Multiple theoretical frameworks posit that interactions between the autonomic nervous system and higher-order neural networks are crucial for cognitive regulation. However, few studies have directly examined whether autonomic physiology influences brain activity during cognitive tasks, and even fewer of those studies have examined both autonomic branches when doing so. Measures of selective attention derived from event-related brain potentials (ERPs) are particularly well-suited for addressing this question, given that ERP selective attention tasks are designed to control for the influences of psychomotor processes and arousal and are predictive of higher-order cognitive function in children and adults. Such research is particularly promising for understanding how early adversity impacts neurocognitive development in children, given that stress experienced early in life impacts both autonomic function and selective attention. Here, a broad literature review is presented, integrating findings across studies of autonomic physiology, cognition, and brain activity in children and adults (Chapter 1). Then, two experiments are described where cardiac measures of parasympathetic and sympathetic activity were recorded concurrently with ERPs during an auditory selective task in a sample of adults (Chapter 2) and in a sample of preschool-aged children (Chapter 3). Results from both experiments demonstrate a key role for the sympathetic nervous system in selective attention for adults and children, such that greater sympathetic activity is associated with larger effects of selective attention on ERPs. These findings are then reviewed with suggestions for how existing models of neurovisceral integration might be updated to better emphasize the role of sympathetic nervous system activity in neurocognitive processes, emphasizing measures of threat-related and reward-related arousal, as represented by galvanic skin response and pre-ejection period, respectively (Chapter 4). Future directions are also discussed, including recommendations for future studies of neurovisceral integration to examine associations between physiology, behavior, and brain activity at the single-trial level, to incorporate participants from more diverse backgrounds of life experience, and to examine the plasticity of autonomic mechanisms implicated in neurocognitive function.

Alzheimer’s disease (AD) is a neurodegenerative disorder in which the death of brain cells causes memory loss and cognitive decline. As AD progresses, changes in the electrophysiological brain activity take place. Such changes can be recorded by the electroencephalography (EEG) and magnetoencephalography (MEG) techniques. These are the only two neurophysiologic approaches able to directly measure the activity of the brain cortex. Since EEGs and MEGs are considered as the outputs of a nonlinear system (i.e., brain), there has been an interest in nonlinear methods for the analysis of EEGs and MEGs. One of the most powerful nonlinear metrics used to assess the dynamical characteristics of signals is that of entropy. The aim of this thesis is to develop entropy-based approaches for characterization of EEGs and MEGs paying close attention to AD. Recent developments in the field of entropy for the characterization of physiological signals have tried: 1) to improve the stability and reliability of entropy-based results for short and long signals; and 2) to extend the univariate entropy methods to their multivariate cases to be able to reveal the patterns across channels. To enhance the stability of entropy-based values for short univariate signals, refined composite multiscale fuzzy entropy (MFE - RCMFE) is developed. To decrease the running time and increase the stability of the existing multivariate MFE (mvMFE) while keeping its benefits, the refined composite mvMFE (RCmvMFE) with a new fuzzy membership function is developed here as well. In spite of the interesting results obtained by these improvements, fuzzy entropy (FuzEn), RCMFE, and RCmvMFE may still lead to unreliable results for short signals and are not fast enough for real-time applications. To address these shortcomings, dispersion entropy (DispEn) and frequency-based DispEn (FDispEn), which are based on our introduced dispersion patterns and the Shannon’s definition of entropy, are developed. The computational cost of DispEn and FDispEn is O(N) – where N is the signal length –, compared with the O(N2) for popular sample entropy (SampEn) and FuzEn. DispEn and FDispEn also overcome the problem of equal values for embedded vectors and discarding some information with regard to the signal amplitudes encountered in permutation entropy (PerEn). Moreover, unlike PerEn, DispEn and FDispEn are relatively insensitive to noise. As extensions of our developed DispEn, multiscale DispEn (MDE) and multivariate MDE (mvMDE) are introduced to quantify the complexity of univariate and multivariate signals, respectively. MDE and mvMDE have the following advantages over the existing univariate and multivariate multiscale methods: 1) they are noticeably faster; 2) MDE and mvMDE result in smaller coefficient of variations for synthetic and real signals showing more stable profiles; 3) they better distinguish various states of biomedical signals; 4) MDE and mvMDE do not result in undefined values for short time series; and 5) mvMDE, compared with multivariate multiscale SampEn (mvMSE) and mvMFE, needs to store a considerably smaller number of elements. In this Thesis, two restating-state electrophysiological datasets related to AD are analyzed: 1) 148-channel MEGs recorded from 62 subjects (36 AD patients vs. 26 age-matched controls); and 2) 16-channel EEGs recorded from 22 subjects (11 AD patients vs. 11 age-matched controls). The results obtained by MDE and mvMDE suggest that the controls’ signals are more and less complex at respectively short (scales between 1 to 4) and longer (scales between 5 to 12) scale factors than AD patients’ recordings for both the EEG and MEG datasets. The p-values based on Mann-Whitney U-test for AD patients vs. controls show that the MDE and mvMDE, compared with the existing complexity techniques, significantly discriminate the controls from subjects with AD at a larger number of scale factors for both the EEG and MEG datasets. Moreover, the smallest p-values are achieved by MDE (e.g., 0.0010 and 0.0181 for respectively MDE and MFE using EEG dataset) and mvMDE (e.g., 0.0086 and 0.2372 for respectively mvMDE and mvMFE using EEG dataset) for both the EEG and MEG datasets, illustrating the superiority of these developed entropy-based techniques over the state-of-the-art univariate and multivariate entropy approaches. Overall, the introduced FDispEn, DispEn, MDE, and mvMDE methods are expected to be useful for the analysis of physiological signals due to their ability to distinguish different types of time series with a low computation time.

