Contexte : La régulation émotionnelle est un ensemble de processus responsables du contrôle, de l’évaluation et de l’ajustement des émotions dans un objectif. Les résultats d’imagerie fonctionnelle s’accordent sur l’implication des structures frontales et limbiques tandis que les résultats en neurophysiologie, encore rares, suggèrent un rôle du rythme alpha dans l’induction émotionnelle et du rythme thêta dans la régulation. Objectifs et hypothèses : Notre objectif était d’étudier le rythme thêta et alpha pendant la réévaluation de stimuli déplaisants. Nous avons émis l’hypothèse que l’activité alpha serait modulée lors de l’induction émotionnelle seulement tandis que l’activité thêta préfrontale serait positivement corrélée à une régulation réussie. Méthode : Vingt-quatre participants sains ont été enregistrés avec 64 électrodes EEG alors qu’ils regardaient passivement ou réévaluaient des images négatives et neutres. Les rythmes thêta et alpha ont été comparés lors de l’induction émotionnelle puis dans les conditions de maintien, de diminution et d’augmentation de l’émotion, et une localisation de la source a estimé les générateurs. Résultats : Le rythme alpha était non sensible à l’induction et à la régulation. L’activité thêta était systématiquement plus élevée dans la condition de régulation à la hausse que dans la condition de maintien (p=.04) principalement au début de la régulation (1-3 sec) pour thêta bas et plus tard (5-7 sec) pour le thêta haut avec comme générateur du thêta bas le gyrus frontal moyen et le cortex cingulaire antérieur dorsal. Conclusion : Le rythme thêta était impliqué dans les processus de réévaluation à la hausse de l’émotion. / Context: Emotion regulation is a set of processes responsible for controlling, evaluating and adjusting reactions to achieve a goal. Results derived from magnetic resonance imaging agreed on the involvement of frontal and limbic structures in this process. Findings using cognition and physiology interactions are still scarce but suggest a role for alpha rhythm in emotional induction and theta in regulation. Objectives and hypotheses: Our goal was to investigate theta and alpha rhythm during the reappraisal of aversive stimuli. We hypothesized that an implication of alpha rhythm in emotional induction only and an increase in prefrontal theta rhythm positively correlated with successful regulation. Method: Twenty-four healthy participants were recorded with 64 EEG electrodes while asked to passively watch or reappraise negative pictures. Theta and alpha rhythms were compared across maintain, decrease and increase regulation conditions, and a source localization estimated the generators. Results: Theta activity was consistently higher in the upregulation than in the maintenance condition (p=.04) for the entire control period, but mainly at the beginning of regulation (1-3 sec) for low-theta and later (5-7 sec) for high-theta. Moreover, our results confirm that a low-theta generator correlated with mainly the middle frontal gyrus and the dorsal anterior cingulate cortex during upregulation. Theta was sensitive to emotion upregulation, whereas the alpha oscillation was non-sensitive to emotion induction and regulation. Conclusion: The low-theta rhythm was involved explicitly in emotion upregulation processes that occur at a definite time during reappraisal, whereas the alpha rhythm was not altered by emotion induction and regulation.

<p>  </p> <p>Hazard identification is one of the most significant components in safety management at construction jobsites to prevent undesired fatalities and injuries of construction workers. The current practice, which relies on a limited number of safety managers’ manual and subjective inspections, and existing research efforts analyzing workers’ physical and physiological signals have achieved limited success, leaving many hazards unidentified at the jobsites. Motivated by this critical need, this research aims to develop a human sensing network that allows for ubiquitous hazard identification in the construction workplace.</p> <p>To attain this overarching goal, this research analyzes construction workers’ collective EEG signals collected from wearable EEG sensors based on machine learning, virtual reality (VR), and advanced signal processing techniques. Three specific research objectives are: (1) establishing a relationship between EEG signals and the existence of construction hazards, (2) identifying correlations between EEG signals/physiological states (e.g., emotion) and different hazard types, and (3) developing an integrated platform for real-time construction hazard mapping and comparing the results developed based on VR and real-world experimental settings.</p> <p>Specifically, the first objective establishes the relationship by investigating the feasibility of identifying construction hazards using a binary EEG classifier developed in VR, which can capture EEG signals associated with perceived hazards. In the second objective, correlations are discovered by testing the feasibility of differentiating construction hazard types based on a multi-class classifier constructed in VR. In the first and second objectives, the complex relationships are also analyzed in terms of brain dynamics and EEG signal components. In the third objective, the platform is developed by fusing EEG signals with heterogeneous data (e.g., location), and the discrepancies in VR and real-world environments are quantitatively assessed in terms of hazard identification performance and human behavioral responses.</p> <p>The primary outcome of this research is that the proposed approach can be applied to actual construction jobsites and used to detect all potential hazards, which was challenging to be achieved based on the current practice and existing research efforts. Also, the human cognitive mechanisms revealed in this research discover new neurocognitive knowledge in construction workers’ hazard perception. As a result, this research contributes to enhancing current hazard identification capability and improving construction workers’ safety and health.</p>

