Investigating neuromagnetic brain responses against chromatic flickering stimuli by wavelet entropies

BACKGROUND: Photosensitive epilepsy is a type of reflexive epilepsy triggered by various visual stimuli including colourful ones. Despite the ubiquitous presence of colorful displays, brain responses against different colour combinations are not properly studied. METHODOLOGY/PRINCIPAL FINDINGS: Here, we studied the photosensitivity of the human brain against three types of chromatic flickering stimuli by recording neuromagnetic brain responses (magnetoencephalogram, MEG) from nine adult controls, an unmedicated patient, a medicated patient, and two controls age-matched with patients. Dynamical complexities of MEG signals were investigated by a family of wavelet entropies. Wavelet entropy is a newly proposed measure to characterize large scale brain responses, which quantifies the degree of order/disorder associated with a multi-frequency signal response. In particular, we found that as compared to the unmedicated patient, controls showed significantly larger wavelet entropy values. We also found that Renyi entropy is the most powerful feature for the participant classification. Finally, we also demonstrated the effect of combinational chromatic sensitivity on the underlying order/disorder in MEG signals. CONCLUSIONS/SIGNIFICANCE: Our results suggest that when perturbed by potentially epileptic-triggering stimulus, healthy human brain manages to maintain a non-deterministic, possibly nonlinear state, with high degree of disorder, but an epileptic brain represents a highly ordered state which making it prone to hyper-excitation. Further, certain colour combination was found to be more threatening than other combinations

Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings

L’épilepsie est une des maladies neurologiques les plus fréquentes, touchant près d’un pourcent de la population mondiale. De nos jours, bien qu’environ deux tiers des patients épileptiques répondent adéquatement aux traitements pharmacologiques, il reste qu’un tiers des patients doivent vivre avec des crises invalidantes et imprévisibles. Quoique la chirurgie d’épilepsie puisse être une autre option thérapeutique envisageable, le recours à la chirurgie de résection demeure très faible en partie pour des raisons diverses (taux de réussite modeste, peur des complications, perceptions négatives). D’autres avenues de traitement sont donc souhaitables. Une piste actuellement explorée par des groupes de chercheurs est de tenter de prédire les crises à partir d’enregistrements de l’activité cérébrale des patients. La capacité de prédire la survenue de crises permettrait notamment aux patients, aidants naturels ou personnels médical de prendre des mesures de précaution pour éviter les désagréments reliés aux crises voire même instaurer un traitement pour les faire avorter. Au cours des dernières années, d’importants efforts ont été déployés pour développer des algorithmes de prédiction de crises et d’en améliorer les performances. Toutefois, le manque d’enregistrements électroencéphalographiques intracrâniens (iEEG) de longue durée de qualité, la quantité limitée de crises, ainsi que la courte durée des périodes interictales constituaient des obstacles majeurs à une évaluation adéquate de la performance des algorithmes de prédiction de crises. Récemment, la disponibilité en ligne d’enregistrements iEEG continus avec échantillonnage bilatéral (des deux hémisphères) acquis chez des chiens atteints d’épilepsie focale à l’aide du dispositif de surveillance ambulatoire implantable NeuroVista a partiellement facilité cette tâche. Cependant, une des limitations associées à l’utilisation de ces données durant la conception d’un algorithme de prédiction de crises était l’absence d’information concernant la zone exacte de début des crises (information non fournie par les gestionnaires de cette base de données en ligne). Le premier objectif de cette thèse était la mise en oeuvre d’un algorithme précis de prédiction de crises basé sur des enregistrements iEEG canins de longue durée. Les principales contributions à cet égard incluent une localisation quantitative de la zone d’apparition des crises (basée sur la fonction de transfert dirigé –DTF), l’utilisation d’une nouvelle fonction de coût via l’algorithme génétique proposé, ainsi qu’une évaluation quasi-prospective des performances de prédiction (données de test d’un total de 893 jours). Les résultats ont montré une amélioration des performances de prédiction par rapport aux études antérieures, atteignant une sensibilité moyenne de 84.82 % et un temps en avertissement de 10 %. La DTF, utilisée précédemment comme mesure de connectivité pour déterminer le réseau épileptique (objectif 1), a été préalablement validée pour quantifier les relations causales entre les canaux lorsque les exigences de quasi-stationnarité sont satisfaites. Ceci est possible dans le cas des enregistrements canins en raison du nombre relativement faible de canaux. Pour faire face aux exigences de non-stationnarité, la fonction de transfert adaptatif pondérée par le spectre (Spectrum weighted adaptive directed transfer function - swADTF) a été introduit en tant qu’une version variant dans le temps de la DTF. Le second objectif de cette thèse était de valider la possibilité d’identifier les endroits émetteurs (ou sources) et récepteurs d’activité épileptiques en appliquant la swADTF sur des enregistrements iEEG de haute densité provenant de patients admis pour évaluation pré-chirurgicale au CHUM. Les générateurs d’activité épileptique étaient dans le volume réséqué pour les patients ayant des bons résultats post-chirurgicaux alors que différents foyers ont été identifiés chez les patients ayant eu de mauvais résultats postchirurgicaux. Ces résultats démontrent la possibilité d’une identification précise des sources et récepteurs d’activités épileptiques au moyen de la swADTF ouvrant la porte à la possibilité d’une meilleure sélection d’électrodes de manière quantitative dans un contexte de développement d’algorithme de prédiction de crises chez l’humain. Dans le but d’explorer de nouvelles avenues pour la prédiction de crises épileptiques, un nouveau précurseur a aussi été étudié combinant l’analyse des spectres d’ordre supérieur et les réseaux de neurones artificiels (objectif 3). Les résultats ont montré des différences statistiquement significatives (p<0.05) entre l’état préictal et l’état interictal en utilisant chacune des caractéristiques extraites du bi-spectre. Utilisées comme entrées à un perceptron multicouche, l’entropie bispectrale normalisée, l’entropie carré normalisée, et la moyenne ont atteint des précisions respectives de 78.11 %, 72.64% et 73.26%. Les résultats de cette thèse confirment la faisabilité de prédiction de crises à partir d’enregistrements d’électroencéphalographie intracrâniens. Cependant, des efforts supplémentaires en termes de sélection d’électrodes, d’extraction de caractéristiques, d’utilisation des techniques d’apprentissage profond et d’implémentation Hardware, sont nécessaires avant l’intégration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT Epilepsy is a chronic condition characterized by recurrent “unpredictable” seizures. While the first line of treatment consists of long-term drug therapy about one-third of patients are said to be pharmacoresistant. In addition, recourse to epilepsy surgery remains low in part due to persisting negative attitudes towards resective surgery, fear of complications and only moderate success rates. An important direction of research is to investigate the possibility of predicting seizures which, if achieved, can lead to novel interventional avenues. The paucity of intracranial electroencephalography (iEEG) recordings, the limited number of ictal events, and the short duration of interictal periods have been important obstacles for an adequate assessment of seizure forecasting. More recently, long-term continuous bilateral iEEG recordings acquired from dogs with naturally occurring focal epilepsy, using the implantable NeuroVista ambulatory monitoring device have been made available on line for the benefit of researchers. Still, an important limitation of these recordings for seizure-prediction studies was that the seizure onset zone was not disclosed/available. The first objective of this thesis was to develop an accurate seizure forecasting algorithm based on these canine ambulatory iEEG recordings. Main contributions include a quantitative, directed transfer function (DTF)-based, localization of the seizure onset zone (electrode selection), a new fitness function for the proposed genetic algorithm (feature selection), and a quasi-prospective assessment of seizure forecasting on long-term continuous iEEG recordings (total of 893 testing days). Results showed performance improvement compared to previous studies, achieving an average sensitivity of 84.82% and a time in warning of 10 %. The DTF has been previously validated for quantifying causal relations when quasistationarity requirements are met. Although such requirements can be fulfilled in the case of canine recordings due to the relatively low number of channels (objective 1), the identification of stationary segments would be more challenging in the case of high density iEEG recordings. To cope with non-stationarity issues, the spectrum weighted adaptive directed transfer function (swADTF) was recently introduced as a time-varying version of the DTF. The second objective of this thesis was to validate the feasibility of identifying sources and sinks of seizure activity based on the swADTF using high-density iEEG recordings of patients admitted for pre-surgical monitoring at the CHUM. Generators of seizure activity were within the resected volume for patients with good post-surgical outcomes, whereas different or additional seizure foci were identified in patients with poor post-surgical outcomes. Results confirmed the possibility of accurate identification of seizure origin and propagation by means of swADTF paving the way for its use in seizure prediction algorithms by allowing a more tailored electrode selection. Finally, in an attempt to explore new avenues for seizure forecasting, we proposed a new precursor of seizure activity by combining higher order spectral analysis and artificial neural networks (objective 3). Results showed statistically significant differences (p<0.05) between preictal and interictal states using all the bispectrum-extracted features. Normalized bispectral entropy, normalized squared entropy and mean of magnitude, when employed as inputs to a multi-layer perceptron classifier, achieved held-out test accuracies of 78.11%, 72.64%, and 73.26%, respectively. Results of this thesis confirm the feasibility of seizure forecasting based on iEEG recordings; the transition into the ictal state is not random and consists of a “build-up”, leading to seizures. However, additional efforts in terms of electrode selection, feature extraction, hardware and deep learning implementation, are required before the translation of current approaches into commercial devices

