Interactive Web Application for Visualization of Brain Connectivity
David Caldwell, Jing Wu, Kaitlyn Casimo, Jeffrey Ojemann, Rajesh P. N. Rao
We present here a novel, lightweight, web-based application for visualizing patterns of connectivity between 3D stacked data matrices with large numbers of pairwise relations. Visualizing a connectivity matrix, looking for trends and patterns of interest, and subsequently dynamically manipulating these values is a challenge for scientists from diverse fields, including neuroscience and genomics. Neuroscience data sets with high-dimensional connectivity data include those acquired via EEG, electrocorticography, magnetoencephalography, and fMRI. We demonstrate the analysis of connectivity data acquired via human electrocorticography recordings as a domain-specific implementation of our application. Neural connectivity data often exists in a high-dimensional space, with multivariate attributes for each edge between different brain regions, which motivates a lightweight, open-source, easy to use visualization tool to allow for the rapid exploration of these connectivity matrices to highlight connections of interest. Here we present a client-side, mobile-compatible visualization tool written entirely in HTML/CSS/JS that allows for in-browser manipulation of user-defined files for the exploration of brain connectivity. Visualizations can highlight different aspects of the data simultaneously across differing dimensions, allowing for rapid exploration and analysis of the data. Input files are in JSON format, and custom Python scripts have been written to allow for the parsing of MATLAB or Python data files into JSON-loadable format. We envision applications for this interactive tool in fields ranging from neuroscience to genomics to any other field seeking to visualize pairwise connectivity.