Contexte théorique : L’encéphalopathie hypoxique ischémique (EHI) est une condition du nouveau-né dans laquelle les mécanismes des variables métaboliques ne sont pas totalement compris. Cette population est particulièrement à risque d’hypo- ou d’hyperglycémie néonatales (HHN). Devant le manque de données sur le fonctionnement métabolique à la suite d’une EHI, cette étude vise à déterminer l’association entre une HHN et l’activité cérébrale mesurée par électroencéphalographie (EEG). Méthodologie : 49 participants avec EHI ont été recrutés au CHU Sainte-Justine peu après leur naissance. Ils ont été monitorés en continu à l’aide de l’EEG et des segments d’intérêt se retrouvant dans les 48 premières heures de vie ont été analysés. L’anormalité de l’activité cérébrale est déterminée selon une analyse quantitative du niveau de discontinuité caractérisée par une proportion de faibles amplitudes (seuils de 25, 15, 12,5, 10 et 7,5 uV) dans le tracé EEG. Les données de glycémie ont été recueillies de façon intermittente par le biais de prises de sang et de glucomètres de chevet. Les participants ont été répartis en 4 groupes : normoglycémie, hyperglycémie, hypoglycémie et glycémie variable (hypo- et hyper-). Résultats : L’analyse de covariation non -paramétrique a relevé une différence significative entre les ratios de discontinuité pour le seuil de 15 uV (F = 3,070 p = 0,037). Les analyses de comparaisons appariées ont montré une différence positive entre le groupe VARIABLE et le groupe contrôle (NORMO-) pour tous les seuils ainsi qu’une différence positive entre le groupe HYPER- et le groupe contrôle pour 4 des 5 seuils (25, 15, 12,5 et 7,5 uV). Aucune différence n’a été relevé entre le groupe HYPO- et le groupe contrôle pour tous les seuils. Conclusions : La variabilité glycémique et l’hyperglycémie seule ont été montrées comme étant associées à une activité cérébrale altérée caractérisée par un tracé de plus faible amplitude mesurée avec l’EEG. / Background: Hypoxic ischemic encephalopathy (HIE) is a newborn condition in which the underlying mechanisms still require further understanding. This clinical population is particularly prone to neonatal hypo- and hyperglycemia (NHH). Given the need to improve our understanding of metabolic functioning following HIE, this study aims to determine the association of NHH on the brain’s background electrophysiological activity measured by electroencephalography (EEG). Methodology: Forty-nine newborns with HIE were recruited at Sainte-Justine University Hospital Center. Continuous EEG monitoring was started as soon as possible and segments of interest in the first 48h of life were analyzed. Brain activity was quantitatively assessed according to an index of discontinuity characterized by the proportion of low EEG amplitudes per segment (< 25, 15, 12.5, 10 and 7.5 uV cutoffs). Glucose measurements were intermittently collected using blood samples and bedside glucometers and were retrospectively retrieved from medical charts. Participants were separated in 4 groups : normoglycemia, hyperglycemia, hypoglycemia and both (hyper- and hypo-). Results: The non-parametric covariance analyses revealed a significant difference between the discontinuity index for the 15 uV threshold (F = 3.070 p = 0.037). The pairwise comparisons showed a positive difference between the group BOTH and the control group (NORMO-) for every thresholds, the labile glucose group having a higher discontinuity index. A similar difference was found between the HYPERGLYCEMIA group and the control group for 4 out 5 thresholds (25, 15, 12.5 and 7.5 uV). No difference was found between the HYPOGLYCEMIA group and the control group. Conclusion: An abnormal glycemic profile, particularly glucose lability and hyperglycemia alone, were shown to be associated with abnormal brain activity characterized by a higher discontinuity index on the EEG.

No description available.