NOVEL GRAPHICAL MODEL AND NEURAL NETWORK FRAMEWORKS FOR AUTOMATED SEIZURE DETECTION, TRACKING, AND LOCALIZATION IN FOCAL EPILEPSY

Epilepsy is a heterogenous neurological disorder characterized by recurring and unprovoked seizures. It is estimated that 60% of epilepsy patients suffer from focal epilepsy, where seizures originate from one or more discrete locations within the brain. After onset, focal seizure activity spreads, involving more regions in the cortex. Diagnosis and therapeutic planning for patients with focal epilepsy crucially depends on being able to detect epileptic activity as it starts and localize its origin. Due to the subtlety of seizure activity and the complex spatio-temporal propagation patterns of seizure activity, detection and localization of seizure by visual inspection is time-consuming and must be done by highly trained neurologists. In this thesis, we detail modeling approaches to identify and capture the spatio-temporal ictal propagation of focal epileptic seizures. Through novel multi-scale frameworks, information fusion between signal paths, and hybrid architectures, models that capture the underlying seizure propagation phenomena are developed. The first half relies on graphical modeling approaches to detect seizures and track their activity through the space of EEG electrodes. A coupled hidden Markov model approach to seizure propagation is described. This model is subsequently improved through the addition of convolutional neural network based likelihood functions, removing the reliance on hand designed feature extraction. Through the inclusion of a hierarchical switching chain and localization variables, the model is revised to capture multi-scale seizure onset and spreading information. In the second half of this thesis, end-to-end neural network architectures for seizure detection and localization are developed. First, combination convolutional and recurrent neural networks are used to identify seizure activity at the level of individual EEG channels. Through novel aggregation, the network is trained to recognize seizure activity, track its evolution, and coarsely localize seizure onset from lower resolution labels. Next, a multi-scale network capable of analyzing the global and electrode level signals is developed for challenging task of end-to-end seizure localization. Onset location maps are defined for each patient and an ensemble of weakly supervised loss functions are used in a multi-task learning framework to train the architecture

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

Background The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. Methods A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. Results The study results show the existence of a statistically significant difference (p \u3c 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. Conclusions The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed

Review on solving the inverse problem in EEG source analysis

In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

BACKGROUND: The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. METHODS: A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. RESULTS:The study results show the existence of a statistically significant difference (p \u3c 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. CONCLUSIONS:The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed