  Neuroimaging data analysis often involves \emph{a-priori} selection of data
features to study the underlying neural activity. Since this could lead to
sub-optimal feature selection and thereby prevent the detection of subtle
patterns in neural activity, data-driven methods have recently gained
popularity for optimizing neuroimaging data analysis pipelines and thereby,
improving our understanding of neural mechanisms. In this context, we developed
a deep convolutional architecture that can identify discriminating patterns in
neuroimaging data and applied it to electroencephalography (EEG) recordings
collected from 25 subjects performing a hand motor task before and after a rest
period or a bout of exercise. The deep network was trained to classify subjects
into exercise and control groups based on differences in their EEG signals.
Subsequently, we developed a novel method termed the cue-combination for Class
Activation Map (ccCAM), which enabled us to identify discriminating
spatio-temporal features within definite frequency bands (23--33 Hz) and assess
the effects of exercise on the brain. Additionally, the proposed architecture
allowed the visualization of the differences in the propagation of underlying
neural activity across the cortex between the two groups, for the first time in
our knowledge. Our results demonstrate the feasibility of using deep network
architectures for neuroimaging analysis in different contexts such as, for the
identification of robust brain biomarkers to better characterize and
potentially treat neurological disorders.
