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Decision-making is necessary to adapt to the variable environment in everyday life. During this process, our goal is to select the most beneficial course of action in order to obtain the best outcome, to develop efficient choice strategies. That is, estimating the probability to obtain any of the available outcomes as well as their value. Moreover, poor decision-making ability is a common symptom to several psychiatric disorders, such as pathological gambling, depression, schizophrenia and bipolar disorder.The cognitive and emotional mechanisms controlling decision-making processes depend, among others, on the striatum, Basal Ganglia’s main input nucleus. The striatum is divided into the dorsal striatum, responsible for motor and cognitive control that initiate actions (Dorsomedial Striatum, DMS) and generate habits (Dorsolateral Striatum, DLS), and Nucleus Accumbens (NAc) which manages reward and the influence of motivation on motor behavior. A2A-expressing and D1-expressing medium spiny neurons (iMSNs and dMSNs, respectively), accounting for 95% of striatal neurons act in coordination to generate adaptive behavioral responses. It has been shown that imbalanced activity between these two populations leads to abnormal behaviors: overactivation of striatonigral neurons promotes an increased locomotion as well as a higher sensitivity for reward, whereas overactivation of striatopallidal neurons produces the exact opposite effects. However, the specific contributions to decision-making of these two populations in each striatal territory remains unclear. Here, we made use of a chemogenetic (DREADD) tool to manipulate striatal projection neurons’ activity within each specific striatal area and tested their role in a decision-making operant protocol. To do so, we used two different mouse models that allowed us to target specifically iMSNs (A2A-Cre mice) or dMSNs (D1-Cre mice) and induce neuronal-specific expression of the hM3Dq DREADD receptor. CNO-mediated activation of these receptors led to neuronal activation. Then, we tested DREADD-dependent activation of MSNs during the Iowa Gambling Task (IGT), a test used to assess the influence of different rewards on choice and to evaluate the ability of mice to develop advantageous choice strategies. We found an exclusive role of DMS’ dMSNs in controlling choice preference, as DREADD-induced activation of these neurons produced a loss of preference. Manipulations of MSNs in other striatal areas led to altered task performance without affecting choice preference.These results contribute to a better understanding of the role of the striatum on decision-making and moreover, suggest the existence of a high level of functional specialization in this area, a fact that could be explained by the local circuits in which each MSN population is involved. / Doctorat en Sciences biomédicales et pharmaceutiques (Médecine) / info:eu-repo/semantics/nonPublished

La schizophrénie est un trouble mental débilitant qui se caractérise par des perturbations de la pensée, des émotions et de la cognition. Ces processus d’intégration fonctionnelle sont généralement associés à des oscillations bioélectriques cérébrales synchrones dans la bande de fréquence gamma (30-80 Hz), lesquelles sont aussi altérées chez des patients souffrant de schizophrénie. Ce travail de thèse vise à développer des méthodes et des outils pour étudier les mécanismes neuronaux sous-tendant les altérations de ces oscillations physiopathologiques. Pour ce faire, nous avons développé des modèles de rongeurs de laboratoire pour la schizophrénie. Nous avons démontré que des modifications génétiques ou pharmacologiques conduisent à des perturbations des oscillations gamma et que des médicaments antipsychotiques peuvent les moduler. / Schizophrenia is a debilitating mental disorder that is characterised by a breakdown in normal thought processes, blunted emotional responses and a variety of cognitive difficulties. Gamma frequency (30 – 80 Hz) oscillations are associated with many processes that are disrupted in people with schizophrenia memory, perception and attention. This thesis aimed to develop methods and tools to investigate the basic mechanisms that underlie the alterations in gamma frequency brain activity that are observed in patients suffering from schizophrenia. To do this we developed a variety of experimental animal models for schizophrenia. We successfully demonstrated that both genetic and pharmacological changes lead to alterations in gamma oscillations and that antipsychotic medications can modulate them.

Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.