This package shares code for web-based visualization of brain connectivity
data, built using D3.js, a Javascript library for
interactive documents. This should potentially work with any data that can be
stored in a connectivity matrix or graph, that is, numerical measures defined
for pairs of variables. For example, this could be measures of structural
integrity between brain regions, or it could be something else, like functional
connectivity or statistical parameters from a group analysis. Two different
visualization approaches included here and illustrated above. The first is a
chord diagram that visualizes brain regions on a circle with interior chords
that connect regions with coloring to denote connectivity strength. The second
is a matrix diagram that supports richer interaction, including querying
individual values, adjusting colormaps, and including several
parameters-of-interest. The chord diagram is ideal for sparse graphs, while
the matrix is more useful for denser graphs.
The code only requires a web-server, and you can find live examples running here:
The provided diagrams present data from a diffusion MRI study of normal aging adults. You can learn more about the data from the repository website and you can request access through GAAIN at the corresponding GAAIN partners page. To use your own data, you simply have to create JSON files that match the structure of the example.
If you find this useful in your research, we kindly ask that you cite the following abstract, which you can find included in the media directory:
Cabeen, R.P., Bastin, M.E. and Laidlaw, D.H., 2013. A
diffusion MRI resource of 80 age-varied subjects with
neuropsychological and demographic measures. In ISMRM,
21st Scientific Meeting and Exhibition (No. 2138).
@inproceedings{cabeen2013diffusion,
title={A diffusion MRI resource of 80 age-varied subjects
with neuropsychological and demographic measures},
author={Cabeen, Ryan P and Bastin, ME and Laidlaw, DH},
booktitle={ISMRM, 21st Scientific Meeting and Exhibition},
number={2138},
year={2013}
}
Author: Ryan Cabeen, cabeen@gmail.com
Data collection and analysis for this project was supported by NIH grant number R01 EB004155. The dissemintation of this work is supported by the CZI Imaging Scientist Award Program, under grant number 2020-225670 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation.