• Computational neuroscience
• Beta oscillations
• Parkinson
• Basal Ganglia
• Dopamine

Parkinson’s Disease (PD) is linked to dopamine deficiency in the basal ganglia, resulting in functional impairment. Synchronized beta range ([12-30] Hz) oscillations in basal ganglia activity are frequently observed alongside PD motor symptoms. Dopaminergic drugs intake is known to decrease at the same time PD symptoms and these beta oscillations. However, neurodegeneration and drugs are not the only factors affecting dopamine levels, which are physiologically highly dynamic and fluctuate on different timescales, with sub-second transients, ramps that may last for several seconds and variations on the timescale of hours. The effects of such transient dopamine fluctuations on the activity Parkinsonian basal ganglia are still unclear.
Here I use tools from computational neuroscience, in particular neuronal network simulations, to address this issue. I explored spectral intensity changes related to fast dopamine modulations, using a spiking neural network model of the basal ganglia nuclei and their interactions. When the dopamine goes above a given threshold, beta oscillations decrease. I characterized the intensity of beta oscillations associated with fast variations of dopamine with expected values obtained sampling at different static dopamine levels, finding that the model reacts to rapid changes differently. In particular, the subthalamic nucleus population’s activity in the beta range changes with a delay compared to the expected intensity. Overall, the basal ganglia network presents a response to dynamical fluctuations of dopamine concentration with a characteristic time scale of fractions of second. This analysis contributes to understanding the intricate role of dopamine in Basal Ganglia oscillations, offering insights on the interaction between physiological and pathological fluctuations.
The work will be presented in five main Chapters:
The first one, the introductory chapter 1, provides an overview of the thesis, introducing the field of computational neuroscience and its relevance to understanding the main properties of the network of the Basal Ganglia. The focus is on the association between Parkinson’s disease and dopamine depletion, with an emphasis on the role of Dopamine depletion and the onset of pathological β oscillations, during dopamine fluctuations. The introduction also outlines the framework for biological applicability, setting the stage for subsequent chapters.
Chapter 2 provides a comprehensive understanding of the existing tools and approaches used in related research. It describes existing methodologies, main concepts and tools that are employed throughout the thesis. Particularly, we introduce the BG computational model used to simulate the network, summarizing the structure and functionalities of neuronal cells, describe their mathematical modelization and the use of homogeneous and non-homogeneous Poissonian processes used to model external input in the nodes of the network.
In Chapter 3, novel methodologies are introduced. We present more specific methods designed to deepen the properties of the employed model. The implementation of a pulse function with sigmoid shaped slopes for modeling dopamine depletion takes center stage, along with the characterization of β range oscillations intensity. The chapter also explores methods for determining the expected intensity in quasi-static variations of dopamine and burst detection.
Following, in Chapter 4, we present the results of the study. It explores the effects of dopamine variation on the basal ganglia network and offers a detailed comparison between quasi-static expected PSD and simulation results. The chapter describes the properties of the system as a function of dopamine depletion and how the model reacts to pulse variations of it, with a delayed response, particularly considering that this time range variations are present in striatal populations. Consequently, we show the neural activity as a function of the characteristics of the pulse. We then conclude this chapter with some results regarding the burst detection in the simulations.
Finally, in the conclusive chapter 5 we summarize key insights gained from the study, showing how gives its contributions to the understanding of dopamine variations in parkinsonian BG, and suggesting potential directions for future research, particularly in the context of Parkinson’s disease.