3-D functional brain network classification using Convolutional Neural Networks

Several recent studies have shown that dictionary learning and sparse representation can effectively reconstruct hundreds of interacting functional brain networks simultaneously from whole-brain fMRI data. However, accurate classification and recognition of those hundreds of functional networks from an individual or a population of many subjects is still a challenging and open problem due to the intrinsic variability of functional networks and other noise sources. To tackle this problem, this paper presents an effective deep learning framework to train convolutional neural networks from a large dataset of hundreds of thousands of available brain network volume maps, which was then applied on testing samples for network classification and recognition. We effectively applied computer-labeled data as training set so the whole process can be automated. Experimental results showed that the proposed method is quite robust in handling noisy patterns in the dataset, which suggests that our work offers a new computational framework for modeling functional connectomes from fMRI big data in the future.