  As machine learning continues to gain momentum in the neuroscience community,
we witness the emergence of novel applications such as diagnostics,
characterization, and treatment outcome prediction for psychiatric and
neurological disorders, for instance, epilepsy and depression. Systematic
research into these mental disorders increasingly involves drawing clinical
conclusions on the basis of data-driven approaches; to this end, structural and
functional neuroimaging serve as key source modalities. Identification of
informative neuroimaging markers requires establishing a comprehensive
preparation pipeline for data which may be severely corrupted by artifactual
signal fluctuations. In this work, we review a large body of literature to
provide ample evidence for the advantages of pattern recognition approaches in
clinical applications, overview advanced graph-based pattern recognition
approaches, and propose a noise-aware neuroimaging data processing pipeline. To
demonstrate the effectiveness of our approach, we provide results from a pilot
study, which show a significant improvement in classification accuracy,
indicating a promising research direction.
