Last updated on May 31st, 2017
We had our work, BrainNetCNN, published in NeuroImage awhile ago,
Kawahara, J., Brown, C. J., Miller, S. P., Booth, B. G., Chau, V., Grunau, R. E., Zwicker, J., G., Hamarneh, G. (2017). BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage, 146(Feb), 1038–1049. http://doi.org/10.1016/j.neuroimage.2016.09.046
and I’ve meant to do a blog writeup about this. We recently released our code for BrainNetCNN on GitHub (based on Caffe), which implements the proposed filters designed for adjacency matrices.
We called this library Ann4Brains. In hindsight, we could have called this something more general and cumbersome like Ann4AdjacencyMatrcies, but I still like the zombie feel that Ann4Brains has.
We designed BrainNetCNN specifically with brain connectome data in mind. Thus the tag line of,
“Convolutional Neural Networks for Brain Networks”
seemed appropriate. However, after receiving some emails about using BrainNetCNN for other types of (non-connectome) data, I’ll emphasize that this approach can be applied to any sort of adjacency matrix, and not just brain connectomes.
The core contribution of this work is the filters designed for adjacency matrices themselves. So we’ll go through each of them. But first, let’s make sure we are clear on what the brain connectome (or adjacency matrix) is.