging modality. They feature small, closely packed electrodes with high impedance that do not provide sufficient SNR for high resolution EIT image reconstruction. They also have a highly limited range. It is necessary to develop depth arrays suitable for fnEIT and evaluate their performance in a representative setting for circuit neuroscience. In this study, we optimized the geometry of depth arrays for fnEIT, and then investigated the prospects of imaging thalamocortical circuit activity in the rat brain. Optimization was consistent with the hypothesis that small, closely spaced electrodes were not suitable for fnEIT. In vivo experiments with the optimized geometry then showed that fnEIT can image thalamocortical circuit activity at a high enough resolution to see the activity propagating from specific thalamic nuclei to specific regions of the somatosensory cortex. This bodes well for fnEIT's potential as a technique for circuit neuroscience

Imaging fast electrical activity in the brain with electrical impedance tomography.

Imaging of neuronal depolarization in the brain is a major goal in neuroscience, but no technique currently exists that could image neural activity over milliseconds throughout the whole brain. Electrical impedance tomography (EIT) is an emerging medical imaging technique which can produce tomographic images of impedance changes with non-invasive surface electrodes. We report EIT imaging of impedance changes in rat somatosensory cerebral cortex with a resolution of 2ms and <200μm during evoked potentials using epicortical arrays with 30 electrodes. Images were validated with local field potential recordings and current source-sink density analysis. Our results demonstrate that EIT can image neural activity in a volume 7×5×2mm in somatosensory cerebral cortex with reduced invasiveness, greater resolution and imaging volume than other methods. Modeling indicates similar resolutions are feasible throughout the entire brain so this technique, uniquely, has the potential to image functional connectivity of cortical and subcortical structures

Imaging physiological brain activity and epilepsy with Electrical Impedance Tomography

Electrical Impedance Tomography (EIT) allows reconstructing conductivity changes into images. EIT detects fast impedance changes occurring over milliseconds, due to ion channel opening, and slow impedance changes, appearing in seconds, due to cell swelling/increased blood flow. The purpose of this work was to examine the feasibility of using EIT for imaging a gyrencephalic brain with implanted depth electrodes during seizures. Chapter 1 summarises the principles of EIT. In Chapter 2, it is investigated whether recent technical improvements could enable EIT to image slow impedance changes upon visual stimulation non-invasively. This was unsuccessful so the remaining studies were undertaken on intracranial recordings. Chapter 3 presents a computer modelling study using data from patients, for whom the detection of simulated seizure-onset perturbations for both, fast and slow impedance changes, were improved with EIT compared to stereotactic electroencephalography (SEEG) detection or EEG inverse-source modelling. Chapter 4 describes the development of a portable EIT system that could be used on patients. The system does not require averaging and post-hoc signal processing to remove switching artefacts, which was the case previously. Chapter 5 describes the use of the optimised method in chemically-induced focal epilepsy in anaesthetised pigs implanted with depth electrodes. This shows for the first time EIT was capable of producing reproducible images of the onset and spread of seizure-related slow impedance changes in real-time. Chapter 6 presents a study on imaging ictal/interictal-related fast impedance changes. The feasibility of reconstructing ictal-related impedance changes is demonstrated for one pig and interictal-related impedance changes were recorded for the first time in humans. Chapter 7 summarises all work and future directions. Overall, this work suggests EIT in combination with SEEG has a potential to improve the diagnostic yield in epilepsy and demonstrates EIT can be performed safely and ethically creating a foundation for further clinical trials

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity

The Feasibility of Fast Neural Magnetic Detection Electrical Impedance Tomography: A Modelling Study

Magnetic Detection Electrical Impedance Tomography (MDEIT) is a possible method to non-invasively image fast neural activity in the human brain by injecting current with scalp electrodes and measuring the change in the magnetic field due to neural activity. A modelling study was performed on an anatomically realistic head model, assessing the SNR and reconstructed image quality for MDEIT and EIT with 3 different realistic noise cases. EIT produced a larger SNR than MDEIT for 2 out of the 3 noise cases. However, MDEIT was found to reconstruct images with a significantly lower error for all the reconstruction cases considered (P< 0.001)

Imaging fascicular organisation in mammalian vagus nerve for selective VNS

Nerves contain a large number of nerve fibres, or axons, organised into bundles known as fascicles. Despite the somatic nervous system being well understood, the organisation of the fascicles within the nerves of the autonomic nervous system remains almost completely unknown. The new field of bioelectronics medicine, Electroceuticals, involves the electrical stimulation of nerves to treat diseases instead of administering drugs or performing complex surgical procedures. Of particular interest is the vagus nerve, a prime target for intervention due to its afferent and efferent innervation to the heart, lungs and majority of the visceral organs. Vagus nerve stimulation (VNS) is a promising therapy for treatment of various conditions resistant to standard therapeutics. However, due to the unknown anatomy, the whole nerve is stimulated which leads to unwanted off-target effects. Electrical Impedance Tomography (EIT) is a non-invasive medical imaging technique in which the impedance of a part of the body is inferred from electrode measurements and used to form a tomographic image of that part. Micro-computed tomography (microCT) is an ex vivo method that has the potential to allow for imaging and tracing of fascicles within experimental models and facilitate the development of a fascicular map. Additionally, it could validate the in vivo technique of EIT. The aim of this thesis was to develop and optimise the microCT imaging method for imaging the fascicles within the nerve and to determine the fascicular organisation of the vagus nerve, ultimately allowing for selective VNS. Understanding and imaging the fascicular anatomy of nerves will not only allow for selective VNS and the improvement of its therapeutic efficacy but could also be integrated into the study on all peripheral nerves for peripheral nerve repair, microsurgery and improving the implementation of nerve guidance conduits. Chapter 1 provides an introduction to vagus nerve anatomy and the principles of microCT, neuronal tracing and EIT. Chapter 2 describes the optimisation of microCT for imaging the fascicular anatomy of peripheral nerves in the experimental rat sciatic and pig vagus nerve models, including the development of pre-processing methods and scanning parameters. Cross-validation of this optimised microCT method, neuronal tracing and EIT in the rat sciatic nerve was detailed in Chapter 3. Chapter 4 describes the study with microCT with tracing, EIT and selective stimulation in pigs, a model for human nerves. The microCT tracing approach was then extended into the subdiaphragmatic branches of the vagus nerves, detailed in Chapter 5. The ultimate goal of human vagus nerve tracing was preliminarily performed and described in Chapter 6. Chapter 7 concludes the work and describes future work. Lastly, Appendix 1 (Chapter 8) is a mini review on the application of selective vagus nerve stimulation to treat acute respiratory distress syndrome and Appendix 2 is morphological data corresponding to Chapter 4

EIT-MESHER – Segmented FEM Mesh Generation and Refinement

EIT-MESHER (https://github.com/EIT-team/Mesher) is C++ software, based on the CGAL library, which generates high quality Finite Element Model tetrahedral meshes from binary masks of 3D volume segmentations. Originally developed for biomedical applications in Electrical Impedance Tomography (EIT) to address the need for custom, non-linear refinement in certain areas (e.g. around electrodes), EIT-MESHER can also be used in other fields where custom FEM refinement is required, such as Diffuse Optical Tomography (DOT)