Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation Study

Long-term electroencephalographic (EEG) recordings are important in the presurgical evaluation of refractory partial epilepsy for the delineation of the ictal onset zones. In this paper, we introduce a new concept for an automatic, fast, and objective localisation of the ictal onset zone in ictal EEG recordings. Canonical decomposition of ictal EEG decomposes the EEG in atoms. One or more atoms are related to the seizure activity. A single dipole was then fitted to model the potential distribution of each epileptic atom. In this study, we performed a simulation study in order to estimate the dipole localisation error. Ictal dipole localisation was very accurate, even at low signal-to-noise ratios, was not affected by seizure activity frequency or frequency changes, and was minimally affected by the waveform and depth of the ictal onset zone location. Ictal dipole localisation error using 21 electrodes was around 10.0 mm and improved more than tenfold in the range of 0.5–1.0 mm using 148 channels. In conclusion, our simulation study of canonical decomposition of ictal scalp EEG allowed a robust and accurate localisation of the ictal onset zone

Imaging brain networks in focal epilepsy: a prospective study of the clinical application of simultaneous EEG-fMRI in pre-surgical evaluation

Epilepsy is a common disorder with significant associated morbidity and mortality. Despite advances in treatment, there remain a minority of people with pharmacoresistant focal epilepsy for whom surgery may be beneficial. It has been suggested that not enough people are offered surgical treatment, partly owing to the fact that current non-invasive techniques do not always adequately identify the seizure onset zone so that invasive EEG is required. EEG-fMRI is an imaging technique, developed in the 1990s (Ives, Warach et al. 1993) which identifies regions of interictal epileptiform discharge associated haemodynamic changes, that are concordant with the seizure onset zone in some patients (Salek-Haddadi, Diehl et al. 2006). To date there has been no large scale prospective comparison with icEEG and postoperative outcome. This thesis presents a series of experiments, carried out in a cohort of patients scanned using EEG-fMRI as part of a multi-centre programme, designed to investigate the relationship between EEG-fMRI and intracranial EEG and to assess its potential role in pre-surgical evaluation of patients with focal epilepsy. The results suggested that positive, localised IED-related BOLD signal changes were sensitive for the seizure onset zone, as determined on icEEG, both in patients neocortical epilepsies, but were not predictive of outcome. Widespread regions of positive IEDrelated BOLD signal change were associated with widespread or multifocal abnormalities on icEEG and poor outcome. Patterns of haemodynamic change, identified using both data driven and EEG derived modeling approaches, correspond to regions of seizure onset on icEEG, but improvements for modeling seizures are required. A study of a single seizure in a patient who underwent simultaneous icEEGfMRI, showed similar findings.. An exploratory investigation of fMRI-DCM in EEG-fMRI, suggested it can provide information about seizure propagation and this opens new avenues for the non-invasive study of the epileptic network and interactions with function

Advances in epilepsy monitoring by detection and analysis of brain epileptiform discharges

Brain interictal and pre-ictal epileptiform discharges (EDs) are transient events occurred between two or before seizure onsets visible in intracranial electroencephalographs. In the diagnosis of epilepsy and localization of seizure sources, both interictal and ictal recordings are extremely informative. For this propose, computerized intelligent spike and seizure detection techniques have been researched and are constantly improving. This is not only to detect more EDs from over the scalp but also to classify epileptic and non-epileptic discharges. Tensor factorization and deep learning are two advanced and powerful techniques which have been recently suggested for ED detection. Here, our main contribution is to review recent ED detection methods with emphasis on multi-way analysis and deep learning approaches. These techniques have opened a new window to the epilepsy diagnosis and management spheres

Characteristics of Evoked Potential Multiple EEG Recordings in Patients with Chronic Pain by Means of Parallel Factor Analysis

This paper presents an alternative method, called as parallel factor analysis (PARAFAC) with a continuous wavelet transform, to analyze of brain activity in patients with chronic pain in the time-frequency-channel domain and quantifies differences between chronic pain patients and controls in these domains. The event related multiple EEG recordings of the chronic pain patients and non-pain controls with somatosensory stimuli (pain, random pain, touch, random touch) are analyzed. Multiple linear regression (MLR) is applied to describe the effects of aging on the frequency response differences between patients and controls. The results show that the somatosensory cortical responses occurred around 250 ms in both groups. In the frequency domain, the neural response frequency in the pain group (around 4 Hz) was less than that in the control group (around 5.5 Hz) under the somatosensory stimuli. In the channel domain, cortical activation was predominant in the frontal region for the chronic pain group and in the central region for controls. The indices of active ratios were statistical significant between the two groups in the frontal and central regions. These findings demonstrate that the PARAFAC is an interesting method to understanding the pathophysiological characteristics of chronic pain

Imaging functional and structural networks in the human epileptic brain

Epileptic activity in the brain arises from dysfunctional neuronal networks involving cortical and subcortical grey matter as well as their connections via white matter fibres. Physiological brain networks can be affected by the structural abnormalities causing the epileptic activity, or by the epileptic activity itself. A better knowledge of physiological and pathological brain networks in patients with epilepsy is critical for a better understanding the patterns of seizure generation, propagation and termination as well as the alteration of physiological brain networks by a chronic neurological disorder. Moreover, the identification of pathological and physiological networks in an individual subject is critical for the planning of epilepsy surgery aiming at resection or at least interruption of the epileptic network while sparing physiological networks which have potentially been remodelled by the disease. This work describes the combination of neuroimaging methods to study the functional epileptic networks in the brain, structural connectivity changes of the motor networks in patients with localisation-related or generalised epilepsy and finally structural connectivity of the epileptic network. The combination between EEG source imaging and simultaneous EEG-fMRI recordings allowed to distinguish between regions of onset and propagation of interictal epileptic activity and to better map the epileptic network using the continuous activity of the epileptic source. These results are complemented by the first recordings of simultaneous intracranial EEG and fMRI in human. This whole-brain imaging technique revealed regional as well as distant haemodynamic changes related to very focal epileptic activity. The combination of fMRI and DTI tractography showed subtle changes in the structural connectivity of patients with Juvenile Myoclonic Epilepsy, a form of idiopathic generalised epilepsy. Finally, a combination of intracranial EEG and tractography was used to explore the structural connectivity of epileptic networks. Clinical relevance, methodological issues and future perspectives are discussed

An adaptive filtering approach using supervised SSA for identification of sleep stages from EEG

Purpose: Sleep is a complex physiological state and an indicator of the changes in the brain function similar to those occurring in many psychiatric and neurological conditions. Since visual sleep scoring consuming process, automatic sleep staging methods, also called scoring, hold promise in diagnosing alterations in the sleep process and the sleep EEG more effectively. Method: In this paper, a supervised approach for sleep scoring from single channel EEG signals is proposed. First, a supervised singular spectrum analysis (SSA) which is a subspace based method is used to extract the desired signal for each stage of sleep. Then, two recursive least squares (RLS) adaptive filters are trained and used to identify first and deep sleep stages. Result: The proposed system which can be considered as a filter bank for separating multiple signal subbands is tested using real EEG where the results verify the accuracy of the proposed method. Conclusion: The overall result show the effectiveness of algorithm for detection of sleep stages from EEG signals often characterised by a sharp increase in delta and a rapid decrease in alpha as sleep deepens

Tensor based singular spectrum analysis for automatic scoring of sleep EEG

A new supervised approach for decomposition of single channel signal mixtures is introduced in this paper. The performance of the traditional singular spectrum analysis (SSA) algorithm is significantly improved by applying tensor decomposition instead of traditional singular value decomposition (SVD). As another contribution to this subspace analysis method, the inherent frequency diversity of the data has been effectively exploited to highlight the subspace of interest. As an important application, sleep EEG has been analysed and the stages of sleep for the subjects in normal condition, with sleep restriction, and with sleep extension have been accurately estimated and compared with the results of sleep scoring by clinical experts

