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Imaging Calcium Dynamics in Subpopulations of Mouse Pancreatic Islet Cells
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Spike Rate Inference from Mouse Spinal Cord Calcium Imaging Data.

Peter Rupprecht1,2, Wei Fan3, Steve J Sullivan3

  • 1Laboratory of Neural Circuit Dynamics, Brain Research Institute, University of Zurich, Zurich CH-8057, Switzerland rupprecht@hifo.uzh.ch sdrulla@ohsu.edu.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|March 24, 2025
PubMed
Summary
This summary is machine-generated.

Spike inference algorithms for calcium imaging generalize well to spinal cord neurons. Re-training models with ground truth data from spinal cord neurons (glutamatergic and GABAergic) further improved spike rate accuracy.

Keywords:
calcium imagingcell-attacheddeconvolutiondeep learningspike inferencespinal cord

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Calcium Imaging

Background:

  • Calcium imaging is a key technique for monitoring neuronal activity, but requires algorithms to infer spiking activity from fluorescence signals.
  • Existing spike inference algorithms are often optimized for cortical neurons, with limited validation in other brain regions like the spinal cord.
  • Ground truth data from simultaneous electrophysiology and calcium imaging is crucial for optimizing and validating these algorithms for new cell types and regions.

Purpose of the Study:

  • To evaluate the performance of established spike inference algorithms (CASCADE and OASIS) in spinal cord neurons.
  • To generate novel ground truth datasets from mouse spinal cord dorsal horn neurons (glutamatergic and GABAergic).
  • To develop and provide re-trained models for accurate spike rate inference in spinal cord calcium imaging data.

Main Methods:

  • Simultaneous electrophysiological and calcium imaging recordings were performed in identified glutamatergic and GABAergic somatosensory neurons in the mouse spinal cord dorsal horn.
  • State-of-the-art supervised (CASCADE) and non-supervised (OASIS) spike inference algorithms were applied to the recorded data.
  • CASCADE models were re-trained using the newly acquired spinal cord ground truth data.

Main Results:

  • Both CASCADE and OASIS algorithms showed good generalization performance for inferring spike rates in spinal cord neurons, despite being developed for cortical neurons.
  • Re-training CASCADE models with spinal cord specific ground truth data significantly improved the accuracy of spike rate inference.
  • The study provides open-access re-trained models applicable to spinal cord calcium imaging data with varying noise and frame rates.

Conclusions:

  • Spike inference algorithms can generalize across different neuronal populations and brain regions, but regional optimization enhances accuracy.
  • This study establishes a foundation for precise interpretation of calcium imaging data from the spinal cord dorsal horn.
  • The developed and validated methods facilitate broader application of calcium imaging in spinal cord research.