Structure-Dynamics relationship in basal ganglia: Implications for brain function

Abstract

In this thesis, I have used a combination of computational models such as mean field and spikingnetwork simulations to study various sub-circuits of basal ganglia. I first studied the striatum(chapter 2), which is the input nucleus of basal ganglia. The two types of Medium SpinyNeurons (MSNs), D1 and D2-MSNs, together constitute 98% of the neurons in striatum. The computational models so far have treated striatum as a homogenous unit and D1 and D2 MSNs as interchangeable subpopulations. This implied that a bias in a Go/No-Go decision is enforced viaexternal agents to the striatum (eg. cortico-striatal weights), thereby assigning it a passive role.New data shows that there is an inherent asymmetry in striatal circuits. In this work, I showed that striatum due to its asymmetric connectivity acts as a decision transition threshold devicefor the incoming cortical input. This has significant implications on the function of striatum as an active participant in influencing the bias towards a Go/No-Go decision. The striatal decisiontransition threshold also gives mechanistic explanations for phenomena such as L-Dopa InducedDyskinesia (LID), DBS-induced impulsivity, etc. In chapter 3, I extend the mean field model toinclude all the nuclei of basal ganglia to specifically study the role of two new subpopulationsfound in GPe (Globus Pallidus Externa). Recent work shows that GPe, also earlier consideredto be a homogenous nucleus, has at least two subpopulations which are dichotomous in theiractivity with respect to the cortical Slow Wave (SWA) and beta activity. Since the data for the sesubpopulations are missing, a parameter search was performed for effective connectivities using Genetic Algorithms (GA) to fit the available experimental data. One major result of this studyis that there are various parameter combinations that meet the criteria and hence the presenceof functional homologs of the basal ganglia network for both pathological (PD) and healthynetworks is a possibility. Classifying all these homologous networks into clusters using somehigh level features of PD shows a large variance, hinting at the variance observed among the PDpatients as well as their response to the therapeutic measures. In chapter 4, I collaborated on a project to model the role of STN and GPe burstiness for pathological beta oscillations as seenduring PD. During PD, the burstiness in the firing patterns of GPe and STN neurons are shownto increase. We found that in the baseline state, without any bursty neurons in GPe and STN,the GPe-STN network can transition to an oscillatory state through modulating the firing ratesof STN and GPe neurons. Whereas when GPe neurons are systematically replaced by burstyneurons, we found that increase in GPe burstiness enforces oscillations. An optimal % of burstyneurons in STN destroys oscillations in the GPe-STN network. Hence burstiness in STN mayserve as a compensatory mechanism to destroy oscillations. We also propose that bursting inGPe-STN could serve as a mechanism to initiate and kill oscillations on short time scales, asseen in the healthy state. The GPe-STN network however loses the ability to kill oscillations in the pathological state.