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bioRxiv · 10.1101/2020.12.03.410761

V-NeuroStack: 3D Time Stacks for Identifying Patterns in Calcium Imaging Data

Naik, A.·Kenyon, R. V.·Taheri, A.·Berger-Wolf, T.·Ibrahim, B.·Llano, D.
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Abstract

BackgroundUnderstanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of cortical neurons in a mouse brain slice. New MethodV-NeuroStack creates 3D time stacks by stacking 2D time frames for a period of 600 seconds. It provides a web interface that enables exploration and analysis of data using a combination of 3D and 2D visualization techniques. Comparison with existing MethodsPrevious attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack can scale data sets with at least a few thousand temporal snapshots. ResultsV-NeuroStacks 3D view is used to explore patterns in the dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of a single neuron or a functional cluster of neurons. ConclusionsV-NeuroStack enables easy exploration and analysis of large spatio-temporal datasets using two visualization paradigms: (a) Space-Time cube (b)Two-dimensional networks, via web interface. It will support future advancements in in vitro and in vivo data capturing techniques and can bring forth novel hypotheses by permitting unambiguous visualization of large-scale patterns in the neuronal activity data.

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Naik, A., Kenyon, R. V., Taheri, A., Berger-Wolf, T., Ibrahim, B., Llano, D.. 2020-12-04. V-NeuroStack: 3D Time Stacks for Identifying Patterns in Calcium Imaging Data. https://doi.org/10.1101/2020.12.03.410761

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