Cocaine is known for its severe addictive properties and still, there is no effective treatment for cocaine addiction. Cocaine is a powerful chemical substance. It enters the brain rapidly and cause abnormal high levels of dopamine. Dopamine is found to be the neural correlate for reward. Addictive drugs such as cocaine are reported to be rewarding and initially generate many dimensions of positive effects. However, repeated cocaine use are associated with both structural and functional abnormalities in several brain regions, especially in areas responsible for voluntary control. Loss of control gives way to compulsive consumption and craving for more cocaine stimulation. These neuronal changes and negative symptoms tend to occur gradually, while the tolerance increases. The addicted individual has to enhance the dose in order to obtain the desired effect, which is; becoming physically dependent of a substance. Also, dysregulation of reward circuitries causes decreased sensitivity to natural rewards leading to increased interest in cocaine-related reward stimulation. The abstinence usually last for long time, even years, after self-administration, which makes addicts highly sensitive to relapse. Up to date, effective therapeutic interventions and pharmacological treatments are limited. Neurostimulation techniques such as DBS have shown positive results in regulation of dopaminergic excitability. Though, more research in the complexity of dopamine and mesolimbic areas is well needed, in order to better understand the neural basis of cocaine addiction and be able to offer evidence-based treatments. This thesis will provide an overview of the neuronal impact of cocaine on the dopaminergic reward circuitries in the brain.

The brain – the organ that allows us to be aware of our surroundings – consists of a complex network of neurons, which seemingly allows the human brain to be able of abstract thinking, emotions, and cognitive function. To learn how the brain is capable of this, the two main branches of neuroscience study either neurons in detail, or how they communicate within neuronal networks. Both these branches often tackle the complexity using a combination of experiments and mathematical modelling. A third and less studied aspect of neuroscience concerns the neurovascular coupling (NVC), for which my research group has previously developed mathematical models. However, these NVC models have still not integrated valuable data from rodents and primates, and the NVC models are also not connected to existing neuronal network models. In this project, I address both of these two shortcomings. First, an existing model for the NVC was connected with a simple model for neuronal networks, establishing a connection between the NVC models and the software NEURON. Second, we established a way to preserved information from NVC data from rodents and mice into NVC models humans. This work thus connects the previously developed NVC model both with data from other species and with other types of models. This brings us one step closer to a more holistic and interconnected understanding of the brain and its many intriguing cognitive and physiological functions.