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Functional Calcium Imaging in Developing Cortical Networks
Published on: October 22, 2011
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Functional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type.
D O'Connor1, F Mandino2, X Shen2
1Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Neuroimage
|November 8, 2022
Summary
We developed a new data analysis pipeline for mesoscale imaging in animal models to advance translational neuroscience. This open-source tool reveals how brain states and cell types impact neural network connectivity.
Area of Science:
- Neuroscience
- Translational Research
- Biomedical Imaging
Background:
- Improving 'bench-to-bedside' translation requires bidirectional knowledge flow between animal models and humans.
- Common analytical frameworks and open data sharing are crucial for this bidirectional flow.
- Wide-field optical fluorescence imaging is an emerging technique for animal model research.
Purpose of the Study:
- To introduce a novel pipeline for preprocessing wide-field optical fluorescence imaging data from animal models.
- To apply functional connectivity and graph theory analyses, inspired by human neuroimaging, to this data.
- To facilitate translational neuroscience by enabling examination of brain state and cell-type differences.
Main Methods:
- Developed and shared an open-source pipeline for wide-field optical fluorescence imaging data preprocessing.
- Utilized seed-based connectivity and graph theory measures (global efficiency, transitivity, modularity, characteristic path-length).
- Analyzed data from two test cases: awake vs. anesthetized conditions and different genetically encoded fluorescent labels targeting neuronal subtypes.
Main Results:
- Demonstrated the utility of the pipeline in quantifying differences between wakefulness states and cell populations.
- Observed widespread effects of wakefulness state and cell type on canonical network connectivity.
- Identified distinct network properties for somatostatin-expressing inhibitory interneurons compared to parvalbumin and vasoactive polypeptide expressing cells.
Conclusions:
- The developed pipeline effectively analyzes mesoscale imaging data to reveal brain state and cell-type specific network differences.
- This approach supports translational neuroscience by providing tools for comparative analysis between species.
- The freely released pipeline and data encourage community adoption and further research in open science practices.
