  While the prevalence of Autism Spectrum Disorder (ASD) is increasing,
research continues in an effort to identify common etiological and
pathophysiological bases. In this regard, modern machine learning and network
science pave the way for a better understanding of the neuropathology and the
development of diagnosis aid systems. The present work addresses the
classification of neurotypical and ASD subjects by combining knowledge about
both the structure and the functional activity of the brain. In particular, we
model the brain structure as a graph, and the resting-state functional MRI
(rs-fMRI) signals as values that live on the nodes of that graph. We then
borrow tools from the emerging field of Graph Signal Processing (GSP) to build
features related to the frequency content of these signals. In order to make
these features highly discriminative, we apply an extension of the
Fukunaga-Koontz transform. Finally, we use these new markers to train a
decision tree, an interpretable classification scheme, which results in a final
diagnosis aid model. Interestingly, the resulting decision tree outperforms
state-of-the-art methods on the publicly available Autism Brain Imaging Data
Exchange (ABIDE) collection. Moreover, the analysis of the predictive markers
reveals the influence of the frontal and temporal lobes in the diagnosis of the
disorder, which is in line with previous findings in the literature of
neuroscience. Our results indicate that exploiting jointly structural and
functional information of the brain can reveal important information about the
complexity of the neuropathology.

Image and Video Processing;Machine Learning;Machine Learning