  Deep learning methods are increasingly being used with neuroimaging data like
structural and function magnetic resonance imaging (MRI) to predict the
diagnosis of neuropsychiatric and neurological disorders. For psychiatric
disorders in particular, it is believed that one of the most promising modality
is the resting-state functional MRI (rsfMRI), which captures the intrinsic
connectivity between regions in the brain. Because rsfMRI data points are
inherently high-dimensional (~1M), it is impossible to process the entire input
in its raw form. In this paper, we propose a very simple transformation of the
rsfMRI images that captures all of the temporal dynamics of the signal but
sub-samples its spatial extent. As a result, we use a very simple 1-D
convolutional network which is fast to train, requires minimal preprocessing
and performs at par with the state-of-the-art on the classification of Autism
spectrum disorders.