Submitted by Rafael Toledo Fernandes de Souza (rafael@ibb.unesp.br) on 2016-04-27T21:08:41Z No. of bitstreams: 1 TeseRafaelTFS.pdf: 24959368 bytes, checksum: f8cc766a544e297b446817f14d467e4e (MD5) / Approved for entry into archive by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br) on 2016-04-29T22:14:03Z (GMT) No. of bitstreams: 1 souza_rtf_dr_bot.pdf: 24959368 bytes, checksum: f8cc766a544e297b446817f14d467e4e (MD5) / Made available in DSpace on 2016-04-29T22:14:03Z (GMT). No. of bitstreams: 1 souza_rtf_dr_bot.pdf: 24959368 bytes, checksum: f8cc766a544e297b446817f14d467e4e (MD5) Previous issue date: 2016-03-10 / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / O sono (do latim, somnus) é um período que apresenta uma atividade fisiológica característica, que pode ser registrada com o EEG. Algumas ondas em um sinal de EEG são vistas apenas durante o sono, como os fusos do sono e complexos K. O fuso é um dos elementos mais bem conhecidos no estudo do sono. No presente estudo serão estudados fusos globais e potenciais complexos K, os quais são observados simultaneamente em todos os canais de EEG. Para isto, um novo método de investigação foi proposto, que estuda tanto o envelope do sinal quanto a fase/frequência de cada fuso. Através da análise da fase do fuso global, foi mostrado que 90% dos fusos de indivíduos saudáveis sincronizam com um tempo de latência de 0,11s. O método também avalia a frequência de modulação (chirp) de fusos globais, e foi averiguado que não há correlação entre o chirp destes fusos e sua sincronização. Através do estudo do envelope do sinal juntamente com a implementação de um modelo de propagação isotrópico, foi possível estimar a origem do fuso e sua velocidade de propagação. Os resultados obtidos indicam que através desta abordagem simples e não invasiva é possível determinar, com uma precisão razoável, o local de origem dos fusos do sono, e sua velocidade estimada de propagação de 0,12m/s. Os potenciais complexos K detectados foram usados para avaliar a robustez do método desenvolvido, e apresentaram frequências, durações e amplitudes dentro das faixas esperadas para complexos K. A velocidade do propagação encontrada para os potenciais complexos K foi de 0,05m/s, menor do que a dos fusos do sono. Os potenciais complexos K mostraram ter uma tendência de sincronização parcial, propriedade esta descrita para os complexos K na literatura. O método desenvolvido também foi aplicado em indivíduos com Apneia Obstrutiva do Sono (AOS). A maioria dos parâmetros analisados não apresentaram diferenças significativas entre indivíduos saudáveis e com AOS; exceto que, em indivíduos com AOS, a duração da sincronização apresentou um valor 34,18% menor, e a posição de origem dos fusos apresentou dois focos diferentes. Desta maneira, conclui-se que o método desenvolvido foi aplicado com sucesso nos grafoelementos avaliados, pois consegue recuperar as informações esperadas, e pode ser útil como uma ferramenta diagnóstica não invasiva. / Sleep (derived from the Latim, somnus) is a brain state with distinct physiological activity that can be investigated by EEG evaluation. Some waves are unique in sleep EEG such as sleep spindles and K complexes. Spindles are one of the best known elements in sleep studies. In this work we considered global spindles and K complexes, which are spindles that are observed simultaneously in all EEG channels. We propose a method that investigates both the signal envelope and phase/frequency of each global spindle. By analysing the spindle phase we showed that 90% of spindles in healthy subjects synchronize with a median latency time of 0.11 s. The method also measured the frequency slope (chirp) of global spindles and found that global spindle chirp and synchronization are not correlated. By investigating the signal envelopes and implementing a homogeneous and isotropic propagation model, we could estimate both the signal origin and velocity in global spindles. Our results indicate that this simple and non-invasive approach could determine with reasonable precision the spindle origin, and allowed us to estimate a signal speed of 0.12 m/s. Potential K complexes are used to assess the robustness of developed method and shows that frequencies, durations and amplitudes within the K complex expected range. Propagation velocity in potential K complexes are around 0.05 m/s which is lower than spindles velocity. Partial synchronization tendencies were detected in potential K complex, a propriety described for K complex in the literature. Obstructive Sleep Apnea (OSA) subjects were also assessed by our method. The majority of analysed parameters do not present significant difference between healthy and OSA subjects except by synchronization duration (34.18% lower in OSA) and two distinct focal points in OSA spindle origin. The proposed methodology retrieved the expected results, obtained by EEG analysis and other more complex techniques and our results indicate that it can be used as a diagnosis tool and to explore other sleep phenomena, such as K complexes. / FAPESP: 2012/22413-2

