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

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included

Are There Brain-Based Predictors of the Ability to Learn a New Skill in Healthy Ageing and Can They Help in the Design of Effective Therapy after Stroke?

This thesis aimed at looking for neural correlates of motor adaptation as a model of rehabilitation after brain injury. Healthy adults across the lifespan and stroke patients were tested in a force-field learning paradigm. This thesis focuses on EEG analysis and the complex relationship of brain-derived measures with observed behaviour. To describe each domain in detail, the focus was first on finding group differences between older and younger healthy adults in a similar manner as it was later between stroke patients versus healthy controls. The analyses were finalised by looking for relationships between the EEG and motor performance data in a multiple linear regression approach. As candidate EEG biomarkers of motor adaptation, error related event related potential around movement onset in the frontocentral electrodes was chosen in time domain. In the time-frequency domain, the focus was on movement related beta band spectral perturbation, looking at the electrodes over the primary motor cortex and the frontocentral ROI found significant in the time domain. Finally, functional connectivity was analysed focusing first on electrode over the primary motor cortex contralateral to the movement as a seed region, to narrow down the analysis to bilateral motor cortex connectivity and connectivity between primary motor cortex contralateral to the movement and the frontocentral region identified as important in the time domain analysis. The crucial part of the project was analysing the relationship between the neural and kinematic measures. The most important predictor of summed error in motor adaptation was the connectivity between C3 and C4 electrode at the baseline prestimulus period in motor adaptation condition and pinch asymmetry. Higher prestimulus interhemispheric connectivity was associated with bigger deviation from the optimal trajectory. When looking at summed error dynamic derivative as a dependent variable - performance index - it was the ERP at the central error-related ROI that explained the most variance. It can be concluded that higher baseline interhemispheric connectivity can be a reflection of a maladaptive process, perhaps related to increased interhemispheric inhibition. It is important to also note that the same connectivity at different timepoints in the movement can be of different significance - differences between stroke patients and controls were present in the postmovement period. In conclusion, brain information could be helpful for e.g. stratifying patients into different intensity programs based on their predicted potential to recover. Moreover, brain information could be utilised to apply closed-loop systems modulating the intensity of tasks to reach the optimal brain state that facilitates learning. I believe this work will help incorporating brain-derived measures in informing neurorehabilitation programmes in the future

