Improving the forward model for electrical impedance tomography of brain function through rapid generation of subject specific finite element models

Electrical Impedance Tomography (EIT) is a non-invasive imaging method which allows internal electrical impedance of any conductive object to be imaged by means of current injection and surface voltage measurements through an array of externally applied electrodes. The successful generation of the image requires the simulation of the current injection patterns on either an analytical or a numerical model of the domain under examination, known as the forward model, and using the resulting voltage data in the inverse solution from which images of conductivity changes can be constructed. Recent research strongly indicates that geometric and anatomical conformance of the forward model to the subject under investigation significantly affects the quality of the images. This thesis focuses mainly on EIT of brain function and describes a novel approach for the rapid generation of patient or subject specific finite element models for use as the forward model. After introduction of the topic, methods of generating accurate finite element (FE) models using commercially available Computer-Aided Design (CAD) tools are described and show that such methods, though effective and successful, are inappropriate for time critical clinical use. The feasibility of warping or morphing a finite element mesh as a means of reducing the lead time for model generation is then presented and demonstrated. This leads on to the description of methods of acquiring and utilising known system geometry, namely the positions of electrodes and registration landmarks, to construct an accurate surface of the subject, the results of which are successfully validated. The outcome of this procedure is then used to specify boundary conditions to a mesh warping algorithm based on elastic deformation using well-established continuum mechanics procedures. The algorithm is applied to a range of source models to empirically establish optimum values for the parameters defining the problem which can successfully generate meshes of acceptable quality in terms of discretization errors and which more accurately define the geometry of the target subject. Further validation of the algorithm is performed by comparison of boundary voltages and image reconstructions from simulated and laboratory data to demonstrate that benefits in terms of image artefact reduction and localisation of conductivity changes can be gained. The processes described in the thesis are evaluated and discussed and topics of further work and application are described

Explicit-Duration Hidden Markov Model Inference of UP-DOWN States from Continuous Signals

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics

Models and image: reconstruction in electrical impedance tomography of human brain function

Electrical Impedance Tomography (EIT) of brain function has the potential to provide a rapid portable bedside neuroimaging device. Recently, our group published the first ever EIT images of evoked activity recorded with scalp electrodes. While the raw data showed encouraging, reproducible changes of a few per cent, the images were noisy. The poor image quality was due, in part, to the use of a simplified reconstruction algorithm which modelled the head as a homogeneous sphere. The purpose of this work has been to develop new algorithms in which the model incorporates extracerebral layers and realistic geometry, and to assess their effect on image quality. An algorithm was suggested which allowed fair comparison between reconstructions assuming analytical and numerical (Finite Element Method - FEM) models of the head as a homogeneous sphere and as concentric spheres representing the brain, CSF, skull and scalp. Comparison was also made between these and numerical models of the head as a homogeneous, head-shaped volume and as a head-shaped volume with internal compartments of contrasting resistivity. The models were tested on computer simulations, on spherical and head-shaped, saline-filled tanks and on data collected during human evoked response studies. EIT also has the potential to image resistance changes which occur during neuronal depolarization in the cortex and last tens of milliseconds. Also presented in this thesis is an estimate of their magnitude made using a mathematical model, based on cable theory, of resistance changes at DC during depolarization in the cerebral cortex. Published values were used for the electrical properties and geometry of cell processes (Rail, 1975). The study was performed in order to estimate the resultant scalp signal that might be obtained and to assess the ability of EIT to produce images of neuronal depolarization