<p>The goal of this study was to elucidate whether articulations of visual-speech are processed phonologically, and in the same manner as auditory-speech. Phonological processing, measured through the amplitude of the Phonological Mapping Negativity (PMN), was compared across three conditions using the electroencephalogram (EEG). Planned polynomial contrasts compared conditions of related and unrelated linguistic stimuli versus a non-linguistic control stimulus. A significant Site x Condition polynomial trend at posterior sites (Pz and Oz) during the N400 tine window revealed that the unrelated condition was most negative in amplitude, an N400-like deflection in the control condition reached similar negative amplitude, while the related condition was the most positive. A significant quadratic trend of PMN amplitude differentiated between the linguistic conditions and the non-linguistic control at site Fz, but did not differentiate the related and unrelated linguistic conditions from each other. These results support a conclusion that non-lexical speech-like and gurning motions of the lips are treated differently than articulations of a meaningful nature. Moreover, the PMN response patterned similarly in the linguistic conditions, compared to the non-linguistic control, indicating phonological processing. The prediction that PMN amplitude will distinguish visual-speech events congruent or incongruent to a phonologically constrained context was not supported.</p> / Master of Science (MSc)

Die Informations- und Kommunikationstechnologien haben die Arbeitswelt grundlegend verändert. Durch den Einsatz komplexer, hochautomatisierter Systeme werden an die kognitive Leistungsfähigkeit und Belastbarkeit von Arbeitnehmern hohe Anforderungen gestellt. Über die Ermittlung der psychischen Beanspruchung des Menschen an Arbeitsplätzen mit hohen kognitiven Anforderungen wird es möglich, eine Über- oder Unterbeanspruchung zu vermeiden. Gegenstand der Dissertation ist deshalb die Entwicklung, Implementierung und der Test eines neuen Systems zur kontinuierlichen Bewertung psychischer Beanspruchung an informationsintensiven Arbeitsplätzen auf Basis des Elektroenzephalogramms. Im theoretischen Teil der Arbeit werden die Konzepte zur Definition der psychischen Beanspruchung und Modelle zur Beschreibung der menschlichen Informationsverarbeitung zusammengestellt. Die Auswertung einer Reihe von Experimenten ist die Basis für die Konzeption und den Test des neuen Systems zur Indexierung der psychischen Beanspruchung. Die Aufgabenbatterie, die Stichprobenbeschreibung, der Versuchsaufbau und -ablauf sind Bestandteil des experimentellen Teils der Arbeit. Während der Aufgabenlösung wird von den Probanden das Elektroenzephalogramm mit 25 Kanälen abgeleitet. Es folgt eine Artefakteliminierung, für die ein neues automatisch und in Echtzeit arbeitendes Verfahren entwickelt wurde. Die Klassifikation und damit die Indexierung von Segmenten des Elektroenzephalogramms in die Klassen niedriger, mittlerer oder hoher Beanspruchung erfolgt auf Basis einer ebenfalls neu entwickelten Methode, deren Grundlage Dual Frequency Head Maps sind. Damit ist ein vollständiges System entstanden, das die einzelnen Verfahrensschritte integriert und die Aufgabenstellung der Arbeit erfüllt: Es kann an informationsintensiven Arbeitsplätzen eingesetzt werden, um kontinuierlich die Bewertung der psychischen Beanspruchung auf Basis des Elektroenzephalogramms vorzunehmen. / Advanced information and communication technology has fundamentally changed the working environment. Complex and highly automated systems impose high demands on employees with respect to cognitive capacity and the ability to cope with workload. The registration of mental workload of employees on-site at workplaces with high cognitive demands enables preventing over- or underload. The subject of this dissertation is therefore the development, implementation and testing of a novel system for continuous assessment of mental workload at information intensive workplaces on the basis of the electroencephalogram. In the theoretical section of the thesis concepts for defining mental workload are given; furthermore, models for describing human information processing are introduced and the relevant terminology such as strain, workload, and performance is clarified. Evaluation of an array of experiments with cognitive tasks forms the basis for the conceptual design and testing of the novel system for indexing mental workload. Descriptions of these tasks, the sample, the experimental set-up and procedure are included in the experimental section. The electroencephalogram with 25 channels was recorded from the subjects while performing the tasks. Subsequently, an artifact elimination was carried out, for which a new, automated, and real-time capable procedure has been developed. Segments from the electroencephalogram are classified and thusly indexed into classes of low, medium, and high workload on the basis of a likewise newly developed method, whose central element are Dual Frequency Head Maps. Hence, a complete system emerges that integrates the single processing steps and satisfies the scope of this thesis: It can be applied on-site at information intensive workplaces for continuous assessment of mental workload on the basis of the electroencephalogram.