La vengeance réfère à la tentative de blesser ou de faire du mal à celui qui nous a causé du tort par sa faute. Alors que la vengeance se rapporte à l’action, le désir de vengeance réfère à l’émotion qui motive à la vengeance. La colère est une émotion ressentie lorsque nous subissons un dommage (interférence subite à la poursuite d’un but que nous tenons à coeur), alors que la rancune est une émotion qui est suscitée par la perception d’avoir ou le fait d’avoir réellement souffert d’une faute (préjudice qui est infligé de manière responsable d’un individu à une victime). Ces définitions de la colère et de la rancune peuvent se traduire de manière opérationnelle par le fait de provoquer des participants de façon qu’ils perçoivent cela comme accidentelle (sans faute) ou personnelle (avec faute) ; la première provocation induirait un état émotionnel de colère alors que la seconde de rancune. Les études passées ont démontré l’effet de la colère sur le taux de rejets d’offres monétaires très injustes et moyennement injustes comparativement aux offres justes lors d’une tâche de prise de décision économique tel que la tâche Ultimatum Game (UG) ainsi que sur l’amplitude de la Feedback-Related Negativity (FRN), une composante de potentiel reliés aux évènements qui devient plus prononcée lors d’une rétroaction négative associée à des résultats défavorable (ex., réponses incorrectes ou pertes monétaires). Ces données suggèrent que la colère augmenterait l’évaluation affective négative associée aux offres très injustes et moyennement injustes et les comportements de vengeance associés aux taux de rejet. Le rôle des émotions dans la vengeance pose la question de savoir si leur influence se transmet directement dans les comportements de vengeance. Le trait de vengeance réfère à la tendance dispositionnelle à entretenir des attitudes positives envers la vengeance et à la rechercher en réponse à des provocations. Les études antérieures ont démontré que le trait d’affects négatifs modérait la relation entre l'état affects négatifs et l’ampleur de la FRN. Il y a un manque dans nos connaissances sur le rôle du trait de vengeance sur la relation entre l’amplitude de la FRN et le taux de rejet d’offres monétaires. Le premier objectif de la présente étude est de comparer les effets de la colère à ceux de la rancune sur le taux de rejets d’offres monétaires justes, moyennement injustes et très injustes ainsi que sur la FRN durant la tâche UG. Le second objectif est de vérifier le rôle de modérateur du trait de vengeance sur la relation entre la FRN et le taux de rejet des offres, et ce en réponse à des offres très injustes et moyennement injustes. Deux groupes expérimentaux ont été créés : le groupe Colère (GrC) et le groupe Rancune (GrR). Le premier groupe a subi une induction de colère par le biais d'une provocation humaine sans faute pendant la réalisation d'une tâche de créativité. Au cours de la même tâche de créativité, les participants du second groupe ont été soumis à une induction de rancune par le biais d'une provocation humaine avec faute. Ensuite, il a été demandé aux participants de participer à la tâche UG, dans lequel ils devaient accepter ou rejeter des offres monétaires très injustes, moyennement injustes et justes proposées par un participant fictif. En plus de l’analyse du taux de rejet, le signal électroencéphalographique (EEG) a été quantifié puis analysé sur la FRN dans le but de servir de proxy de la perception d’injustice et du niveau de complexité de la prise de décision en fonction du groupe de participants et du type d’offres. Contrairement à ce qui était attendu, les résultats indiquent que le taux de rejet des participants du GrC devant les offres moyennement injustes est significativement plus élevés que celui des participants GrR. De plus, les résultats ne démontrent pas une amplitude FRN significativement plus grande chez les participants GrR comparée à celle chez les participants GrC, et ceci peu importe le type d’offres. De manière congruente avec la littérature, la FRN associée aux offres très injustes et moyennement injustes est plus négative que celle associée aux offres justes. Toutefois, dans le cadre de la présente étude, ce résultat sur la FRN a été observé uniquement pour le GrC. Enfin, que ce soit en réponse à des offres très injustes ou moyennement injustes, les résultats ne démontrent pas le rôle modérateur du trait de vengeance dans la relation entre l’amplitude de la FRN et le taux de rejet d’offres monétaires. L’effet de groupe observé sur le taux de rejet des offres moyennement injustes suggère que les personnes en colère résolvent leur conflit cognitif davantage en outre-passant leurs intérêts personnels monétaires comparativement aux personnes qui vivent de la rancune. Ces résultats suggèrent aussi que, contrairement aux individus en état de colère qui perçoivent les offres justes d’une façon différente des autres types d’offres, les individus vivant de la rancune perçoivent les offres justes, moyennement injustes et très injustes de la même façon. Il est possible de croire que l’état de rancune augmente la sensibilité à l’injustice envers des offres qui normalement devrait être perçues comme différentes. Des limites méthodologiques peuvent possiblement expliquer l’absence d’effet de modération du trait de vengeance. / Vengeance refers to the attempt to hurt or harm someone who has caused us harm through their wrong. While revenge refers to action, the desire for vengeance refers to the emotion that motivates revenge. Anger is an emotion felt when we suffer from a perceived or actual harm (sudden interference in the pursuit of an important goal for us), while resentment is an emotion that is aroused by the perception of having or the fact of having actually suffered from a wrong (harm that is responsibly inflicted by an individual on a victim). These definitions of anger and resentment can be operationalized by provoking participants in such a way that they perceive it as accidental (without wrong) or personal (with wrong); the first provocation would induce an emotional state of anger while the second would induce a state of resentment. Past studies have demonstrated the effect of anger on the rejection rate of unfair and mid-value offers compared to fair offers during an economic decision-making task such as the Ultimatum Game (UG), as well as on the amplitude of Feedback-Related Negativity (FRN), an event-related potential that becomes more pronounced during negative feedback associated