Progesterone actions in protein phosphorylation in the central nervous system

O presente trabalho propõe-se esclarecer o papel que a progesterona e os seus metabolitos exercem no sistema nervoso central. Nos últimos anos, com a descoberta da síntese local de esteróides no cérebro, a progesterona, assim como outras hormonas sexuais, ganharam uma relevância crescente em fenómenos tais como plasticidade neuronal e neuroprotecção. Ainda que já se comece a entender o papel de muitas hormonas no cérebro, tal como o estrogénio, o papel da progesterona continua menos conhecido. Deste modo, o nosso trabalho centrou-se na elucidação dos efeitos da progesterona em fenómenos de sobrevivência celular, plasticidade neuronal/sináptica. Graças à colaboração com um grupo pioneiro em estudos sobre hormonas sexuais neuroactivas, o presente trabalho fornece uma importante contribuição ao entendimento do papel desta hormona no sistema nervoso central. Este trabalho fornece novos dados, relativamente ao papel da progesterona e dos seus metabolitos reduzidos na regulação de vias de sinalização associadas com sobrevivência celular, tal como Akt/PI3K e ERK. Também é analisado o efeito do tratamento hormonal na expressão e estado de fosforilação da proteína Tau, sendo ainda motivo de estudo cinases e fosfatases envolvidas nestes mecanismos.

Phase transitions and nonlinear phenomena in neuronal network models

Communication and cooperation between billions of neurons underlie the power of the brain. How do complex functions of the brain arise from its cellular constituents? How do groups of neurons self-organize into patterns of activity? These are crucial questions in neuroscience. In order to answer them, it is necessary to have solid theoretical understanding of how single neurons communicate at the microscopic level, and how cooperative activity emerges. In this thesis we aim to understand how complex collective phenomena can arise in a simple model of neuronal networks. We use a model with balanced excitation and inhibition and complex network architecture, and we develop analytical and numerical methods for describing its neuronal dynamics. We study how interaction between neurons generates various collective phenomena, such as spontaneous appearance of network oscillations and seizures, and early warnings of these transitions in neuronal networks. Within our model, we show that phase transitions separate various dynamical regimes, and we investigate the corresponding bifurcations and critical phenomena. It permits us to suggest a qualitative explanation of the Berger effect, and to investigate phenomena such as avalanches, band-pass filter, and stochastic resonance. The role of modular structure in the detection of weak signals is also discussed. Moreover, we find nonlinear excitations that can describe paroxysmal spikes observed in electroencephalograms from epileptic brains. It allows us to propose a method to predict epileptic seizures. Memory and learning are key functions of the brain. There are evidences that these processes result from dynamical changes in the structure of the brain. At the microscopic level, synaptic connections are plastic and are modified according to the dynamics of neurons. Thus, we generalize our cortical model to take into account synaptic plasticity and we show that the repertoire of dynamical regimes becomes richer. In particular, we find mixed-mode oscillations and a chaotic regime in neuronal network dynamics.