Interactive Web Application for Exploring Matrices of Neural Connectivity
David J. Caldwell, Jing Wu, Kaitlyn Casimo, Jeffrey G. Ojemann, Rajesh, P.N. Rao

TL;DR
This paper introduces a lightweight, browser-based visualization tool for exploring high-dimensional neural connectivity matrices, enabling scientists to interactively analyze complex brain data across various modalities.
Contribution
The authors developed an open-source, client-side web application that visualizes neural connectivity matrices in 3D, supporting dynamic manipulation and multi-dimensional data highlighting.
Findings
Successfully visualized ECoG connectivity data in-browser
Supports large matrices with interactive features
Eases exploration of neural connectivity patterns
Abstract
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…
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