with unfavorable outcomes (e.g., incorrect responses or monetary losses). These prior studies suggest that anger would increase the negative affective evaluation associated with unfair and mid-value offers and vengeance behaviors associated with rejection rates. The role of emotions in vengeance raises the question of whether their influence is transmitted directly into revenge behaviors. Trait vengeance refers to the dispositional tendency to maintain positive attitudes toward revenge and to seek it in response to provocations. Previous studies demonstrated that trait negative affect moderated the relationship between state negative affect and FRN magnitude. There is a gap in our knowledge about the role of trait vengeance on the relationship between the FRN amplitude and the rejection rate of monetary offers. The first objective of the current study is to compare the effects of anger to those of resentment on the rejection rate of fair, mid-value and unfair offers as well as on the FRN amplitude during the UG. The second objective is to verify the moderating role of trait vengeance on the relationship between FRN amplitude and the rejection rate in response to unfair and mid-value offers. Two experimental groups were created: a group primed with a human provocation without wrong (Unwronged) and a second group primed with a human provocation with wrong (Wronged). The first group underwent anger induction through a human provocation without wrong while performing a creativity task. During the same creativity task, participants in the second group were subjected to a resentment induction through a human provocation with wrong. Next, participants were asked to participate in the UG, in which they had to accept or reject unfair, mid-value, and fair offers proposed by a fictitious participant. In addition to the analysis of the rejection rate, the electroencephalographic (EEG) signal was quantified and then analyzed on the FRN amplitude with the aim of serving as a proxy for the perception of injustice and the level of complexity of decision-making based on the experimental groups and the type of offers. Contrary to what was expected, the results indicate that the rejection rate of the Unwronged group in response to mid-value offers is significantly higher than the Wronged group. Furthermore, the results do not demonstrate a significantly greater FRN amplitude in the Wronged group compared to the Unwronged group, regardless of the type of offers. In accordance with the literature, the FRNs associated with unfair and mid-value offers are more negative compared to the FRN associated with fair offers. However, in the context of the present study, this result on FRN was observed only for the Unwronged group. Finally, whether in response to unfair or mid-value offers, the results do not demonstrate the moderating role of the trait vengeance in the relationship between the FRN amplitude and the rejection rate. The group effect observed on the rejection rate of mid-value offers suggests that angry participants resolve their cognitive conflict more by overriding their personal monetary interests compared to participants who feel resentment. These results also suggest that, unlike individuals in a state of anger who perceive fair offers in a different way from other types of offers, individuals feeling resentment perceive fair, mid-value and unfair offers in the same way. It is possible to believe that the state of resentment increases sensitivity to injustice towards offers that should normally be perceived as different. Methodological limitations can possibly explain the lack of moderation effect of the trait vengeance.

L'objectif de ces travaux de recherche est l'étude et le développement d'un système ICM embarqué en utilisant la méthodologie de conception conjointe afin de satisfaire ses contraintes spécifiques. Il en a découlé la constitution d'un système ICM complet intégrant un système d'acquisition OpenBCI et un système de traitement à base de FPGA. Ce système pourrait être utilisé dans des contextes variés : médicale (pour les diagnostiques précoces des pathologies), technologique (informatique ubiquitaire), industriel (communication avec des robots), ludique (contrôler un joystick dans les jeux vidéo), etc. Dans notre contexte d’étude, la plateforme ICM proposée a été réalisée pour assister les personnes à mobilité réduite à commander les équipements domestiques. Nous nous sommes intéressés en particulier à l'étude et à l'implémentation des modules de filtrage adaptatif et dynamique, sous forme d'un coprocesseur codé en HDL afin de réduire son temps d'exécution car c'est le bloc le plus critique de la chaine ICM. Quant aux algorithmes d'extraction des caractéristiques et de classification, ils sont exécutés par le processeur Nios-II sous son système d'exploitation en ANSI-C. Le temps de traitement d'un trial par notre système ICM réalisé est de l'ordre de 0.4 s/trial et sa consommation ne dépasse guère 0.7 W. / The main purpose of this thesis is to study and develop an embedded brain computer interface (BCI) system using HW/SW methodology in order to satisfy the system specifications. A complete BCI system integrated in an acquisition system (OpenBCI) and a hardware platform based on the FPGA were achieved. The proposed system can be used in a variety of contexts: medical (for early diagnosis of pathologies, assisting people with severe disabilities to control home devices system through thought), technological (ubiquitous computing), industrial (communication with Robots), games (control a joystick in video games), etc. In our study, the proposed ICM platform was designed to control home devices through the thought of people with severe disabilities. A particular attention has been given to the study and implementation of the filtering module, adaptive and dynamic filtering, in the form of a coprocessor coded in HDL in order to reduce its execution time as it is the critical block in the returned ICM algorithms. For the feature extraction and classification algorithms, they are executed in the Nios-II processor using ANSI-C language. The prototype operates at 200 MHz and performs a real time classification with an execution delay of 0.4 second per trial. The power consumption of the proposed system is about 0.7 W.