Trotz zahlreicher Untersuchungen lässt sich bei der Zwangsstörung noch kein einheitliches, alle Befunde integrierendes Krankheitsmodell formulieren. Die Verarbeitung von emotionalen Reizen könnte bei Personen mit Zwangsstörungen verändert sein. Dies trägt möglicherweise zur Entwicklung und Aufrechterhaltung der Störung bei. Das Ziel der vorliegenden Arbeit war es, spezifische Komponenten dieser Verarbeitung zu untersuchen. Zuerst wurde in zwei unterschiedlichen Studien überprüft, ob die Orientierung der Aufmerksamkeit zu neuen Reizen bei Patienten mit Zwangsstörungen verstärkt ist. Zu diesem Zweck wurden durch neue Reize evozierte Potentiale im Elektroenzephalogramms (EEG) gemessen. Anschließend wurde in einer Studie überprüft, ob das Verhältnis der Aktivierungen von dem Vermeidungs- zum Annäherungssystem bei den Betroffenen verändert ist. Dies lässt sich an Hand der Ermittlung der hemisphärischen Verteilung von Alpha-Wellen in frontalen Hirnregionen feststellen. Die Ergebnisse der ersten beiden Studien ergaben, dass Patienten unabhängig vom emotionalen Kontext eine stärkere Aufmerksamkeitshinwendung zu neuen Reizen zeigen (Studie 1), was allerdings nicht beobachtet wurde, wenn die neuen Reize innerhalb des Aufmerksamkeitsfokus lagen (Studie 2). Dieses Ergebnis wurde als überaktives Gefahrenerkennungssystem bei Patienten interpretiert. Weiterhin ließ sich feststellen, dass Patienten im Vergleich zu gesunden Kontrollen in frontalen Hirnregionen eine Verlagerung der Alpha Asymmetrie zur linken Gehirnhemisphäre aufwiesen (Studie 3). Dieser Befund wurde unabhängig von einer Stimulierung durch emotionale Reize gemacht. Er lässt sich als stärkere Aktivierung des Vermeidungs- im Verhältnis zum Annäherungssystem deuten. Zusammengefasst zeigte sich bei Patienten mit Zwangsstörungen eine veränderte Verarbeitung von emotionalen Reizen. Aus diesen Befunden können spezifische Empfehlungen für die Behandlung der Störung abgeleitet werden. / It is so not possible to formulate a disease model of obsessive-compulsive disorder (OCD) that integrates all the results of the many studies carried out. The neural processing of emotional stimuli might be altered in people with OCD. This might play an important role in the development and maintenance of OCD. The present work aimed to investigate specific components of the neural processing of emotional stimuli. The first two studies examined if the orienting of attention towards novel stimuli is enhanced in patients with OCD. For this purpose the event-related brain potentials evocated by novel stimuli in the electroencephalogram were recorded. The third study tested if the relationship between the withdrawal-avoidance mode and the approach mode of the motivational brain system is altered in OCD patients. This can be assessed by the hemispheric distribution of alpha power in frontal brain regions. The results of the first two studies showed that the orienting of attention towards novel stimuli is enhanced in patients with OCD independently of the emotional context condition (study 1), which could not be observed if the novel stimuli were listened to in active attentional mode (study 2). This was interpreted as a hypersensitive threat detection system. Further, it was found that patients showed a shift of frontal alpha activity to the left hemisphere compared to healthy control subjects (study 3). This result was independent of the viewing of emotional stimuli. It can be concluded that the avoidance mode is relatively increased in patients with OCD. Taken together, patients showed an altered neural processing of emotional stimuli. Specific recommendations for he treatment of the disorder can be drawn out of them.

