This in silico study predicts the impact that the single-cell neuronal morphological alterations will have on the striatal microcircuit connectivity. We find that the richness in the topological striatal motifs is significantly reduced in Parkinson's disease (PD), highlighting that just measuring the pairwise connectivity between neurons gives an incomplete description of network connectivity. Moreover, we predict how the resulting electrophysiological changes of striatal projection neuron excitability together with their reduced number of dendritic branches affect their response to the glutamatergic drive from the cortex and thalamus. We find that the effective glutamatergic drive is likely significantly increased in PD, in accordance with the hyperglutamatergic hypothesis.

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.

Basala ganglierna, som är placerade vid basen av framhjärnan, är evolutionärt konserverade kärnor. De utgör ett centralt nav för kontrollen av motoriken, och dysfunktioner i basala ganglierna leder till en mängd olika rörelserelaterade störningar, inklusive Parkinsons sjukdom (PD).Den största kärnan, är den del av basala ganglierna och som fungerar som det huvudsakliga input steget, är striatum. Den tar emot excitatoriska glutamaterga projektioner främst från cortex och thalamus såväl som modulerande dopaminerga input från substantia nigra pars compacta och det ventrala tegmentumområ det. Striatum projicerar till andra delar av basala ganglierna via de direkta och indirekta striatala projektionsneuronerna (dSPNs respektive iSPNs). De utaör 95% av neuronerna hos möss, medan de återstå ende 5% är interneuroner, som inte projicerar utanför striatum.

Syftet med denna avhandling är att bygga en in silico modell av det lokala striatala neuronnätverket som kan användas för att förstå både det friska nätverket och hur det förändras vid PD. Dessa nätverksmodeller jämförs sedan med hjälp av biofysikaliskt detaljerade simuleringar samt genom användandet av topologisk analys.

De neurontyper som ingår i modellen är de striatala projektionsneuronerna (dSPN och iSPN) och tre av de huvudsakliga interneurontyperna: FS, LTS och ChIN. Multi-kompartmentmodeller av dessa neurontyper baseras på detaljerade morfologiska rekonstruktioner av neuron,  genuttryck av jonkanaler samt elektrofysiologiska ex vivo experimentella data från möss som representerar kontroll- och PD-hjärnor.

I artikel A presenterades en jämförelse mellan två metoder som vanligtvis används för att modellera jonkanaler.

I artikel B byggde vi en modell av det lokala striatala neuronnätverket. Vi beskriver ett ramverk som heter Snudda för att bygga nätverket. Det möjliggör skapandet av storskaliga modellnätverk genom att: först placera neuroner med den densitet som uppmätts; sedan prediceras synapsernas placering baserat på detektion av var axon och dendriter är tillräckligt nära varann, och i detta steg används också en uppsättning s.k. pruningsregler; därefter definieras hur nätverksmodellen skall aktiveras; och slutligen körs simuleringarna. Koden är skriven i Python och använder NEURON-simulatorn.

I artikel C genomförde vi en simuleringsstudie av den reciproka interaktionen mellan ChIN och LTS interneuroner. Specifikt simulerar vi både hämningen av LTS via muskarina M4-receptorer, aktiverade av acetylkolinfrisättningen från ChIN, samt undersöker även den förlängda depolariseringen av ChIN som ses efter frisättning av kväveoxid från LTS.

I artikel D utvecklade vi en pipeline för att modellera NMDA- och AMPA-postsynaptiska strömmar i striatala neuroner efter glutamatfrisättning från cortex och thalamus. Målet med denna studie var att förbättra noggrannheten hos de synaptiska modeller som ofta använts i liknande studier.

I artikel E byggdes en modell av hur det lokala striatala nätverket förändras pga PD. Först modellerade vi de morfologiska förändringarna i både SPN och FS samt de elektrofysiologiska förändringarna i SPN. Sedan predicerade vi samt kvantifierade vi hur de intrastriatala synapsernas antal förändras som följd av de morfologiska förändringar som ses vid PD. För att kvantifiera våra resultat användes topologisk analys av det resulterande nätverket. Slutligen undersökte vi hur den effektiva glutamaterga aktiveringen av SPN modifieras vid PD.

