Six new structural parameters £rB, £pB, £rC, £pC, £rS, and £pS are proposed to enhance the side chain actions in protein structures. Programs for calculating these new parameters based on phi and psi torsion angles vector algebra calculation method are established. A bivariate model with von Mises marginal distributions are applied to establish models of phi and psi in protein class Ophiophagus hannah neurotoxins and alpha-bungarotoxins respectively. 11 global structural parameters include phi and psi torsion angles, bond lengths of C-N, C-O, C£\ -C, and N-C£\, and bond angles of C-N-C£\, C£\-C-N, C£\-C-O, N-C£\-C, and O-C-N are considered to classify long alpha-neurotoxins by Ward's cluster method and LIBSVM program package. Those global structural parameters of loop II Trp residues of alpha-neurotoxins are discussed.

Cluster analysis is a popular method of multivariate statistics. Based on mutual similarities between objects this method is able to classify and divide objects into several groups or clusters. The results of the clustering can be different by using different methods, measures of distance and procedures. The main aim of this thesis is to compare the results of several methods of cluster analysis with the known classification of classes from the original data file. In total, there are 15 data files, which were analyzed and each of them contained known information about the right allocation of objects in groups. The success of clustering of each method was calculated by comparing the known classification of classes and resulted clusters. In addition to the comparison of individual methods of cluster analysis was compared the impact of standardization and correlation to the success of each method. To reflect the distance betweeen the objects within each clusters, squared Euclidean distance was used. The results of this thesis point out that better success of clustering were achieved in the case of correlated variables in data file. The succes of clustering was higher about 2 percent points than in the case when correlated variables were deleted from data set. The methods divided 69,8 % objects before standardization and 70,8 % objects after standardization. The results also show a large importance of standardization in the case of Ward´s method. After standardization this method rank the most objects into correct classification classes and were more succesful, about nine percent points. In the case of correlated variables is the succes of the method 76,4 %. Standardization positively influences also centroid method and the method of farthest neighbour. Median method, nearest neighbour method and the method of average linkage achieve higher success of clustering in the case of original, nonstandardized variables (uneven variables).

L'imagerie par résonance magnétique fonctionnelle (IRMf) étant une technique non invasive pour l'étude de cerveau, elle a été employée pour comprendre les mécanismes cérébraux sous-jacents à la prise alimentaire. Cependant, l'utilisation de stimuli liquides pour simuler la prise alimentaire engendre des difficultés supplémentaires par rapport aux stimulations visuellement habituellement mises en œuvre en IRMf. L'objectif de cette thèse a donc été de proposer une méthode robuste d'analyse des données tenant compte de la spécificité d'une stimulation alimentaire. Pour prendre en compte le mouvement dû à la déglutition, nous proposons une méthode de censure fondée uniquement sur le signal mesuré. Nous avons de plus perfectionné l'étape de normalisation des données afin de réduire la perte de signal. La principale contribution de cette thèse est d'implémenter l'algorithme de Ward de sorte que parcelliser l'ensemble du cerveau soit réalisable en quelques heures et sans avoir à réduire les données au préalable. Comme le calcul de la distance euclidienne entre toutes les paires de signaux des voxels représente une part importante de l'algorithme de Ward, nous proposons un algorithme cache-aware du calcul de la distance ainsi que trois parallélisations sur les architectures suivantes : architecture à mémoire partagée, architecture à mémoire distribuée et GPU NVIDIA. Une fois l'algorithme de Ward exécuté, il est possible d'explorer toutes les échelles de parcellisation. Nous considérons plusieurs critères pour évaluer la qualité de la parcellisation à une échelle donnée. À une échelle donnée, nous proposons soit de calculer des cartes de connectivités entre les parcelles, soit d'identifier les parcelles répondant à la stimulation à l'aide du coefficient de corrélation de Pearson. / As a non-invasive technology for studying brain imaging, functional magnetic resonance imaging (fMRI) has been employed to understand the brain underlying mechanisms of food intake. Using liquid stimuli to fake food intake adds difficulties which are not present in fMRI studies with visual stimuli. This PhD thesis aims to propose a robust method to analyse food stimulated fMRI data. To correct the data from swallowing movements, we have proposed to censure the data uniquely from the measured signal. We have also improved the normalization step of data between subjects to reduce signal loss.The main contribution of this thesis is the implementation of Ward's algorithm without data reduction. Thus, clustering the whole brain in several hours is now feasible. Because Euclidean distance computation is the main part of Ward algorithm, we have developed a cache-aware algorithm to compute the distance between each pair of voxels. Then, we have parallelized this algorithm for three architectures: shared-memory architecture, distributed memory architecture and NVIDIA GPGPU. Once Ward's algorithm has been applied, it is possible to explore multi-scale clustering of data. Several criteria are considered in order to evaluate the quality of clusters. For a given number of clusters, we have proposed to compute connectivity maps between clusters or to compute Pearson correlation coefficient to identify brain regions activated by the stimulation.