Functional mapping of human brain networks with high temporal and spatial resolution methods

Tese de doutoramento em Biologia (Fisiologia e Bioquímica), apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008As limitações de ordem ética à investigação no homem impedem a aplicação dos métodos experimentais largamente usados em outros campos da fisiologia ao estudo da dinâmica cerebral humana. Os maiores avanços nesta área resultaram do estudo, por métodos não invasivos, das alterações associadas a lesões estruturais do cérebro em diversas patologias. Mais recentemente tornou-se possivel estudar as alterações neurofisiológicas e vasculares associadas a tarefas experimentais. Em qualquer dos casos a possibilidade de mapear no espaço e tempo as lesões ou as activações fisiológicas é de fundamental importância. As técnicas de mapeamento funcional não invasivo actualmente disponiveis apresentam significativas limitações, sendo que o EEG e MEG são as que apresentam melhor resolução temporal (milisegundos), enquanto a RMf apresenta a melhor resolução espacial (milimetros). Na presente tese caracterizamos o potencial do mapeamento funcional por EEG, bem como as vantagens e limitações do registo simultâneo EEG/RMf, num modelo patológico da actividade cerebral humana. O mapeamento funcional nas epilepsias occipitais idiopáticas ilustra a necessidade da análise dinâmica detalhada da propagação da actividade paroxística, as vantagens de uma boa amostragem espacial do EEG, bem como o contributo adicional dos registos EEG/RMf. Na epilepsia associada aos Hamartomas Hipotalâmicos ilustra-se a excelente complementaridade entre a RMf e o EEG para caracterizar a dinâmica espacio-temporal e cortico-subcortical da actividade neuronal. Nas epilepsias submetidas a cirurgia da epilepsia validou-se metodologia de resolução do problema inverso em EEG num tipo particular de patologia cerebral (Esclerose Tuberosa), e estudou-se a concordância entre o EEG e a RMf da actividade epiléptica. Conclui-se que a capacidade do EEG para detectar e seguir as rápidas modificações do estado funcional das redes neuronais lhe confere um papel da maior relevância entre as metodologias de mapeamento funcional. A RMf, efectuada concomitantemente, melhora a localização espacial da actividade neuronal e revela o importante contributo de áreas subcorticais.Ethical limitations to human research prevent the use of the experimental methods widely used in animal physiology to the study of brain function in man. The major advances in the area resulted from non-invasive studies of functional changes associated with structural brain lesions in various pathologies. Recent technological advances allowed detection of neurophysiological and vascular changes associated with experimental behavioral tasks. In either case, the ability to map in space and time the lesions and physiological activations is of fundamental importance. The available non-invasive functional mapping techniques have nevertheless significant methodological limitations. Among these techniques the EEG and MEG have the best temporal resolution (milliseconds), while fMRI has the best spatial resolution (millimeters). In the present thesis we study, in a pathological model of human brain function, the characteristics of functional brain mapping through EEG, as well as the advantages and limitations of the simultaneous EEG/fMRI recording. The functional mapping in idiopathic occipital lobe epilepsies illustrates the necessity of a detailed dynamical analysis of propagation of paroxysmal activity, the advantages of a good EEG spatial sampling, as well as the additional contribution of EEG/fMRI recording. In the epilepsies associated with hypothalamic hamartomas the synergy between fMRI and EEG to characterize the spatial-temporal and cortical-subcortical brain dynamics is demonstrated. In epilepsy cases submitted to epilepsy surgery, a method to solve the inverse problem in EEG was validated in patients with Tuberous Sclerosis, and the degree of concordance between EEG and fMRI studied. We conclude that the EEG ability to detect and follow the fast functional changes in state of brain networks gives it a role of the highest importance among functional brain mapping methods. The fMRI, recorded simultaneously, improves the spatial localization of the neuronal activity and highlights the contribution of subcortical areas