  Machine learning techniques have become increasingly popular in the field of
resting state fMRI (functional magnetic resonance imaging) network based
classification. However, the application of convolutional networks has been
proposed only very recently and has remained largely unexplored. In this paper
we describe a convolutional neural network architecture for functional
connectome classification called connectome-convolutional neural network
(CCNN). Our results on simulated datasets and a publicly available dataset for
amnestic mild cognitive impairment classification demonstrate that our CCNN
model can efficiently distinguish between subject groups. We also show that the
connectome-convolutional network is capable to combine information from diverse
functional connectivity metrics and that models using a combination of
different connectivity descriptors are able to outperform classifiers using
only one metric. From this flexibility follows that our proposed CCNN model can
be easily adapted to a wide range of connectome based classification or
regression tasks, by varying which connectivity descriptor combinations are
used to train the network.