L'étude par neuro-imagerie de l'activité de plusieurs cerveaux en interaction (hyperscanning) permet d'étendre notre compréhension des neurosciences sociales. Nous proposons un cadre pour l'hyperscanning utilisant les interfaces cerveau-ordinateur multi-utilisateur qui inclut différents paradigmes sociaux tels que la coopération ou la compétition. Les travaux de cette thèse comportent trois contributions interdépendantes. Notre première contribution est le développement d'une plateforme expérimentale sous la forme d'un jeu vidéo multijoueur, nommé Brain Invaders 2, contrôlé par la classification de potentiels évoqués visuels enregistrés par électroencéphalographie (EEG). Cette plateforme est validée par deux protocoles expérimentaux comprenant dix-neuf et vingt-deux paires de sujets et utilise différentes approches de classification adaptative par géométrie riemannienne. Ces approches sont théoriquement et expérimentalement comparées et nous montrons la supériorité de la fusion des classifieurs indépendants sur la classification d'un hypercerveau durant la seconde contribution. L'analyse de coïncidence des signaux entre les individus est une approche classique pour l'hyperscanning, elle est pourtant difficile quand les signaux EEG concernés sont transitoires avec une grande variabilité (intra- et inter-sujet) spatio-temporelle et avec un faible rapport signal-à-bruit. En troisième contribution, nous proposons un nouveau modèle composite de séparation aveugle de sources physiologiquement plausibles permettant de compenser cette variabilité. Une solution par diagonalisation conjointe approchée est proposée avec une implémentation d'un algorithme de type Jacobi. A partir des données de Brain Invaders 2, nous montrons que cette solution permet d'extraire simultanément des sources d'artéfacts, des sources d'EEG évoquées et des sources d'EEG continues avec plus de robustesse et de précision que les modèles existants. / The study of several brains interacting (hyperscanning) with neuroimagery allows to extend our understanding of social neurosciences. We propose a framework for hyperscanning using multi-user Brain-Computer Interfaces (BCI) that includes several social paradigms such as cooperation or competition. This dissertation includes three interdependent contribution. The first contribution is the development of an experimental platform consisting of a multi-player video game, namely Brain Invaders 2, controlled by classification of visual event related potentials (ERP) recorded by electroencephalography (EEG). The plateform is validated through two experimental protocols including nineteen and twenty two pairs of subjects while using different adaptive classification approaches using Riemannian geometry. Those approaches are theoretically and experimentally compared during the second contribution ; we demonstrates the superiority in term of accuracy of merging independent classifications over the classification of the hyperbrain during the second contribution. Analysis of inter-brain synchronizations is a common approach for hyperscanning, however it is challenging for transient EEG waves with an great spatio-temporal variability (intra- and inter-subject) and with low signal-to-noise ratio such as ERP. Therefore, as third contribution, we propose a new blind source separation model, namely composite model, to extract simultaneously evoked EEG sources and ongoing EEG sources that allows to compensate this variability. A solution using approximate joint diagonalization is given and implemented with a fast Jacobi-like algorithm. We demonstrate on Brain Invaders 2 data that our solution extracts simultaneously evoked and ongoing EEG sources and performs better in term of accuracy and robustness compared to the existing models.

<p>A Real-Time Brain-Computer Interface is a technical system classifying increased or decreased brain activity in Real-Time between different body movements, actions performed by a person. Focus in this thesis will be on testing algorithms and settings, finding the initial time interval and how increased activity in the brain can be distinguished and satisfyingly classified. The objective is letting the system give an output somewhere within 250ms of a thought of an action, which will be faster than a persons reaction time. </p><p>Algorithms in the preprocessing were Blind Signal Separation and the Fast Fourier Transform. With different frequency and time interval settings the algorithms were tested on an offline Electroencephalographic data file based on the "Ten Twenty" Electrode Application System, classified using an Artificial Neural Network. </p><p>A satisfying time interval could be found between 125-250ms, but more research is needed to investigate that specific interval. A reduction in frequency resulted in a lack of samples in the sample window preventing the algorithms from working properly. A high frequency is therefore proposed to help keeping the sample window small in the time domain. Blind Signal Separation together with the Fast Fourier Transform had problems finding appropriate correlation using the Ten-Twenty Electrode Application System. Electrodes should be placed more selectively at the parietal lobe, in case of requiring motor responses.</p>

A Real-Time Brain-Computer Interface is a technical system classifying increased or decreased brain activity in Real-Time between different body movements, actions performed by a person. Focus in this thesis will be on testing algorithms and settings, finding the initial time interval and how increased activity in the brain can be distinguished and satisfyingly classified. The objective is letting the system give an output somewhere within 250ms of a thought of an action, which will be faster than a persons reaction time. Algorithms in the preprocessing were Blind Signal Separation and the Fast Fourier Transform. With different frequency and time interval settings the algorithms were tested on an offline Electroencephalographic data file based on the "Ten Twenty" Electrode Application System, classified using an Artificial Neural Network. A satisfying time interval could be found between 125-250ms, but more research is needed to investigate that specific interval. A reduction in frequency resulted in a lack of samples in the sample window preventing the algorithms from working properly. A high frequency is therefore proposed to help keeping the sample window small in the time domain. Blind Signal Separation together with the Fast Fourier Transform had problems finding appropriate correlation using the Ten-Twenty Electrode Application System. Electrodes should be placed more selectively at the parietal lobe, in case of requiring motor responses.