Orientadores: Fernando Cendes, Li Li Min / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciencias Medicas / Made available in DSpace on 2018-08-10T05:20:09Z (GMT). No. of bitstreams: 1 Betting_LuizEduardoGomesGarcia_D.pdf: 9629246 bytes, checksum: a5717d51675609b6835960db129c8d3b (MD5) Previous issue date: 2006 / Resumo: Epilepsias generalizadas idiopáticas (EGI) constituem de 20-40% das epilepsias e de forma oposta às epilepsias parciais, anormalidades estruturais não são esperadas. De acordo com a idade de início e o tipo principal de crise, as EGI são divididas principalmente em epilepsia ausência infantil e juvenil (EA), epilepsia mioclônica juvenil (EMJ) e epilepsia com crises tônico-clônicas generalizadas (CTCG). Os limites entre estas subsíndromes são imprecisos e a classificação muitas vezes é difícil. Devido às características semelhantes, alguns autores consideram a EGI como uma única patologia com múltiplos fenótipos (continuum biológico). O eletroencefalograma (EEG) auxilia no diagnóstico das EGI especialmente quando evidencia descargas do tipo espícula onda-lenta generalizadas com atividade de base normal. Entretanto, o EEG pode ser normal e até mesmo mostrar focalidades dificultando o diagnóstico. A ressonância magnética (RM) não é realizada de forma rotineira em pacientes com EGI. Contudo, novas técnicas de aquisição e processamento de imagens vêm detectando anormalidades sutis nestes indivíduos. O objetivo deste estudo foi investigar a fisiopatologia das EGI através da análise de características clínicas, eletroencefalográficas e de neuroimagem. Inicialmente, as características dos EEGs de 180 pacientes com diagnóstico clínico de EGI foram avaliadas. 493 exames foram analisados. Em 33% dos pacientes o EEG inicial foi característico e em 22% o exame evidenciou focalidades. Após a identificação de focalidades utilizamos a neuroimagem convencional (análise visual) na avaliação de 134 pacientes com EGI. Observamos anormalidades na RM de 27 (20%) pacientes. A maioria das anormalidades não apresentou relação direta com as crises. Utilizamos a técnica da morfometria baseada em voxel (MBV) para investigar lesões discretas eventualmente não identificadas na neuroimagem de rotina. Esta técnica permite a comparação entre grupos de imagens aumentando a chance de detecção de anormalidades. Observamos aumento na concentração de substância cinzenta (CSC) localizada no córtex frontal de pacientes com EMJ (n=44) e EA (n=24). Observamos também uma maior CSC na região anterior do tálamo nos pacientes com crises de ausência (n=47). Avaliando as focalidades clínicas e de EEG de 22 pacientes com EGI utilizando a MBV, observamos áreas de aumento da CSC em 8 dos 9 (89%) pacientes com EMJ, 5 dos 6 (83%) pacientes com EA e 5 dos 7 (71%) pacientes com CTCG ao despertar. A volumetria do tálamo foi realizada para investigar o aumento de CSC sugerido pela MBV. A comparação entre 147 pacientes e um grupo controle evidenciou um maior volume da região anterior do tálamo nos pacientes com crises de ausência. Nossos resultados revelam que a fisiopatologia das EGI envolve o tálamo e o córtex cerebral. As diversas alterações na neuroimagem quantitativa apresentadas por cada subsíndrome sugerem um diferente mecanismo para as EGI. Este achado fortalece o conceito de diferentes doenças com fenótipos semelhantes. Mais do que isso, nossos achados indicam, uma alteração estrutural no cérebro destes indivíduos. Os diversos fenótipos estão relacionados a diferentes mecanismos fisiopatológicos. As focalidades observadas no EEG e na RM refletem a patogênese das crises em pacientes com EGI / Abstract: Idiopathic generalized epilepsies (IGE) represent 20-40% of all epilepsies and opposed to partial epilepsies, structural abnormalities are not expected. According to the age of onset and the main seizure type, IGE are divided mainly in childhood and juvenile absence epilepsy (AE), juvenile myoclonic epilepsy (JME) and generalized tonic-clonic seizures (GTCS). The limits between these subsyndromes are unclear and sometimes classification is difficult. Because of the similar characteristics, some authors consider IGE as a single pathology with multiple phenotypes (biological continuum). Electroencephalogram (EEG) helps the IGE diagnosis specially when it shows the generalized spike and wave discharges with normal background. However, the EEG may be normal or even disclose focalities difficulting the diagnosis. Magnetic resonance imaging (MRI) is not routinely performed in patients with IGE. In spite of this, new techniques of acquisition and processing of the images are detecting subtle abnormalities in these individuals. The objective of this study was to investigate the pathophysiology of the IGE using the clinical, EEG and neuroimaging features. Initially, the characteristics of the EEGs of 180 patients with clinical diagnosis of IGE were evaluated. 493 exams were analyzed. In 33% of the patients the initial EEG was characteristic and in 22% the exam revealed focalities. After the identification of the focalities, we used conventional neuroimaging (visual analysis) on the evaluation of 134 patients with IGE. We observed abnormalities in the MRI of 27 (20%) patients. Most of the abnormalities were not directly related to the seizures. We used the voxel base morphometry (VBM) technique to evaluate the images. This technique allows comparisons between groups of images increasing the chances of detecting abnormalities. We observed increased gray matter concentration (GMC) localized in the frontal cortex of patients with JME (n=44) and AE (n=24). We also observed increased GMC in the anterior thalamic region of patients with absence seizures (n=47). Evaluating the clinical and EEG focalities of 22 patients with IGE using VBM, we observed areas of increased GMC in 8 of 9 (89%) patients with JME, 5 of 6 (83%) patients with AE and 5 of 7 (71%) patients with GTCS on awakening. The volumetry of the thalamus was performed to investigate the increased GMC suggested by the VBM. The comparison between 147 patients with a control group showed increased volume of the anterior thalamic region in patients with absence seizures. Our results revealed that the pathophysiology of the IGE involves the thalamus and the cerebral cortex. The several abnormalities on the neuroimage presented by each subsyndrome suggest a different mechanism for the IGE. This finding strengths the concept of multiple diseases with similar phenotypes. Furthermore, our findings indicate a structural abnormality in the brain of these individuals. The several phenotypes are related with different pathophysiological mechanisms. The focalities present on the EEG and in the MRI reflect the pathogenesis of the seizures in patients with IGE / Doutorado / Neurociencias / Doutor em Fisiopatologia Medica

Orientador: Prof. Dr. João Ricardo Sato / Coorientador: Prof. Dr. André Mascioli Cravo / Tese (doutorado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, São Bernardo do Campo, 2018.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / Guanylin and uroguanylin are heat-stable peptides isolated and identified from rat intestine and opossum urine, respectively. They control salt and water transport in the kidney and intestine mediated by cGMP. In this study we tried to show the effects of the guanylin-like peptides on EEG-parameters, as well to investigate possible cerebral action mechanisms in the central nervous system. The experiments were performed using anaesthetized male Wistar rats that were placed on the stereotaxic frame for surgery to implant a guide cannula towards to cisterna magna. After 48 hours, the animals were divided in three groups: guanylin (2&#956;g/&#956;l/min) and uroguanylin (2&#956;g/&#956;l/min and 6&#956;g/&#956;l/min), and recived intracisternal infusion by a infusion pump. Another two groups were performed using uroguanylin (2&#956;g/&#956;l/min) and a pretreatment of two Cl&#713; blockers: niflumic acid and nedocromil sodium. EEG recordings were made throughout the experimental procedure, using a software for spectral activity study and absolute amplitude, starting with the control recording segment, followed by drug infusion segment and finishing with after infusion segment. Guanylin peptide in the rat brain increased the frontal waves amplitude and induced spikes. Uroguanylin induced the same changes more intensively (p<0.05). Niflumic acid didnât promoted changes, but nedocromil seemed to inhibit the spikes (p<0.05). We propose that guanylin and uroguanilyn EEG effects were caused by Cl&#713; channels envolvement. / Os