Physiological signals, electroencephalogram (EEG) and heart rate variability (HRV), are generated by complex self-regulating systems. These signals are extremely inhomogeneous and nonstationary, and fluctuate in an irregular and highly complex manner. These fluctuations are due to underlying dynamics of the system. The synchronous neural activity measured as scalp EEG indicates underlying neural dynamics of the brain. Hence, quantitative EEG analysis has become a very useful tool in interpreting results from physiological experiments. The analysis of HRV provides valuable information to assess the autonomous nervous system (ANS). The HRV can be significantly affected by physiological state changes and many disease states. Hence, HRV analysis is becoming a major experimental and diagnostic tool. In this thesis, we focus on the study of EEG and HRV time series using tools from nonlinear time series analysis with special emphasis on its implications in detecting physiological state changes such as, in diseases like epileptic seizure and schizophrenia, and in altered states of consciousness as in sleep and meditation. The proposed nonlinear techniques are used in discriminating different physiological states from control states. Artifact processing of EEG signal Interferences (artifacts) from various sources unavoidably contaminate EEG recordings. In quantitative analysis, results can differ significantly by these artifacts, which may lead to wrong interpretation of the results. In this part of the thesis, we have devised methods to minimize ocular and muscle artifacts in EEG. The artifact correction methods are based on blind source separation (BSS) techniques such as singular value decomposition (SVD), algorithm for multiple signal extraction (AMUSE), canonical correlation analysis (CCA), information maximization (INFOMAX) independent component analysis (ICA) and joint approximate diagonalization of eigen-matrices (JADE) ICA. We have proposed a method to simulate clean and artifact corrupted EEG data based on the BSS methods. In order to enhance the performance of BSS methods, a technique called wavelet-filtered component inclusion method has been introduced. In addition, second-order statistics (SOS) and higher-order statistics (HOS) based BSS methods have been studied considering less number of EEG channels; and performance comparison of these methods has also been made. We have also addressed the problem of simultaneous correction of ocular and muscle artifacts in EEG recordings using the BSS methods. Irrespective of the BSS methods, the component elimination method has introduced high spectral error in all the bands after reconstruction of clean EEG. However, the wavelet filtered component inclusion method has retained almost all spectral powers of EEG channels in theta, alpha, and beta bands after ocular artifact minimization. When the number of EEG channels is very less, the enhanced CCA (SOS BSS) has given superior artifact minimization results than HOS BSS methods, especially in delta band. The component elimination method is used in muscle artifact minimization, and hence the SVD method cannot be used for this purpose since it leads to large spectral distortion of reconstructed EEG. The AMUSE and CCA methods have given comparable performance in muscle artifact minimization. In addition, the JADE method has introduced less mean spectral error compared to other methods. The CCA method has shown superior performance in simultaneous minimization of ocular and muscle artifacts, and AMUSE and JADE methods have given comparable results. Furthermore, the less computation time of wavelet enhanced SOS BSS methods make them very useful in real clinical environments. Fractal characterization of time series In biomedical signal analysis, fractal dimension (FD) is used as a quantitative measure to estimate complexity of physiological signals. Such analysis helps to study physiological processes of underlying systems. The FD can also be used to study dynamics of transitions between different states of systems like brain and ANS, in various physiological and pathological states. In this part, we have proposed a method to estimate FD of time series, called multiresolution box-counting (MRBC) method. A modification of this method resulted in multiresolution length (MRL) method. The estimation performance of the proposed methods is compared with that of Katz, Sevcik, and Higuchi methods, by simulating mathematically defined fractal signals, and also the computation time is compared between the methods. The MRBC and MRL methods have given comparable performance to that of Higuchi method, in estimating FD of waveforms, with the advantage of less computational time. In addition, various properties of the FD are studied and discussed in connection with classical signal processing concepts such as amplitude, frequency, sampling frequency, effect of noise, band width, correlation, etc. The FD value of signals has increased with number of harmonics, noise variance, band-width, and mid-band frequency, and decreased with degree of correlation in AR signal. An analogy between Katz FD and smoothed Teager energy operator has also been made. Application of fractal analysis to EEG and HRV time series The fluctuation of EEG potentials normally depends upon degree of alertness, and varies in amplitude and frequency. Hence, the EEG is an important clinical tool for studying sleep and sleep related disorders, epileptic seizures, schizophrenia, and meditation. In this part of the thesis, we have used FD which gives signal complexity, and detrended fluctuation analysis (DFA) which gives multiscale exponent of time series to quantify EEG. We have extended the concept of FD to multiscale FD to compute complexity of time series at multiple scales. The main applications of the proposed method are epileptic seizure detection, sleep stage detection, schizophrenia EEG analysis, and analysis of heart rate variability during meditation. For seizure detection, we have used intracranial EEG recordings with seizure-free and seizure intervals. In sleep EEG analysis, whole-night sleep EEG is used and results are compared with the manually scored hypnogram. The schizophrenia symptom is further categorized into positive and negative symptoms and complexity is estimated using FD and DFA. We have also analyzed HRV data of Chi and Kundalini meditation using FD and DFA techniques. In all the applications considered, we have tested for statistical significance of the computed parameters, between the case of interest and corresponding