  Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for
studying brain activity. During an fMRI session, the subject executes a set of
tasks (task-related fMRI study) or no tasks (resting-state fMRI), and a
sequence of 3-D brain images is obtained for further analysis. In the course of
fMRI, some sources of activation are caused by noise and artifacts. The removal
of these sources is essential before the analysis of the brain activations.
Deep Neural Network (DNN) architectures can be used for denoising and artifact
removal. The main advantage of DNN models is the automatic learning of abstract
and meaningful features, given the raw data. This work presents advanced DNN
architectures for noise and artifact classification, using both spatial and
temporal information in resting-state fMRI sessions. The highest performance is
achieved by a voting schema using information from all the domains, with an
average accuracy of over 98% and a very good balance between the metrics of
sensitivity and specificity (98.5% and 97.5% respectively).