A recent model of Parkinson’s disease (PD) suggests that the neuropathological, behavioural and cognitive symptoms progress in stages. There is substantial evidence for a prodromal stage of PD, during which time pre-motor symptoms develop. Rapid eye movement (REM) sleep behaviour disorder (RBD) is a risk factor for developing PD and may be part of the pre-motor stage. In both disorders, neuropathological α-synuclein aggregates are thought to be a direct cause of the resulting symptoms. One model has shown that in rats, proteasome inhibition produced by systemic exposure to environmental toxins results in α-synuclein pathology and motor behaviour dysfunction that mimics the progression of PD in humans. The present study examined the hypothesis that the systemic proteasome inhibition model would produce pre-Parkinsonian RBD-like pathology in rats. It was expected that sleep disturbances would be seen prior to behavioural disturbances in rats treated systemically with PSI (a proteasome inhibitor). Following baseline sleep recording and training on the inclined beam-traverse task, rats were injected with PSI (a proteasome inhibitor) or ethanol (control), 6 times over 2 wk. Sleep recording over 8 wk and behavioural testing over 16 wk provided no evidence of sleep disturbances or motor dysfunction. Post-mortem immunohistochemical analyses of brain tissue provided no evidence of PSI-associated α-synuclein aggregates in the locus coeruleus, subcoeruleus (dorsal part), or substantia nigra (areas involved in RBD and/or PD). These results did not provide support for RBD as a prodromal phase of PD within the systemic proteasome inhibitor-based model and add to a growing body of research reporting inconsistent findings using this model. We suggest that systemic PSI exposure in rats does not produce a viable model of RBD or PD. Whether RBD is an early symptom in the progression of PD remains to be established. / Thesis (Master, Neuroscience Studies) -- Queen's University, 2010-04-28 12:04:50.613

For most of the naturally occurring signals, especially biomedical signals, the underlying physical process generating the signal is often not fully known, making it difficult to obtain a parametric model. Therefore, signal processing techniques are used to analyze the signal for non-parametrically characterizing the underlying system from which the signals are produced. Most of the real life systems are nonlinear and time varying, which poses a challenge while characterizing them. Additionally, multiple sensors are used to extract signals from such systems, resulting in multichannel signals which are inherently coupled. In this thesis, we counter this challenge by using Recurrence Plot based techniques for characterizing biomedical systems such as heart or brain, using signals such as heart rate variability (HRV), electroencephalogram(EEG) or functional magnetic resonance imaging (fMRI), respectively, extracted from them. In time series analysis, it is well known that a system can be represented by a trajectory in an N-dimensional state space, which completely represents an instance of the system behavior. Such a system characterization has been done using dynamical invariants such as correlation dimension, Lyapunov exponent etc. Takens has shown that when the state variables of the underlying system are not known, one can obtain a trajectory in ‘phase space’ using only the signals obtained from such a system. The phase space trajectory is topologically equivalent to the state space trajectory. This enables us to characterize the system behavior from only the signals sensed from them. However, estimation of correlation dimension, Lyapunov exponent, etc, are vulnerable to non-stationarities in the signal and require large number of sample points for accurate computation, both of which are important in the case of biomedical signals. Alternatively, a technique called Recurrence Plots (RP) has been proposed, which addresses these concerns, apart from providing additional insights. Measures to characterize RPs of single and two channel data are called Recurrence Quantification Analysis (RQA) and cross RQA (CRQA), respectively. These methods have been applied with a good measure of success in diverse areas. However, they have not been studied extensively in the context of experimental biomedical signals, especially multichannel data. In this thesis, the RP technique and its associated measures are briefly reviewed. Using the computational tools developed for this thesis, RP technique has been applied on select single channel, multichannel and multimodal (i.e. multiple channels derived from different modalities) biomedical signals. Connectivity analysis is demonstrated as post-processing of RP analysis on multichannel