peptÃdeos termo-estÃveis guanilina e uroguanilina foram inicialmente isolados e identificados do intestino de rato e de urina de opossum: suas propriedades sÃo atribuÃdas ao controle do transporte de sal e Ãgua no rim e intestino, mediado pelo GMPc. O presente estudo propÃe-se a avaliar a atividade neurofisiolÃgica dos peptÃdeos do tipo guanilina, atravÃs da anÃlise do registro eletroencefÃlico, bem como investigar os mecanismos de aÃÃo responsÃveis pela possÃvel aÃÃo sobre o sistema nervoso central. Para tanto, grupos de ratos Wistar machos anestesiados foram submetidos a uma cirurgia para a colocaÃÃo de uma cÃnula na cisterna magna. Decorridas 48 horas da cirurgia, estes animais foram novamente anestesiados, sendo infundidas atravÃs de uma bomba de infusÃo: guanilina (2&#956;g/&#956;l/min) e uroguanilina (2&#956;g/&#956;l/mim e 6&#956;g/&#956;l/min), em trÃs grupos distintos. Posteriormente, outros dois grupos de animais foram submetidos ao mesmo protocolo experimental, com a uroguanilina, porÃm adicionalmente, receberam um prÃ-tratamento (antes da infusÃo) de duas substÃncias bloqueadoras de canais de Cl&#713;: o Ãcido niflÃmico e o nedocromil sÃdico. Durante a infusÃo intracisternal dos peptÃdeos, houve o registro do EEG dos diversos espectros de ondas, sendo gravados trÃs momentos: antes da infusÃo ( controle), durante e apÃs a infusÃo. O peptÃdeo guanilina quando infundido em cÃrebro de ratos levou a alteraÃÃes na amplitude do traÃado e o surgimento de pontas no EEG. A uroguanilina induziu as mesmas alteraÃÃes, contudo houve uma maior intensidade (p<0.05). O prÃ-tratamento com Ãcido niflÃmico nÃo influiu nos resultados da infusÃo de uroguanilina, porÃm o nedocromil inibiu o surgimento de pontas (p<0.05). Sugerimos atravÃs deste estudo, que os peptÃdeos guanilina e uroguanilina produzem alteraÃÃes eletroencefalogrÃficas, atuando sobre o cÃrebro por mecanismos de aÃÃo envolvendo canais de Cl&#713;.

O ácido indol-3-acético (AIA) é um produto do metabolismo do triptofano encontrado nos organismos animais, vegetais e em microrganismos. Destacam-se os trabalhos que atribuíram ao AIA efeitos tanto antioxidantes quanto proxidantes em diferentes sistemas biológicos. O objetivo do presente estudo foi o de avaliar os efeitos da administração do AIA no metabolismo muscular e cerebral e na atividade elétrica cerebral de ratos. Foram realizados dois grupos de experimentos. No primeiro grupo foram avaliados os seguintes parâmetros: taxa glicêmica e o ganho de peso corporal de animais tratados por 14 dias com AIA (40 mg/Kg de peso vivo, via intragástrica); atividade das enzimas antioxidantes glutationa redutase (GR), catalase (CAT) e superóxido dismutase (SOD) e das enzimas do metabolismo da glicose hexoquinase (HQ), lactato desidrogenase (LDH) e glicose-6-fosfato desidrogenase (G6PDH) nos músculos sóleo e gastrocnêmio e a atividade da enzimas antioxidantes GR, CAT e SOD e a quantificação dos produtos resultantes da peroxidação lipídica (TBARs) no cérebro de ratos tratados por 14 dias com diferentes doses de AIA (1, 18 e 40 mg/Kg de peso animal, via intragástrica). Os respectivos controles de todas essas análises foram obtidos de ratos que receberam 1 mL de tampão fosfato pH 7,4 via intragástrica sob as mesmas condições experimentais. No segundo grupo de experimentos foi obtido o eletroencefalograma (EEG) dos animais. O EEG obtido foi filtrado nas bandas de freqüências delta (0,3-4 Hz), teta (4-8 Hz), alfa (8-12 Hz) e beta (12-30 Hz) e em cada banda calculou-se a energia do sinal. Foram avaliados o EEG de animais tratados com AIA (40 mg/Kg de peso vivo) e tratados com triptofano (40 mg/Kg de peso animal), ambos por via intragástrica. Os controles para esses tratamentos foram o EEG coletado 1 hora antes e 1 hora depois da administração de 1mL de tampão fosfato por via intragástrica no mesmo animal que recebeu o tratamento. Os resultados foram analisados por ANOVA com significância de 0,05 usando o teste de Tuckey e os estimadores foram validados usando-se bootstrap. A adminitração de AIA (40mg/Kg de peso vivo) não alterou a taxa glicêmica e evolução de peso corporal dos animais, em relação ao controle. Não foram observadas diferenças significativas entre os resultados obtidos de amostras de animais tratados com AIA (todas as doses) em relação aos respectivos controles para: atividade das enzimas antioxidantes muscular e cerebral; enzimas envolvidas com o metabolismo da glicose muscular; conteúdo de peroxidação lipídica (TBARs) cerebral. O método não invasivo de aquisição de EEG desenvolvido para ratos permitiu adquirir e analisar o sinal elétrico cerebral. Não foram observadas alterações no padrão do EEG após a administração de tampão fosfato. No entanto, o AIA na dose de 40 mg/Kg de peso vivo alterou o padrão do EEG do animal, pois, a energia das freqüências de ondas alfa (8-12 Hz) e beta (12-30 Hz) foi maior em relação ao estado normal e após administração de tampão fosfato. Já o triptofano na dose de 40 mg/Kg de peso vivo aumentou a energia da onda delta (0,3-4 Hz) e diminuiu na energia da onda beta (12-30 Hz) em relação ao estado normal. O método não invasivo de EEG para rato desenvolvido neste trabalho foi sensível para detectar a atividade elétrica encefálica dos animais e o triptofano serviu como parâmetro de referência, pois promoveu diferentes alterações no padrão do EEG daquelas observadas nos animais tratados com AIA. Conclui-se que o AIA não interferiu nos parâmetros metabólicos oxidativos e energéticos dos músculos e do cérebro dos animais estudados, mas promoveu alterações fisiológicas que desencadearam as mudanças observadas na energia do sinal de EEG dos animais. / Indole-3-acetic acid (IAA) is a tryptophan metabolic found in animals organisms, microorganisms and vegetables. It is remarkable the work done to IAA antioxidants and proxidants effects in several biological systems. The main purpose of these studies was to evaluate the effects of intragastric IAA administration in brain and in muscle metabolism and electrical brain activities in rat. The experiments were done in two groups. The first one, were evaluated the following parameters: glycemic rate and corporal gain weight to those animals treated14 days with IAA (40 mg/Kg of body weight); activity of antioxidants enzymes as glutathione reductase (GR), catalase (CAT), superoxide dismutase (SOD); activities of hexokinase (HQ), lactate dehidrogenase (LDH) and glucose-6-phosphate dehidrogenase (G6PDH) on soleus and gastrocnemic muscle; antioxidants enzymes activities and level of tiobarbituric reactives subtances (TBARs) in brain from rats treated during 14 days with doses of IAA (1,18 and 40 Kg/kg body of weight). All those analyses controls were obtained from rat that was given 1 mL of phosphate buffered saline, pH 7 (PBS), under the same experiments conditions as the group treated with IAA. On the second group of experiments was evaluated EEG pattern obtained from fixed electrodes on the animal skin surface were not sedated, and shown at delta frequency (0.3-4 Hz), theta (4-8 Hz), alpha (8-12 Hz) and beta (12-30 Hz) and the energy of those band frequency was calculated using a developed algorithm software MATLAB®. EEG was evaluated from animals treated with IAA (40 mg/Kg body weight) and treated with tryptophan (40 mg/Kg body weight), both intragastric. The management control for those treatments were EEG collected 1 hour before and 1 hour after the intragstric administration of 1mL PBS at the same animal that received the treatment. The results were analysed by ANOVA with great significance of 0.05 using the Tukey test and were evaluated by bootstrap. The IAA administration (40 mg/Kg body weight) did not change the glycaemia rate and the animal weight evolution, to compare with the control. Were not observed any significant differences among results from animals treated with IAA (all doses) relating to respective controls to: a) brain and muscles antioxidants enzymes activity; b) activities of enzymes with muscular glucose metabolism; c) brain lipid peroxidation contents by TBARs level. No invasive EEG colleting methods developed for rat allowed to collect and analyse electric brain signal. After an administration of PBS, were not observed any changes at EEG pattern. IAA dose of 40 mg/Kg body weight did change the animal EEG standard, the frequency energy of alpha wave to (8-12 Hz) and beta (12-30 Hz) was higher then normal after administration of PBS. On the other side, tryptophan dose of 40 mg/Kg body weight increased the delta wave energy to (0,3-4 Hz) and decreased the beta wave energy to (12-30 Hz), to compare withfthe normal standard. Non invasion EEG colleting methods for rat developed in this studies was sensible in order to detect an animals electric encephalic activity and the tryptophan became as reference parameter, due to several changes on pattern EEG to those animals treated with IAA. Concluding that, IAA did not interfere on oxidative metabolic parameters, neither to the brain and muscles of the studied animals, but promoted physiological changes that was possible to observe on animals electroencephalogram.