L’imagerie de l’activité neuronale (AN) permet d’étudier le fonctionnement normal et pathologique du cerveau humain, en plus d’aider au diagnostic et à la planification d’interventions neurochirurgicales. L’électroencéphalographie (EEG) et l’imagerie par résonance magnétique fonctionnelle (IRMf) comptent parmi les modalités d’imagerie fonctionnelle les plus utilisées en recherche et en clinique. Plusieurs éléments de la structure cérébrale peuvent toutefois influencer les signaux mesurés, de sorte qu’ils ne reflètent pas uniquement l’AN. Il importe donc d’en tenir compte pour bien interpréter les résultats, surtout lorsqu’on compare des sujets à l’anatomie cérébrale très différente. En outre, la maturation, le vieillissement et certaines pathologies s’accompagnent de changements structurels du cerveau. Ceci complique l’analyse de données longitudinales et la comparaison d’un groupe cible avec un groupe contrôle. Or, notre compréhension des interactions structure-signal demeure incomplète et très peu d’études en tiennent compte. Mon projet de doctorat a consisté à étudier les impacts de la structure cérébrale sur les signaux d’EEG et d’IRMf ainsi qu’à explorer des pistes de solution pour s’en affranchir. J’ai d’abord étudié l’effet de l’amincissement cortical dû au vieillissement sur la désynchronisation liée à l’événement (« event-related desynchronization » - ERD) en EEG. Les résultats ont mis en lumière une relation linéaire négative entre l’ERD et l’épaisseur corticale, ce qui a permis de corriger les signaux par régression. J’ai ensuite étudié l’impact de la présence de veines sur la réponse BOLD (blood-oxygen-level dependent) mesurée en IRMf suite à une stimulation visuelle. Ces travaux ont démontré que la densité veineuse locale, qui varie fortement d’une région et d’un sujet à l’autre, corrèle positivement avec l’amplitude et le délai de la réponse BOLD. Finalement, j’ai adapté une technique de classification de données visant à améliorer la détection des régions du cortex activées en IRMf. Cette méthode permet d’éviter plusieurs problèmes de l’analyse classique en IRMf, de réduire l’impact de la structure cérébrale sur les résultats obtenus et d’établir des cartes d’activité cérébrale contenant plus d’information. Globalement, ces travaux contribuent à l’amélioration de notre compréhension des interactions structure-signal en EEG et en IRMf, ainsi qu’au développement de méthodes d’analyse réduisant leur impact sur l’interprétation des données en termes d’AN. / Abstract : Imaging neural activity allows studying normal and pathological function of the human brain, while also being a useful tool for diagnosis and neurosurgery planning. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are some of the most commonly used functional imaging modalities, both in research and clinic. Many aspects of cerebral structure can however influence the measured signals, so that they do not only reflect neural activity. Taking them into account is therefore of import to correctly interpret results, especially when comparing subjects displaying large differences in brain anatomy. In addition, maturation, aging as well as some pathologies are associated with changes in brain structure. This acts as a confounding factor when analysing longitudinal data or comparing target and control groups. Yet, our understanding of structure-signal relationships remains incomplete and very few studies take them into account. My Ph.D. project consisted in studying the impacts of cerebral structure on EEG and fMRI signals as well as exploring potential solutions to mitigate them. In that regard, I first studied the effect of age-related cortical thinning on event-related desynchronization (ERD) in EEG. Results allowed identifying a negative linear relationship between ERD and cortical thickness, enabling signal correction using regression. I then investigated how the presence of veins in a region impacts the blood-oxygen-level dependent (BOLD) response measured in fMRI following visual stimulation. This work showed that local venous density, which strongly varies across regions and subjects, correlates positively with the BOLD response amplitude and delay. Finally, I adapted a data clustering technique to improve the detection of activated cortical regions in fMRI. This method allows eschewing many problematic assumptions used in classical fMRI analyses, reducing the impacts of cerebral structure on results and establishing richer brain activity maps. Globally, this work contributes to further our understanding of structure-signal interactions in EEG and fMRI as well as to develop analysis methods that reduce their impact on data interpretation in terms of neural activity.

Nowadays quality of life is often discussed topic. In defining this term, there is considerable ambiguity and disunity, since there is no universally accepted definition, nor theoretically sophisticated model. However, despite this fact, the level of quality of life is currently one of the most discussed topic. Monitoring the quality of life by using a variety of indicators are engaged in several international organizations, one of them is the Development Programme of the United Nations. This organization annually publishes the Human Development Index, which divides the world´s countries into four groups according to their level of development: low, medium, high and very high development. The aim of this thesis is to analyze the quality of life in 125 countries by using cluster analysis, accurately the Ward's method. Quality of life in this thesis is evaluated based on 19 demographic and economic indicators, which include life expectancy, literacy rate, access to drinking water and infant mortality rate. The cluster analysis divided the country into individual clusters by their similarities. Six clusters were created by this analysis, which had been compared with the results of Human Development Index. The clusters very well reflect the division, which is commonly used in the characterization of developing and developed countries. Each of the six clusters can be very well described and characterized in terms of quality of life. It is also possible qualify those clusters as poorest developing, low developed, moderately developed, medium development, high and very high development countries. Based on the results it can be stated that this analysis is consistent with other indicators of quality of life and the resulting clusters are identical with the division of countries which is commonly used.