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Open-source statistical and data processing tools for wide-field optical imaging data in mice.

Lindsey M Brier1, Joseph P Culver1,2,3,4

  • 1Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States.

Neurophotonics
|March 6, 2023
PubMed
Summary
This summary is machine-generated.

Researchers developed an open-source MATLAB toolbox for processing wide-field optical imaging (WOI) data. This tool enables analysis of hemodynamic and calcium recordings in animal models, aiding disease research.

Keywords:
calcium imagingdata processingoptical imagingwide-field imaging

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Area of Science:

  • Neuroscience
  • Optical Imaging
  • Data Analysis

Background:

  • Wide-field optical imaging (WOI) provides concurrent hemodynamic and cell-specific calcium recordings across the entire cerebral cortex in animal models.
  • WOI is valuable for studying various diseases in mouse models, complementing human functional magnetic resonance imaging (fMRI).
  • A lack of open-source, user-friendly processing and statistical analysis toolboxes for WOI data limits its broader application.

Purpose of the Study:

  • To develop a MATLAB toolbox for processing WOI data.
  • To integrate techniques from multiple WOI research groups and fMRI analysis methods.
  • To provide an open-source, user-friendly platform for WOI data analysis.

Main Methods:

  • Assembled a MATLAB toolbox available on GitHub with data analysis packages.
  • Adapted a statistical approach commonly used in fMRI for WOI data.
  • Demonstrated the toolbox's utility by analyzing a mouse model of stroke and mapping sensory stimulus responses.

Main Results:

  • The processing toolbox and statistical methods identified a somatosensory deficit 3 days post-photothrombotic stroke.
  • The framework successfully localized sensory stimulus-evoked brain activity.
  • The study showcases the ability to detect established deficits and map neural activations.

Conclusions:

  • The developed MATLAB toolbox offers an open-source, user-friendly solution for processing WOI data.
  • It provides essential statistical methods applicable to diverse biological questions investigated using WOI.
  • This resource facilitates more accessible and robust analysis of WOI data in neuroscience research.