Localization of brain signal sources using blind source separation

Reliable localization of brain signal sources by using convenient, easy, and hazardless data acquisition techniques can potentially play a key role in the understanding, analysis, and tracking of brain activities for determination of physiological, pathological, and functional abnormalities. The sources can be due to normal brain activities, mental disorders, stimulation of the brain, or movement related tasks. The focus of this thesis is therefore the development of novel source localization techniques based upon EEG measurements. Independent component analysis is used in blind separation (BSS) of the EEG sources to yield three different approaches for source localization. In the first method the sources are localized over the scalp pattern using BSS in various subbands, and by investigating the number of components which are likely to be the true sources. In the second method, the sources are separated and their corresponding topographical information is used within a least-squares algorithm to localize the sources within the brain region. The locations of the known sources, such as some normal brain rhythms, are also utilized to help in determining the unknown sources. The final approach is an effective BSS algorithm partially constrained by information related to the known sources. In addition, some investigation have been undertaken to incorporate non-homogeneity of the head layers in terms of the changes in electrical and magnetic characteristics and also with respect to the noise level within the processing methods. Experimental studies with real and synthetic data sets are undertaken using MATLAB and the efficacy of each method discussed

Computational modelling of normal function and pathology in neural systems: new tools, techniques and results in cortex and basal ganglia.

Oscillations between various populations of neurons are common and well documented. However, there are oscillations that can emerge within networks of neurons that are pathological and highly detrimental to the normal functioning of the brain. This thesis is concerned with modelling the transition from healthy network states to the pathological oscillatory states in two different brain disorders; epilepsy and Parkinson’s disease (PD). To study these transitions, existing computational methods for modelling large systems of interacting populations of neurons are used and new tools are developed. The first half of this thesis explores the evidence for the dynamic evolution of focal epilepsy using bifurcation analysis of a neural mass model, and relating these bifurcations to specific features of clinical data recordings in the time-domain. These findings are used to map out the evolution of seizures based on features of segments of the clinically recorded electroencephalograms. The similarity of seizure evolution within patients is tested. Statistically significant similarities were found between the evolutions of seizures from the same patient. In the latter half of the thesis a way of creating firing rate models is described, in which the value of the membrane time constant is dependent on the activity of afferent populations. This method is applied to modelling the basal ganglia (BG). The hypothesis that the BG are responsible for selection in the primate brain is tested and confirmed. The model is then used to investigate the development of PD. It was found that the loss of dopaminergic innervation caused a failure of selection capability but did not directly give rise to the beta oscillations ubiquitous in PD. Network connection strength changes that are seen in PD cause the model to regain selection functionality but lead to a beta frequency resting state oscillation, as is the case in real PD

Quantitative Methods For Guiding Epilepsy Surgery From Intracranial Eeg

Despite advances in intracranial EEG (iEEG) technique, technology and neuroimaging, patients today are no more likely to achieve seizure freedom after epilepsy surgery than they were 20 years ago. These poor outcomes are in part due to the difficulty and subjectivity associated with interpreting iEEG recordings, and have led to widespread interest in developing quantitative methods to localize the epileptogenic zone. Approaches to computational iEEG analysis vary widely, spanning studies of both seizures and interictal periods, and encompassing a range of techniques including electrographic signal analysis and graph theory. However, many current methods often fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Indeed, none have completed prospective clinical trials. In this dissertation, I develop and validate tools for guiding epilepsy surgery through the quantitative analysis of intracranial EEG. Specifically, I leverage methods from graph theory for mapping network synchronizability to predict surgical outcome from ictal recordings, and also investigate the effects of sampling bias on network models. Finally, I construct a normative intracranial EEG atlas as a framework for objectively identifying patterns of abnormal neural activity and connectivity. Overall, the methods and results of this dissertation support the implementation of quantitative iEEG analysis in epilepsy surgical evaluation