Understanding functional correlations between the activities of neuron populations are 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 neurons in a mouse brain slice as well as in vivo using two-photon imaging. V-NeuroStack creates 3D time stacks by stacking 2D time frames for a time-series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack’s 3D view is used to explore patterns in 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 activity from an individual or a functional cluster of neurons. V-NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data-capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data. See https://onlinelibrary.wiley.com/doi/10.1002/jnr.25139

Keywords: 3D visualization, calcium imaging, dynamic network, neural network, spatio-temporal data

PDF

URL

Naik, A., Kenyon, R.V., Taheril, A., BergerWolf, T.Y., Ibrahim, B.A., Shinagawa, Y., Llano, D.A., V-NeuroStack: Open-source 3D time stack software for identifying patterns in neuronal data, Journal of Nueroscience Research, October 12th, 2022. https://doi.org/10.1002/jnr.25139