signals such as EEG and fMRI. Finally, a novel metric, based on the modification of a CRQA measure is proposed, which shows improved results. For the case of single channel signal, we have considered a large database of HRV signals of 112 subjects recorded for both normal and abnormal (anxiety disorder and depression disorder) subjects, in both supine and standing positions. Existing RQA measures, Recurrence Rate and Determinism, were used to distinguish between normal and abnormal subjects with an accuracy of 58.93%. A new measure, MLV has been introduced, using which a classification accuracy of 98.2% is obtained. Correlation between probabilities of recurrence (CPR) is a CRQA measure used to characterize phase synchronization between two signals. In this work, we demonstrate its utility with application to multimodal and multichannel biomedical signals. First, for the multimodal case, we have computed running CPR (rCPR), a modification proposed by us, which allows dynamic estimation of CPR as a function of time, on multimodal cardiac signals (electrocardiogram and arterial blood pressure) and demonstrated that the method can clearly detect abnormalities (premature ventricular contractions); this has potential applications in cardiac care such as assisted automated diagnosis. Second, for the multichannel case, we have used 16 channel EEG signals recorded under various physiological states such as (i) global epileptic seizure and pre-seizure and (ii) focal epilepsy. CPR was computed pair-wise between the channels and a CPR matrix of all pairs was formed. Contour plot of the CPR matrix was obtained to illustrate synchronization. Statistical analysis of CPR matrix for 16 subjects of global epilepsy showed clear differences between pre-seizure and seizure conditions, and a linear discriminant classifier was used in distinguishing between the two conditions with 100% accuracy. Connectivity analysis of multichannel EEG signals was performed by post-processing of the CPR matrix to understand global network-level characterization of the brain. Brain connectivity using thresholded CPR matrix of multichannel EEG signals showed clear differences in the number and pattern of connections in brain connectivity graph between epileptic seizure and pre-seizure. Corresponding brain headmaps provide meaningful insights about synchronization in the brain in those states. K-means clustering of connectivity parameters of CPR and linear correlation obtained from global epileptic seizure and pre-seizure showed significantly larger cluster centroid distances for CPR as opposed to linear correlation, thereby demonstrating the efficacy of CPR. The headmap in the case of focal epilepsy clearly enables us to identify the focus of the epilepsy which provides certain diagnostic value. Connectivity analysis on multichannel fMRI signals was performed using CPR matrix and graph theoretic analysis. Adjacency matrix was obtained from CPR matrices after thresholding it using statistical significance tests. Graph theoretic analysis based on communicability was performed to obtain community structures for awake resting and anesthetic sedation states. Concurrent behavioral data showed memory impairment due to anesthesia. Given the fact that previous studies have implicated the hippocampus in memory function, the CPR results showing the hippocampus within the community in awake state and out of it in anesthesia state, demonstrated the biological plausibility of the CPR results. On the other hand, results from linear correlation were less biologically plausible. In biological systems, highly synchronized and desynchronized systems are of interest rather than moderately synchronized ones. However, CPR is approximately a monotonic function of synchronization and hence can assume values which indicate moderate synchronization. In order to emphasize high synchronization/ desynchronization and de-emphasize moderate synchronization, a new method of Correlation Synchronization Convergence Time (CSCT) is proposed. It is obtained using an iterative procedure involving the evaluation of CPR for successive autocorrelations until CPR converges to a chosen threshold. CSCT was evaluated for 16 channel EEG data and corresponding contour plots and histograms were obtained, which shows better discrimination between synchronized and asynchronized states compared to the conventional CPR. This thesis has demonstrated the efficacy of RP technique and associated measures in characterizing various classes of biomedical signals. The results obtained are corroborated by well known physiological facts, and they provide physiologically meaningful insights into the functioning of the underlying biological systems, with potential diagnostic value in healthcare.