Sammantaget har arbetena i denna avhandling utvecklat både ett modelleringsramverk för studier av basala gangliernas funktion samt initierat en systematisk modellbaserad storskalig beräkningsanalys av förändringar som ses vid PD.

Plateau potentials are a critical feature of neuronal excitability, but their all-or-none behavior is not easily captured in modeling. In this study, we investigated models of plateau potentials in multi-compartment neuron models and found that including glutamate spillover provides robust all-or-none behavior. This result arises due to the prolonged duration of extrasynaptic glutamate. When glutamate spillover is not included, the all-or-none behavior is very sensitive to the steepness of the Mg2+ block. These results suggest a potentially significant role of glutamate spillover in plateau potential generation, providing a mechanism for robust all-or-none behavior across a wide range of slopes of the Mg2+ block curve. We also illustrate the importance of the all-or-none plateau potential behavior for nonlinear computation with regard to the nonlinear feature binding problem.

The basal ganglia are important for action initiation, selection, and motor learning. The input level, the striatum, receives input preferentially from the cortex and thalamus and is to 95% composed of striatal projection neurons (SPNs) with sparse GABAergic collaterals targeting distal dendrites of neighboring SPNs, in a distance-dependent manner. The remaining 5% are GABAergic and cholinergic interneurons. Our aim here is to investigate the role of surround inhibition for the intrinsic function of the striatum. Large-scale striatal networks of 20 to 40 thousand neurons were simulated with detailed multicompartmental models of different cell types, corresponding to the size of a module of the dorsolateral striatum, like the forelimb area (mouse). The effect of surround inhibition on dendritic computation and network activity was investigated, while groups of SPNs were activated. The SPN-induced surround inhibition in distal dendrites shunted effectively the corticostriatal EPSPs. The size of dendritic plateau-like potentials within the specific dendritic segment was both reduced and enhanced by inhibition, due to the hyperpolarized membrane potential of SPNs and the reversal-potential of GABA. On a population level, the competition between two subpopulations of SPNs was found to depend on the distance between the two units, the size of each unit, the activity level in each subgroup and the dopaminergic modulation of the dSPNs and iSPNs. The SPNs provided the dominating source of inhibition within the striatum, while the fast-spiking interneuron mainly had an initial effect due to short-term synaptic plasticity as shown in with ablation of the synaptic interaction.

The majority of excitatory synapses in the brain uses glutamate as neurotransmitter, and the synaptic transmission is primarily mediated by AMPA and NMDA receptors in postsynaptic neurons. Here, we present data-driven models of the postsynaptic currents of these receptors in excitatory synapses in mouse striatum. It is common to fit two decay time constants to the decay phases of the current profiles but then compute a single weighted mean time constant to describe them. We have shown that this approach does not lead to an improvement in the fitting, and, hence, we present a new model based on the use of both the fast and slow time constants and a numerical calculation of the peak time using Newton's method. Our framework allows for a more accurate description of the current profiles without needing extra data and without overburdening the comptuational costs. The user-friendliness of the method, here implemented in Python, makes it easily applicable to other data sets.

The striatum is the main input stage of the basal ganglia receiving extrinsic input from cortex and thalamus. The striatal projection neurons (SPN) constitute 95% of the neurons in the striatum in mice while the remaining 5% are cholinergic and GABAergic interneurons. The cholinergic (ChIN) and low-threshold spiking interneurons (LTS) are spontaneously active and form a striatal subnetwork involved in salience detection and goal-directed learning. Activation of ChINs has been shown to inhibit LTS via muscarinic receptor type 4 (M4R) and LTS in turn can modulate ChINs via nitric oxide (NO) causing a prolonged depolarization. Thalamic input prefentially excites ChINs, whereas input from motor cortex favours LTS, but can also excite ChINs. This varying extrinsic input with intrinsic reciprocal, yet opposing, effects raises the possibility of a slow input-dependent modulatory subnetwork. Here, we simulate this subnetwork using multicompartmental neuron models that incorporate data regarding known ion channels and detailed morphological reconstructions. The modelled connections replicate the experimental data on muscarinic (M4R) and nitric oxide modulation onto LTS and ChIN, respectively, and capture their physiological interaction. Finally, we show that the cortical and thalamic inputs triggering the opposing modulation within the network induce periods of increased and decreased spiking activity in ChINs and LTS. This could provide different temporal windows for selective modulation by acetylcholine and nitric oxide, and the possibility of interaction with the wider striatal microcircuit. 