Risk-taking behavior (RTB) is defined as behavior involving the probability of reward with concurrent probability of some negative outcome. Peer influence is among the most robust predictors of RTB, such that greater peer influence, particularly deviant or delinquent peer influence, is associated with increased RTB. Evidence suggests that those with genetic predispositions for RTB may also be more susceptible to peer influence as a function of genotype. Given that genetic polymorphisms within the dopaminergic system have evidenced associations with various forms of RTB and delinquent peer affiliation, it is possible that these genes may interact with peer influence to predict increased RTB, a process called gene × environment interaction (G×E). We expected that those genetically at risk would take more risks in the presence of a peer than alone. To test this effect, five polymorphisms within the dopaminergic system were genotyped in a sample of 85 undergraduate students. Participants completed a behavioral risk task alone and in the presence of a peer providing "risky" feedback. No significant G×Es were identified for any of the dependent variables. However, participants took significantly more risks in the presence of a risky peer than when taking risks alone. These results suggest that G×E may not be a relevant process for peer-influenced RTB during late adolescence. It is possible that G×E is a relevant process during early adolescence, while gene-environment correlation (rGE) is the dominant process during late adolescence. Future research would benefit from testing whether these genes are relevant to G×E in early adolescence, as well as to rGE during late adolescence.

L’électroencéphalogramme (EEG), mesure de l'activité électrique du cerveau, reste une des meilleures méthodes de prévision non-invasive des résultats neurologiques. L'objectif de notre travail est de développer un système de classification automatique qui prévoit des risques sur la maturation cérébrale, se traduisant par un état pathologique à 2 ans. Les caractéristiques du signal EEG, qui sont utiles à la prévision automatisée, sont traitées via un module appelée EEGDiag, et sont appliquées sur un ensemble de données issues de 397 dossiers de nouveau-nés prématurés. Chaque dossier comprend un enregistrement EEG, et un rapport concernant les informations et les diagnostics de l’enfant à la naissance et 2 ans plus tard (normal, pathologique ou douteux). Pour aider les médecins à prévenir l’état neurologique anormal du nouveau-né prématuré, nous avons développé plusieurs modèles de classification qui s’appliquent sur différentes séries de caractéristiques du signal EEG inspirées des annotations des neuropédiatres. Plusieurs modèles de classification et d’aide à la décision sont testés sur différentes extractions de la base de données afin de fournir aux médecins le système de classification le plus performant. Notre système proposé permet de détecter automatiquement des pronostics sur l’état pathologique du nouveau-né prématuré. Notre travail a consisté à subdiviser l’amplitude des bouffées du signal EEG en trois catégories : faible, moyenne et forte. Cette étude de subdivision a permis de choisir les intervalles associés à ces trois catégories permettant d’augmenter considérablement la performance de notre système de classification automatique. Une analyse de corrélation a permis de détecter des relations d’indépendance et de redondance entre certaines données, ce qui permet de réduire le nombre de variables décisives et de sélectionner ainsi la meilleure série de variables qui ramènent notre système à devenir optimal et plus efficace. Ces études nous ont permis d’atteindre un système de classification automatique basé sur une série de 17 variables avec une exactitude 93.2%. Ce système peut apporter une bonne sensibilité à la prévision de l’état neurologique du nouveau-né prématuré et peut servir comme aide à la décision dans le traitement clinique. / The electroencephalogram (EEG), a measure of the electrical activity of the brain, remains one of the best non-invasive methods for predicting neurological outcomes. The aim of our work is to develop an automatic classification system which predicts risks on cerebral maturation that can lead to a pathological condition at 2 years. The EEG signal characteristics, which are useful for automated prediction, are processed via an application called EEGDiag, and applied to a set of 397 records for premature infants. Each record include an EEG record and a report on infant information and diagnosis at birth and 2 years later (normal, sick or risky). To assist physicians in preventing any abnormal neurological condition of the premature newborn, we have developed several intelligent classification models which can be applied to several series of characteristics of the EEG inspired from the annotations of neuropediatricians. Several classification and decisional aid models have been tested on different extracted databases in order to offer to doctors the best efficient classification system. Our proposed system automatically detects the prognosis of the premature newborn pathological condition. Our work consisted of subdividing the amplitude of EEG signal burst into three categories: low, medium and high. This subdivision study allowed to choose Intervals of these three categories which have served to greatly increase the performance of our intelligent classification system. A correlative data analysis allowed to create an independence and redundancy relation between the data attributes, which reduces the number of decisive parameters and thus selects the best series of parameters that made our system optimal and more efficient. These studies enabled us to achieve a classification system based on a series of 17 parameters with an accuracy 93.2%. This system can provide good sensitivity on predicting the neurological status of premature newborn and can be used as a decisional aid in clinical treatment.