control cases, to discriminate between the physiological states. The ocular artifact has reduced FD while muscle artifact increased FD of EEG. The FD of seizure EEG has shown high value compared to that of seizure-free EEG. In addition, the seizure-free EEG has more DFA exponent-1 than seizure EEG. The value of FD of EEG is decreased with deepening of sleep, wake state having high FD value. The FD of REM state sleep EEG showed value between that of wake and state-1. The DFA exponent-1 has increased with deepening of sleep state, having small value for wake state. The REM state has given exponent-1 value between wake and state-1. The schizophrenia subjects have shown lower FD value than healthy controls in all the EEG channels except the bilateral temporal and occipital regions. The positive symptom sub-group has shown comparatively high FD values than healthy controls as well as overall schizophrenia sample in the bilateral tempero-parietal-occipital region. In addition, the positive symptom sub-group has shown significantly higher regional FD values than negative symptom sub-group especially in right temporal region. The overall schizophrenia samples as well as the positive and negative subgroup have shown least FD values in the bilateral frontal region. The values of DFA exponent-2 have shown significant high value in schizophrenia samples. In addition, the schizophrenia group has shown less DFA exponent-1 in bilateral temporal region than healthy control. The FD, multiscale FD, DFA exponents have shown significant performance in discriminating different physiological states from control states. The FD value of HRV time series during meditation is less compared to pre-meditation state in both Chi and Kundalini meditation. Irrespective of the type of meditation, meditation state has shown significantly high DFA exponent-1 than pre-meditation state, and significantly high DFA exponent-2 in pre-meditation state compared to meditation state. Functional connectivity analysis of brain during meditation In functionally related regions of the brain, even in those regions separated by substantial distances, the EEG fluctuations are synchronous, which is termed as functional connectivity. In this part, a novel application of functional connectivity analysis of brain using graph theoretic approach has been made on the EEG recorded from meditation practitioners. We have used 16 channel EEG data from subjects while performing Raja Yoga meditation. The pre-meditation condition is used as control state, against which meditation state is compared. For finding connectivity between EEG of various channels, we have computed pair-wise linear correlation and mutual information between the EEG channels, to form a connection matrix of size 16x16. Then, various graph parameters, such as average connection density, degree of nodes, characteristic path length, and cluster index, are computed from the connection matrix. The computed parameters are projected on to the scalp to get topographic head maps that give spatial variation of the parameter, and results are compared between meditation and pre-meditation states. The meditation state has shown low average connection density, less characteristic path length, and high average degree in fronto-central and central regions. Furthermore, high cluster index is shown in frontal and central regions than pre-meditation state. The parameters such as complexity, characteristic path length and average connection density are used as features in quadratic discriminant classifier to classify meditation and pre-meditation state, and have given good accuracy performance. Connectivity analysis using mutual information has given high average connection density in meditation state in theta, alpha and beta bands compared to pre-meditation state. The characteristic path length is high in delta, alpha and beta bands in meditation state. In addition, the meditation state has shown high degree and cluster index in theta and beta bands compared to pre-meditation state. Nonlinear dynamical characterization of HRV during meditation The cardiovascular system is influenced by internal dynamics as well as from various external factors, which makes the system more dynamic and nonlinear. In this part of the thesis, a novel application of using HRV data for studying Chi and Kundalini meditation has been made. The HRV time series are embedded into higher dimensional phase-space using Takens’ embedding theorem to reconstruct the attractor. After estimating the minimum embedding dimension to unfold the attractor dynamics, the complexity of the attractor is computed using correlation dimension, Lyapunov exponent, and nonlinearity scores. In all the analyses, the pre-meditation state is used as control state against which meditation state is compared. The statistical significance of the parameters estimated is tested to discriminate meditation state from control state. The HRV time series of both pre-meditation and meditation have shown similar minimum embedding dimensions in both Chi and Kundalini meditation. Irrespective of the type of meditation, the meditation state has shown high correlation dimension, largest Lyapunov exponent, and low nonlinearity score compared to pre-meditation state. Recurrent quantification analysis of HRV during meditation In this part, a novel application of recurrent quantification analysis (RQA) to HRV during meditation is studied. Here, the time series is embedded into a higher dimensional phase-space and Euclidean distance between the embedded vectors is calculated to form a distance matrix. The matrix is converted into binary matrix by applying a suitable threshold, and plotted as image to get recurrence plot. Various parameters are extracted from the recurrence plot such as percent recurrence rate, diagonal parameters (determinism, divergence, entropy, ratio), and vertical or horizontal parameters (laminarity, trapping time, maximal vertical line length). The procedure is applied to HRV data during meditation and pre-meditation (control) to discriminate between the states. The HRV of meditation state has shown more diagonal line structure whereas more black patches are observed in pre-meditation state. In addition, at low embedding dimensions, the meditation state has shown low recurrence rate, high determinism, low divergence, low entropy, high ratio, high laminarity, high trapping time, and less maximal vertical line length compared to pre-meditation state. These RQA parameters have shown superior performance in discriminating meditation state from control state.