The basal ganglia play an important role in decision making and selection of action primarily based on input from cortex, thalamus, and the dopamine system. Their main input structure, striatum, is central to this process. It consists of two types of projection neurons, together representing 95% of the neurons, and 5% of interneurons, among which are the cholinergic, fast-spiking, and low threshold-spiking subtypes. The membrane properties, somadendritic shape, and intrastriatal and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they can be simulated in sufficient detail to capture their intrinsic properties, as well as the connectivity. We focus on simulation at the striatal cellular/microcircuit level, in which the molecular/subcellular and systems levels meet. We present a nearly full-scale model of the mouse striatum using available data on synaptic connectivity, cellular morphology, and electrophysiological properties to create a microcircuit mimicking the real network. A striatal volume is populated with reconstructed neuronal morphologies with appropriate cell densities, and then we connect neurons together based on appositions between neurites as possible synapses and constrain them further with available connectivity data. Moreover, we simulate a subset of the striatum involving 10,000 neurons, with input from cortex, thalamus, and the dopamine system, as a proof of principle. Simulation at this biological scale should serve as an invaluable tool to understand the mode of operation of this complex structure. This platform will be updated with new data and expanded to simulate the entire striatum.

Computational models of ion channels represent the building blocks of conductance-based, biologically inspired models of neurons and neural networks. Ion channels are still widely modelled by means of the formalism developed by the seminal work of Hodgkin and Huxley (HH), although the electrophysiological features of the channels are currently known to be better fitted by means of kinetic Markov-type models. The present study is aimed at showing why simplified Markov-type kinetic models are more suitable for ion channels modelling as compared to HH ones, and how a manual optimization process can be rationally carried out for both. Previously published experimental data of an illustrative ion channel (Na(V)1.5) are exploited to develop a step by step optimization of the two models in close comparison. A conflicting practical limitation is recognized for the HH model, which only supplies one parameter to model two distinct electrophysiological behaviours. In addition, a step by step procedure is provided to correctly optimize the kinetic Markov-type model. Simplified Markov-type kinetic models are currently the best option to closely approximate the known complexity of the macroscopic currents of ion channels. Their optimization can be achieved through a rationally guided procedure, and allows to obtain models with a computational burden that is comparable with HH models one.

Striatum, the input stage of the basal ganglia, is important for sensory-motor integration, initiation and selection of behaviour, as well as reward learning. Striatum receives glutamatergic inputs from mainly cortex and thalamus. In rodents, the striatal projection neurons (SPNs), giving rise to the direct and the indirect pathway (dSPNs and iSPNs, respectively), account for 95% of the neurons and the remaining 5% are GABAergic and cholinergic interneurons. Interneuron axon terminals as well as local dSPN and iSPN axon collaterals form an intricate striatal network. Following chronic dopamine depletion as in Parkinson’s disease (PD), both morphological and electrophysiological striatal neuronal features are altered. Our goal with this \textit{in-silico} study is twofold: a) to predict and quantify how the intrastriatal network connectivity structure becomes altered as a consequence of the morphological changes reported at the single neuron level, and b) to investigate how the effective glutamatergic drive to the SPNs would need to be altered to account for the activity level seen in SPNs during PD. In summary we find that the richness of the connectivity motifs is significantly decreased during PD, while at the same time a substantial enhancement of the effective glutamatergic drive to striatum is present.