Lorsque l'on enregistre l'activité cérébrale en électroencéphalographie (EEG) de surface, le signal d'intérêt est fréquemment bruité par des activités différentes provenant de différentes sources de bruit telles que l'activité musculaire. Le débruitage de l'EEG est donc une étape de pré-traitement important dans certaines applications, telles que la localisation de source. Dans cette thèse, nous proposons six méthodes permettant la suppression du bruit de signaux EEG dans le cas particulier des activités enregistrées chez les patients épileptiques soit en période intercritique (pointes) soit en période critique (décharges). Les deux premières méthodes, qui sont fondées sur la décomposition généralisée en valeurs propres (GEVD) et sur le débruitage par séparation de sources (DSS), sont utilisées pour débruiter des signaux EEG épileptiques intercritiques. Pour extraire l'information a priori requise par GEVD et DSS, nous proposons une série d'étapes de prétraitement, comprenant la détection de pointes, l'extraction du support des pointes et le regroupement des pointes impliquées dans chaque source d'intérêt. Deux autres méthodes, appelées Temps Fréquence (TF) -GEVD et TF-DSS, sont également proposées afin de débruiter les signaux EEG critiques. Dans ce cas on extrait la signature temps-fréquence de la décharge critique par la méthode d'analyse de corrélation canonique. Nous proposons également une méthode d'Analyse en Composantes Indépendantes (ICA), appelé JDICA, basée sur une stratégie d'optimisation de type Jacobi. De plus, nous proposons un nouvel algorithme direct de décomposition canonique polyadique (CP), appelé SSD-CP, pour calculer la décomposition CP de tableaux à valeurs complexes. L'algorithme proposé est basé sur la décomposition de Schur simultanée (SSD) de matrices particulières dérivées du tableau à traiter. Nous proposons également un nouvel algorithme pour calculer la SSD de plusieurs matrices à valeurs complexes. Les deux derniers algorithmes sont utilisés pour débruiter des données intercritiques et critiques. Nous évaluons la performance des méthodes proposées pour débruiter les signaux EEG (simulés ou réels) présentant des activités intercritiques et critiques épileptiques bruitées par des artéfacts musculaires. Dans le cas des données simulées, l'efficacité de chacune de ces méthodes est évaluée d'une part en calculant l'erreur quadratique moyenne normalisée entre les signaux originaux et débruités, et d'autre part en comparant les résultats de localisation de sources, obtenus à partir des signaux non bruités, bruités, et débruités. Pour les données intercritiques et critiques, nous présentons également quelques exemples sur données réelles enregistrées chez des patients souffrant d'épilepsie partielle. / In the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing stages including spike peak detection, extraction of exact time support of spikes and clustering of spikes involved in each source of interest. Two other methods, called Time Frequency (TF)-GEVD and TF-DSS, are also proposed in order to denoise ictal EEG signals for which the time-frequency signature is extracted using the Canonical Correlation Analysis method. We also propose a deflationary Independent Component Analysis (ICA) method, called JDICA, that is based on Jacobi-like iterations. Moreover, we propose a new direct algorithm, called SSD-CP, to compute the Canonical Polyadic (CP) decomposition of complex-valued multi-way arrays. The proposed algorithm is based on the Simultaneous Schur Decomposition (SSD) of particular matrices derived from the array to process. We also propose a new Jacobi-like algorithm to calculate the SSD of several complex-valued matrices. The last two algorithms are used to denoise both interictal and ictal data. We evaluate the performance of the proposed methods to denoise both simulated and real epileptic EEG data with interictal or ictal activity contaminated with muscular activity. In the case of simulated data, the effectiveness of the proposed algorithms is evaluated in terms of Relative Root Mean Square Error between the original noise-free signals and the denoised ones, number of required ops and the location of the original and denoised epileptic sources. For both interictal and ictal data, we present some examples on real data recorded in patients with a drug-resistant partial epilepsy.