Computational Modelling and Topological Analysis of the striatal microcircuitry in health and Parkinson's disease
Time: Mon 2023-12-11 13.30
Location: D37, Lindstedtsvägen 5, Stockholm
Video link: https://kth-se.zoom.us/j/64099915499
Subject area: Computer Science
Doctoral student: Ilaria Carannante , Beräkningsvetenskap och beräkningsteknik (CST), Science for Life Laboratory, SciLifeLab, Hellgren Kotaleski's group
Opponent: Professor Sean Hill,
Supervisor: Professor Jeanette Hellgren Kotaleski, Beräkningsvetenskap och beräkningsteknik (CST); Alexander Kozlov, Beräkningsvetenskap och beräkningsteknik (CST)
The basal ganglia are evolutionary conserved nuclei located at the base of the forebrain. They are a central hub in the control of motion and their dysfunctions lead to a variety of movement related disorders, including Parkinson's disease (PD).
The largest nucleus and main input stage of the basal ganglia is the striatum. It receives excitatory glutamatergic projections primarily from cortex and thalamus as well as modulatory dopaminergic input from the substantia nigra pars compacta and the ventral tegmental area. Striatal output is mediated by the direct and indirect pathway striatal projection neurons (dSPNs and iSPNs, respectively). In rodents, they account for 95% of the neurons, while the remaining 5% are interneurons, which do not project outside the striatum.
The aim of this thesis is to develop an in silico striatal microcircuit in health and PD, and to compare these two networks using electrophysiological simulations and topological analysis.
The neuron types included are the striatal projection neurons (dSPN and iSPN) and three of the main interneuron classes: FS, LTS and ChIN. Their multi-compartmental models are based on detailed morphological reconstructions, ion channels expression and electrophysiological ex vivo rodents experimental data from control and PD brains.
In Paper A, a comparison between two methods commonly used to model ion channels was presented.
In Paper B, the healthy striatal microcircuit was created. We presented a modelling framework called Snudda. It enables the creation of large-scale networks by: placing neurons using appropriate density, predicting synaptic connectivity based on touch detection and a set of pruning rules, setting up external input and modulation, and finally running the simulations. It is written in Python and uses the NEURON simulator.
In Paper C, we conducted a computational study on the reciprocal interaction between ChIN and LTS interneurons. Specifically, we simulate the inhibition of LTS via muscarinic M4 receptors following acetylcholine release from ChIN as well as the prolonged depolarization of ChIN subsequent to the release of nitric oxide from LTS.
In Paper D, we developed a pipeline to model the NMDA and AMPA postsynaptic currents in striatal neurons following glutamate release from cortex and thalamus. This was done to improve the accuracy of the existing synaptic models.
In Paper E, the PD striatal microcircuit was created. First, we modelled the morphological changes in both SPNs and FS as well as the electrophysiological alterations in SPNs. Then we predicted and quantified how the intrastriatal connectivity is altered using anatomically constrained synapse placement and topological analysis of the resulting network. Finally we investigated how the effective glutamatergic drive to SPNs is modified.
Overall, in this thesis we further advanced the development of the simulation framework for the study of the basal ganglia function and initiated systematic model-based large-scale computational analysis of their abnormal PD state.