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

EpiGauss : caracterização espacio-temporal da actividade cerebral em epilepsia

Doutoramento em Engenharia ElectrotécnicaA epilepsia é uma patologia cerebral que afecta cerca de 0,5% da população mundial. Nas epilepsias focais, o principal objectivo clínico é a localização da zona epileptogénica (área responsável pelas crises), uma informação crucial para uma terapêutica adequada. Esta tese é centrada na caracterização da actividade cerebral electromagnética do cérebro epiléptico. As contribuições nesta área, entre a engenharia e neurologia clínica, são em duas direcções. Primeiro, mostramos que os conceitos associados às pontas podem ser imprecisos e não ter uma definição objectiva, tornando necessária uma reformulação de forma a definir uma referência fiável em estudos relacionados com a análise de pontas. Mostramos que as características das pontas em EEG são estatisticamente diferentes das pontas em MEG. Esta constatação leva a concluir que a falta de objectividade na definição de ponta na literatura pode induzir utilizações erradas de conceitos associados ao EEG na análise de MEG. Também verificamos que o uso de conjuntos de detecções de pontas efectuadas por especialistas (MESS) como referência pode fornecer resultados enganadores quando apenas baseado em critérios de consenso clínico, nomeadamente na avaliação da sensibilidade e especificidade de métodos computorizados de detecção de pontas Em segundo lugar, propomos o uso de métodos estatísticos para ultrapassar a falta de precisão e objectividade das definições relacionadas com pontas. Propomos um novo método de neuroimagem suportado na caracterização de geradores electromagnéticos – EpiGauss – baseado na análise individual dos geradores de eventos do EEG que explora as suas estruturas espacio-temporais através da análise de “clusters”. A aplicação de análise de “clusters” à análise geradores de eventos do EEG tem como objectivo usar um método não supervisionado, para encontrar estruturas espacio-temporais dps geradores relevantes. Este método, como processo não supervisionado, é orientado a utilizadores clínicos e apresenta os resultados sob forma de imagens médicas com interpretação similar a outras técnicas de imagiologia cerebral. Com o EpiGauss, o utilizador pode determinar a localização estatisticamente mais provável de geradores, a sua estabilidade espacial e possíveis propagações entre diferente áreas do cérebro. O método foi testado em dois estudos clínicos envolvendo doentes com epilepsia associada aos hamartomas hipotalâmicos e o outro com doentes com diagnóstico de epilepsia occipital. Em ambos os estudos, o EpiGauss foi capaz de identificar a zona epileptogénica clínica, de forma consistente com a história e avaliação clínica dos neurofisiologistas, fornecendo mais informação relativa à estabilidade dos geradores e possíveis percursos de propagação da actividade epileptogénica contribuindo para uma melhor caracterização clínica dos doentes. A conclusão principal desta tese é que o uso de técnicas não supervisionadas, como a análise de “clusters”, associadas as técnicas não-invasivas de EMSI, pode contribuir com um valor acrescido no processo de diagnóstico clínico ao fornecer uma caracterização objectiva e representação visual de padrões complexos espaciotemporais da actividade eléctrica epileptogénica.Epilepsy is a brain pathology that affects 0.5% of the world population. In focal epilepsies, the main clinical objective is the localization of the epileptogenic zone (brain area responsible for the epileptic seizures – EZ), a key information to decide an adequate therapeutic approach. This thesis is centred on electromagnetic activity characterization of the epileptic brain. Our contribution to this boundary area between engineering and clinical neurology is two-folded. First we show that spike related clinical concepts can be unprecise and some do not have objective definitions making necessary a reformulation in order to have a reliable reference in spike related studies. We show that EEG spike wave quantitative features are statistically different from their MEG counterparts. This finding leads to the conclusion that the lack of objective spike feature definitions in the literature can induce the wrong usage of EEG feature definition in MEG analysis. We also show that the use of multi-expert spike selections sets (MESS) as gold standard, although clinically useful, may be misleading whenever defined solely in terms of clinical agreement criteria, namely as references for automatic spike detection algorithms in sensitivity and specificity method analysis. Second, we propose the use of statistical methods to overcome some lack of precision and objectivity in spike related definitions. In this context, we propose a new ElectroMagnetic Source Imaging (EMSI) method – EpiGauss – based on cluster analysis that explores both spatial and temporal information contained in individual events sources analysis characterisation. This automatic cluster method for the analysis of spike related electric generators based in EEG is used to provide an unsupervised tool to find their relevant spatio-temporal structures. This method enables a simple unsupervised procedure aimed for clinical users and presents its results in an intuitive representation similar to other brain imaging techniques. With EpiGauss, the user is able to determine statistically probable source locations, their spatial stability and propagation patterns between different brain areas. The method was tested in two different clinical neurophysiology studies, one with a group of Hypothalamic Hamartomas and another with a group of Occipital Epilepsy patients. In both studies EpiGauss identified the clinical epileptogenic zone, consistent with the clinical background and evaluation of neurophysiologists, providing further information on stability of source locations and their probable propagation pathways that enlarges their clinical interpretation. This thesis main conclusion is that the use of unsupervised techniques, such as clustering, associated with EMSI non-invasive techniques, can bring an added value in clinical diagnosis process by providing objective and visual representation of complex epileptic brain spatio-temporal activity patterns

Autonomic and central nervous system correlates of cognitive control training for attentional disorders

Deficits in cognitive control and attentional processing are commonly observed in people with Attention-Deficit/Hyperactivity Disorder (ADHD) and Specific Learning Difficulties (SpLDs) such as Dyslexia. Poorer performance in the pro/antisaccade task have been observed in these individuals, which suggests impaired visual attention and inhibitory control mechanisms. Atypical cognitive processing is also related to a state of autonomic hypoarousal in conditions such as ADHD. In this thesis, I examined whether the computer-based gaze-control RECOGNeyes training program using the pro/antisaccade task could improve cognitive control of visual attention by targeting the visual attention network and whether such improvements correlate with increased arousal. A group of 35 volunteers with SpLDs and/or ADHD completed the pro/antisaccade task before and after two weeks of training their visual attention using RECOGNeyes. Magnetoencephalography (MEG), pupillometry and electrocardiography were recorded, while they performed the pro/antisaccade task. Our task performance measures, reaction time (RT) and accuracy, and reading indices improved after RECOGNeyes training. Our findings demonstrate for the first time that autonomic measures of sympathetic pupil dilation and parasympathetic cardiac deceleration both correlate with faster saccadic RTs together (which was stronger for antisaccade trials than prosaccade trials) and account for separate variance in RT. Additionally, distinct MEG oscillatory profiles were uncovered in different frequency bands within regions of the visual attention network during the pro/antisaccade task. Slow-wave oscillations of delta and theta bands show anteriorising effects, suggested to mediate timing responses and bottom-up communication from the posterior to anterior network regions. Alpha-oscillations are proposed to have top-down preparatory inhibitory effects, particularly from the bilateral frontal eye field, and alpha-suppression in the right parietal eye field. Beta amplitude presents an additional “anticipatory” event-related desynchronisation (ERD) prior to target onset that is stronger on day 2 and antisaccade trials, which could relate to generalised inhibitory control mechanisms. This thesis supports the existence of complex central and autonomic processes underlying attention and arousal that are not yet fully understood and warrant further investigation. By increasing our understanding of the integrated attentional processes and inhibitory control, this could help the development of targeted treatment solutions, such as RECOGNeyes, for ADHD and SpLDs, to improve outcomes in these individuals