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Pathological cell assembly dynamics in a striatal MSN network model.

Astrid Correa1, Adam Ponzi1,2, Vladimir M Calderón3

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|June 21, 2024
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Summary
This summary is machine-generated.

Researchers modeled medium spiny neuron (MSN) network activity, revealing how slow, sequential cell assembly patterns emerge. This model aids in understanding and diagnosing basal ganglia disorders like Parkinson's disease.

Keywords:
Parkinson's diseaseSimulation Based Inferencebasal gangliacalcium imagecell assemblynetwork modelpathologystriatum

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Neuropharmacology

Background:

  • Medium spiny neurons (MSNs) in the striatum exhibit slow, sequential cell assembly activity patterns over minutes.
  • This activity is disrupted in pathologies like Parkinson's disease and dyskinesia, linked to altered MSN connectivity and cortical excitation.
  • Understanding the mechanisms behind these long timescales is crucial for neurological research.

Purpose of the Study:

  • To investigate the emergence of long timescales in MSN network activity.
  • To develop a computational model that replicates observed MSN network dynamics.
  • To establish a method for diagnosing basal ganglia pathologies using neural activity data.

Main Methods:

  • Extended a previous MSN network model to incorporate short-term synaptic plasticity.
  • Utilized Uniform Manifold Approximation and Projection (UMAP) for analyzing cell assembly activity.
  • Employed Simulation Based Inference (SBI) to train a deep network for mapping activity features to network parameters.

Main Results:

  • The model successfully generated non-stationary activity patterns with slow timescales (minutes) under realistic conditions.
  • SBI accurately estimated MSN network parameters from simulated and experimental ex-vivo calcium imaging data.
  • Network parameters differed significantly in preparations from Parkinsonian, decorticated, and dyskinetic models compared to controls.

Conclusions:

  • The study provides a computational framework for understanding slow dynamics in striatal networks.
  • The developed pipeline shows potential for diagnosing basal ganglia disorders from neural spiking data.
  • This approach could inform the design of targeted pharmacological treatments for neurological conditions.