Pathological cell assembly dynamics in a striatal MSN network model

TL;DR

This study models how medium spiny neurons in the striatum generate slow, structured activity patterns and how these are disrupted in diseases like Parkinson's.

Contribution

A novel pipeline using a striatal MSN network model and SBI to estimate network parameters from calcium imaging data for disease diagnosis.

Findings

The model generates slow, non-stationary cell assembly activity patterns resembling physiological conditions.

SBI accurately estimated network parameters from healthy data, but not from Parkinsonian or dyskinetic preparations.

The approach could aid in diagnosing basal ganglia pathologies and designing treatments.

Abstract

Under normal conditions the principal cells of the striatum, medium spiny neurons (MSNs), show structured cell assembly activity patterns which alternate sequentially over exceedingly long timescales of many minutes. It is important to understand this activity since it is characteristically disrupted in multiple pathologies, such as Parkinson's disease and dyskinesia, and thought to be caused by alterations in the MSN to MSN lateral inhibitory connections and in the strength and distribution of cortical excitation to MSNs. To understand how these long timescales arise we extended a previous network model of MSN cells to include synapses with short-term plasticity, with parameters taken from a recent detailed striatal connectome study. We first confirmed the presence of sequentially switching cell clusters using the non-linear dimensionality reduction technique, Uniform Manifold Approximation and Projection (UMAP). We found that the network could generate non-stationary activity patterns varying extremely slowly on the order of minutes under biologically realistic conditions. Next we used Simulation Based Inference (SBI) to train a deep net to map features of the MSN network generated cell assembly activity to MSN network parameters. We used the trained SBI model to estimate MSN network parameters from ex-vivo brain slice calcium imaging data. We found that best fit network parameters were very close to their physiologically observed values. On the other hand network parameters estimated from Parkinsonian, decorticated and dyskinetic ex-vivo slice preparations were different. Our work may provide a pipeline for diagnosis of basal ganglia pathology from spiking data as well as for the design pharmacological treatments.

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