Flow is a mental state characterised by a feeling of energised focus, complete involvement and success when fully immersed in an activity. The dimensions of and the conditions required for flow to occur have been explored in a broad spectrum of situational contexts. The close relationship between flow and peak performance sparked an interest in ways to induce flow. However, any process of flow induction requires a measure to trace the degree to which flow is in fact occurring. Self-reports of the flow experience are subjective and provide ad hoc information. Psycho-physiological measures, such as EEG, can provide objective and continuous indications of the degree to which flow is occurring. Unfortunately few studies have explored the relationships between psycho-physiological measures and flow. The present study was an attempt to determine the EEG correlates of flow under conditions of peak performance. Twenty participants were asked to perform a continuous visuomotor task 10 times. Time taken per task was used as an indicator of task performance. EEG recordings were done concurrently. Participants completed an Abbreviated Flow Questionnaire (AFQ) after each task and a Game Flow Inventory (GFI) after having finished all 10 tasks. On completion, performance times and associated flow scores were standardised where after the sample was segmented into a high flow - peak performance and a low flow - low performance level. Multi-variate analysis of variance (MANOVA) was conducted on the performance, flow and EEG data to establish that a significant difference existed between the two levels. In addition, a one-way analysis of variance between high and low flow data was conducted for all variables and main effects were established. Inter-correlations of all EEG data at both levels were then conducted across four brain sites (F3, C3, P3, O1). In high flow only, results indicated increased lobeta power in the sensorimotor cortex together with a unique EEG pattern showing beta band synchronisation between the prefrontal and sensori-motor areas and de-synchronisation between all other areas, while all other frequencies (delta, theta, alpha, lobeta, hibeta, and gamma) remained synchronised across all scalp locations. These findings supported a theoretical neuropsychological model of flow. / Psychology / D. Com. (Consulting Psychology)

L’autisme constitue un trouble neurodéveloppemental dont l’étiologie demeure inconnue. Les données en neuroimagerie des dernières années chez les autistes convergent vers l’observation d’une altération du transfert de l’information corticale entre les différentes régions du cerveau, plutôt qu’une atteinte sélective des structures cérébrales. Quelques modèles théoriques ont été postulés afin d’expliquer ces observations, sans toutefois unifier l’ensemble des observations. Les résultats de la littérature à ce sujet sont souvent hétérogènes et plusieurs disparités méthodologiques existent entre les études. Les conditions d’enregistrement variables et l’hétérogénéité des populations d’étude présentant souvent de multiples comorbidités limitent également leur comparaison. L’objectif de cette thèse était donc d’étudier la connectivité cérébrale de participants adultes avec autisme sans déficience intellectuelle, âgés entre 18 et 35 ans, par rapport à celle des participants neurotypiques, à l’aide d’un outil de mesure offrant une vision complémentaire à la neuroimagerie : la cohérence de l’électroencéphalographie (EEG). La cohérence de l’EEG est une méthode qui fournit de l’information quant à la synchronisation dans le temps entre paires de signaux électriques enregistrés à des sites néocorticaux distincts et constitue essentiellement une mesure de la connectivité fonctionnelle entre régions corticales. Dans cette thèse, nous avons exercé un contrôle rigoureux afin de s’assurer que nos résultats ne soient pas influencés par des variables confondantes et nous avons évalué nos participants durant le sommeil paradoxal (premier article) et lors de deux moments d’activation spontanés pendant lesquels le cortex est activé mais non sollicité, l’éveil calme yeux fermés, en soirée et au matin (deuxième article). Nous avons également évalué la relation entre les indices de cohérence significatifs à l’éveil dans le groupe avec autisme, en relation avec leurs symptômes cliniques aux questionnaires d’évaluation comportementale ADI-R et ADOS-G. Plusieurs des résultats significatifs obtenus dans cette recherche se sont avérés communs aux différents moments d’activation étudiés. En effet, l’observation d’une cohérence EEG supérieure impliquant l’aire visuelle gauche durant les états d’éveil ainsi que durant le SP semblent corroborer une certaine facilitation des régions visuelles chez les autistes par rapport au groupe contrôle. La présence d’une cohérence frontale gauche diminuée chez les participants autistes par rapport aux neurotypiques concorde avec les observations anatomiques et cliniques suggérant un déficit des fonctions cognitives impliquées dans cette région. La cohérence inter-hémisphérique frontale significativement diminuée chez les autistes par rapport aux contrôles à l’éveil du matin supporte pour sa part une altération des fibres calleuses qui pourrait être modulée par les changements développementaux associés à l’âge. Finalement, des corrélations significatives impliquant le nombre de symptômes cliniques et la cohérence EEG chez les autistes pourraient suggérer que des signes d’altération de la connectivité ont un impact sur le comportement diurne et la symptomatologie autistique. L’ensemble des résultats de cette thèse a donc permis d’approfondir les connaissances scientifiques concernant les dynamiques de connectivité cérébrale dans l’autisme et supportent l’hypothèse d’une organisation cérébrale atypique, distincte des neurotypiques, tant à l’éveil qu’au sommeil. / Autism is a neurodevelopmental disorder of unknown etiology. Converging neuroimaging data in the last years suggest that alteration in communication between regions within the autistic brain is governed by the cognitive functions associated with these regions rather than by their sheer physical distance. Some theoretical models were postulated to explain these observations, without unifying all of them. Results of the literature on this matter are often heterogeneous and several methodological disparities exist between the studies, moments and conditions of recording, and the heterogeneousness of the populations often presenting multiple comorbidity limit their interpretation. The objective of this thesis was to compare the brain connectivity of adult participants with autism (18-35 years old) without intellectual deficiency to neurotypical participants, by means of a measurement tool offering a complementary vision to the neuroimaging: the electroencephalography (EEG) coherence. The EEG coherence is a method which evaluates the synchronization in time between pairs of electrod signals recorded at different neocortical sites and constitutes essentially a measure of the functional connectivity between cortical regions. In this thesis, we exercised a rigorous control to make sure that our results are not influenced by staggering variables and we recorded our participants during REM sleep (first paper) and during two spontaneous moments of activation while the cortex is activated but not requested, waking resting state with closed eyes, during evening and morning (second paper). We also estimated the correlation between the significant EEG coherence results observed during waking state in the autism group, with their clinical symptoms on the behavioural questionnaires ADI-R and ADOS-G. Several of the significant results obtained in this research were common to all studied moments of brain activation. Indeed, the observation of a superior EEG coherence involving the left visual area during the waking states as well as during the REM sleep confirms a certain facilitation of the visual regions in the autistic group compared to the control group. The presence of a left frontal coherence decreased in the participants with autism compared to the neurotypicals supports anatomical and clinical observations suggesting a deficit of the cognitive functions involved in this region. The significantly decreased frontal inter-hemispheric coherence in the autistic group compared to the controls in the morning waking recording supports an alteration in the callosal fibers which could be modulated by developmental changes associated with age. Finally, significant correlations involving the number of clinical symptoms and the EEG coherence of autistic participants could suggest that alteration of connectivity has an impact on the diurnal behavior and the symptomatology. Thus the results of this thesis add to the scientific knowledge concerning the dynamics of cerebral connectivity in autism and support the hypothesis of an atypical brain organization, distinct from neurotypicals, both in the waking as in the sleep states.

