  We present here a browser-based application for visualizing patterns of
connectivity in 3D stacked data matrices with large numbers of pairwise
relations. Visualizing a connectivity matrix, looking for trends and patterns,
and dynamically manipulating these values is a challenge for scientists from
diverse fields, including neuroscience and genomics. In particular,
high-dimensional neural data include those acquired via electroencephalography
(EEG), electrocorticography (ECoG), magnetoencephalography (MEG), and
functional MRI. Neural connectivity data contains multivariate attributes for
each edge between different brain regions, which motivated our lightweight,
open source, easy-to-use visualization tool for the exploration of these
connectivity matrices to highlight connections of interest. Here we present a
client-side, mobile-compatible visualization tool written entirely in
HTML5/JavaScript that allows in-browser manipulation of user-defined files for
exploration of brain connectivity. Visualizations can highlight different
aspects of the data simultaneously across different dimensions. Input files are
in JSON format, and custom Python scripts have been written to parse MATLAB or
Python data files into JSON-loadable format. We demonstrate the analysis of
connectivity data acquired via human ECoG recordings as a domain-specific
implementation of our application. We envision applications for this
interactive tool in fields seeking to visualize pairwise connectivity.
