  In this paper, we propose a novel unsupervised learning method to learn the
brain dynamics using a deep learning architecture named residual D-net. As it
is often the case in medical research, in contrast to typical deep learning
tasks, the size of the resting-state functional Magnetic Resonance Image
(rs-fMRI) datasets for training is limited. Thus, the available data should be
very efficiently used to learn the complex patterns underneath the brain
connectivity dynamics. To address this issue, we use residual connections to
alleviate the training complexity through recurrent multi-scale representation.
We conduct two classification tasks to differentiate early and late stage Mild
Cognitive Impairment (MCI) from Normal healthy Control (NC) subjects. The
experiments verify that our proposed residual D-net indeed learns the brain
connectivity dynamics, leading to significantly higher classification accuracy
compared to previously published techniques.