La sclérose tubéreuse de Bourneville (STB) est une maladie génétique et multi-systémique à transmission autosomique dominante due à des mutations d’un gène TSC1 ou TSC2 qui codent respectivement pour hamartine et tuberine ayant une action inhibitrice sur la voie de signalisation mTOR. L’épilepsie précoce et pharmacorésistante est la manifestation neurologique la plus fréquente et la plus délétère de la STB. Elle débute souvent dans la première année de vie par des spasmes infantiles qui évoluent avec l’âge et en absence de traitement vers des crises toniques ou tonico-cloniques. Bien que les crises soient supposées être générées dans des tubers corticaux, les mécanismes de l’épilepsie ne sont pas bien élucidés et le traitement reste souvent inefficace. Des études morphologiques ont montré une altération de l’expression ARNm des récepteurs au glutamate dans les cellules géantes et les neurones dysplasiques des tubers, mais leur implication fonctionnelle restait à montrer. Les différentes sous-unités NMDA ont une expression âge-dépendante et région-spécifique, les plus grands changements survenant au début de la vie quand l’épilepsie de la STB apparaît. Ce travail avait pour but d’étudier à l’aide de méthodes électrophysiologiques in vitro et in vivo l’expression fonctionnelle des sous-unités NMDA aberrantes et de déterminer leur rôle dans l’épileptogènese chez les souris hétérozygotes Tsc1+/- et sur le tissu humain STB post-opératoire. Nous avons pu démontrer que : i) Les souris hétérozygotes pour le gène Tsc1 sont spontanément épileptiques in vivo et in vitro dans une courte fenêtre dévelopmentale de P9 à P18. ii) Elles présentent une altération d’expression des récepteurs NMDA couche-spécifique et mTOR dépendante avec une surexpression des sous-unités GluN2C/D dans la couche 4 et 2/3 et GluN2B dans les couches 2/3. Cette expression anormale est prévenue par l’administration d’un inhibiteur de la voie mTOR, la rapamycine. iii) Les mêmes altérations d’expression des récepteurs NMDA, sont montrées sur les tissus post-opératoires, non seulement de tubers de STB mais aussi des dysplasies corticales focales (DCF), ces deux malformations ayant des similarités étiologiques et physiopathologiques. iv) La RT-PCR quantitative confirme une expression excessive de GluN2C dans le cortex de souris Tsc1+/- et sur le tissu humain des tubers et DCF. v) Les décharges épileptiques chez la souris Tsc1+/- sont générées dans la couche granulaire 4 du cortex avant de se propager vers les couches superficielles et les couches profondes, empruntant ainsi les microcircuits corticaux. vi) L’expression excessive de la sous-unité GluN2C dans le cortex contribue à l’hyperexcitabilité neuronale chez la souris Tsc1+/- et sur des tissus humains de tubers et de DCF puisque les crises et les décharges sont bloquées par les antagonistes sélectifs de GluN2C/D. vii) Les crises chez la souris Tsc1+/- suivent une séquence âge-dépendante évoluant du type «spasms-like» vers «tonic-clonic like», rappelant celle de l’épilepsie humaine, avec deux pics de haute incidence de crises à P13 et P16 correspondant chez l’homme respectivement l’âge des spasmes infantiles et celui des crises toniques. L’évolution avec l’âge du délai de propagation inter-hémisphérique pourrait contribuer à ce changement de types de crises. Ces résultats montrent donc pour la première fois qu’une happloinsuffisance pour le gène Tsc1 chez les souris Tsc1+/- sans tubers suffit à produire une altération de l’expression des récepteurs NMDA de manière mTOR dépendante et contribuer ainsi à l’épileptogènese dans la STB. La souris Tsc1+/- est le premier modèle génétique sans anomalies morphologiques présentant une épilepsie spontanée qui évolue des spasmes vers des crises toniques et tonico-cloniques. Néanmoins cette épilepsie diffère de l’épilepsie humaine de la STB par l’absence de crises focales et de pharmacorésistance, ce qui pourrait être expliqué par l’absence de tubers chez la souris Tsc1+/-. (...) / Tuberous sclerosis complex (TSC) is a genetic multisystemic disease with autosomal dominant transmission due to mutations in a gene TSC1 or TSC2 respectively which encode hamartin and tuberin proteins having an inhibitory action on the mTOR signaling pathway. Early refractory epilepsy is the most common and most deleterious neurological manifestation. The epilepsy often begins in the first year of life by infantile spasms that change in the lack of treatment to tonic or tonic-clonic seizures in age-dependent manner. Although seizures are thought to be generated in cortical tubers, epilepsy mechanisms are not well understood and treatment is often ineffective. Morphological studies showed the altered expression of glutamate receptor mRNA in the giant cells and dysplastic neurons of tubers, but their functional involvement remains unknown. The different NMDA subunits have an age-dependent and region-specific expression, the greatest changes occurring early in life when the TSC epilepsy appears. This work aimed to study the functional expression of aberrant NMDA subunits expression and their role in the epileptogenesis in heterozygous Tsc1+/- mice and post-surgical human tissue of TSC patients using in vitro and in vivo electrophysiological methods. The study reveal that: i) Heterozygous tuber-free Tsc1+/- mice show spontaneous epilepsy in vivo and in vitro in a short developmental window from P9 to P18. ii) These mice exhibit an altered NMDA receptor expression in mTOR dependent and layer-specific manner with GluN2C/D subunits overexpression in layers 4 and 2/3, and GluN2B ovexpression in layers 2/3. This abnormal NMDA receptors expression is prevented by the administration of an mTOR inhibitor, rapamycin. iii) The same alterations of NMDA receptors’ expression are shown in post-surgical tissues not only in tubers from TSC patients, but also in focal cortical dysplasia (FCD), these two malformations sharing etiological and pathophysiological similarities. iv) Quantitative RT-PCR confirms the excessive GluN2C subunit expression in Tsc1+/- mouse cortex and human tissue of tubers and DCF. v) Epileptic discharges in Tsc1+/- mice are generated in the granular layer 4 of the cortex before spreading to the superficial and then to deep layers, thus borrowing the cortical microcircuits. vi) Excessive expression of GluN2C subunit in the cortex contributes to neuronal hyperexcitability in Tsc1+/- mice, as well as in human tubers and DCF tissues, since epileptic discharges are blocked by selective GluN2C/D antagonists. vii) Seizures in Tsc1+/- mice follow the age-dependent sequence, evolving from "spasms-like" to "tonic-clonic like" thus reminding the human epilepsy, with two peaks of highest seizure incidence at P13 and P16 corresponding respectively to age of infantile spasms and of tonic seizures in human. The age-dependent evolution of interhemispheric propagation delay could contribute to this change in seizure type. These results show for the first time that TSC1 happloinsuffisancy in tuber-free Tsc1+/- mice is sufficient to produce an alteration in NMDA receptor expression in an mTOR dependent manner, and thus contributes to epileptogenesis in TSC. The Tsc1+/- mouse line is the first genetic model of TSC without morphological abnormalities presenting with early spontaneous seizures which evolves from “spasms-like” to “tonic-clonic like” seizures. However, the epilepsy in Tsc1+/- mice differs from human TSC epilepsy by the absence of focal seizures and of drug-resistance. Both could be explained by the lack of tubers in the Tsc1+/- mice. It remains to determine whether the expression of GluN2C subunit is also transitional in Tsc1+/- mice and whether other factors contribute to determine the age-dependent epilepsy. This study opens new therapeutic perspectives of TSC epilepsy targeting GluN2C subunit of NMDA receptors.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

Le sommeil est essentiel au bon fonctionnement de l’organisme. Ce dernier est régulé, entre autres, par le processus de régulation homéostatique qui dépend de la pression de sommeil accumulée suite à l’éveil. Des études ont suggéré que ce processus pourrait être lié à la plasticité synaptique, et que le changement de la pression de sommeil affecterait le degré de plasticité du cerveau. Les récepteurs N-méthyl-D-aspartate, des médiateurs importants de plasticité, semblent impliqués dans les conséquences délétères du manque de sommeil ainsi que dans la régulation de la synchronisation corticale caractéristique du sommeil lent profond. Leur activité est contrôlée par Neuroligine 1 (NLGN1), une molécule d’adhésion synaptique. Une mutation de Nlgn1 a des effets similaires à ceux de la privation de sommeil sur la mémoire et le comportement. Dans le manuscrit de mon mémoire, nous présentons l’hypothèse d’une implication de NLGN1 dans la régulation du sommeil et de l’éveil. Pour tester cette hypothèse, l’expression d’ARNm et de protéine NLGN1 a été mesurée suite à une privation de sommeil et le sommeil de souris n’exprimant pas NLGN1 a été caractérisé. Les résultats de mon projet de maîtrise montrent, en premier lieu, qu’une augmentation de la pression pour dormir altère l’expression de l’ARNm et de la protéine NLGN1 chez la souris. De plus, nos observations révèlent qu’une mutation de Nlgn1 diminue la quantité d’éveil et modifie l’activité spectrale en éveil et en sommeil. Ces observations dévoilent l’importance de NLGN1 dans le maintien de l’éveil et la régulation du sommeil, et supportent un rôle de NLGN1 dans la régulation de l’activité neuronale. / Sleep is essential for the well-functioning of the body. It has been suggested that sleep is regulated, in part, by the homeostatic process of sleep regulation which controls a pressure for sleep in function of the amount of time spent awake. Studies have suggested that the homeostatic process could be linked to synaptic plasticity, and that changes in sleep pressure can affect brain plasticity. N-methyl-D-aspartate receptors, which are important plasticity mediators, appear to be implicated in the deleterious effects related to sleep loss as well as in the regulation of cortical synchrony characteristic of slow wave sleep. Their activity is controlled by Neuroligin 1 (NLGN1), a synaptic adhesion molecule. Also, a Nlgn1 mutation has similar effects on memory and behavior as those observed following a sleep deprivation. In this master’s thesis, we hypothesized that NLGN1 is implicated in sleep and wake regulation. To test this hypothesis, Nlgn1 mRNA and protein expression has been measured after sleep deprivation, and the sleep of mice lacking NLGN1 has been studied. The results of my research project show that an increase in sleep pressure changes Nlgn1 mRNA and protein expression in mice. We also find that Nlgn1 mutation reduces wake duration and modifies the EEG spectral composition during wake and sleep. These results indicate that NLGN1 is important in the maintenance of wakefulness and the regulation of sleep, and provide further support to a role for NLGN1 in the regulation of neuronal activity.