نبذة مختصرة : Autism Spectrum Disorder (ASD) is a brain disorder that is typically characterized by deficits in social communication and interaction, as well as restrictive and repetitive behaviors and interests. During the last years, there has been an increase in the use of magnetic resonance imaging (MRI) to help in the detection of common patterns in autism subjects versus typical controls for classification purposes. In this work, we propose a method for the classification of ASD patients versus control subjects using both functional and structural MRI information. Functional connectivity patterns among brain regions, together with volumetric correspondences of gray matter volumes among cortical parcels are used as features for functional and structural processing pipelines, respectively. The classification network is a combination of stacked autoencoders trained in an unsupervised manner and multilayer perceptrons trained in a supervised manner. Quantitative analysis is performed on 817 cases from the multisite international Autism Brain Imaging Data Exchange I (ABIDE I) dataset, consisting of 368 ASD patients and 449 control subjects and obtaining a classification accuracy of 85.06 ± 3.52% when using an ensemble of classifiers. Merging information from functional and structural sources significantly outperforms the implemented individual pipelines ; This work has been supported by Retos de Investigacin TIN2015- 73563-JIN and DPI2017-86696-R from the Ministerio de Ciencia y Tecnología. Mladen Rakić holds an EACEA Erasmus+ grant for the master in Medical Imaging and Applications (MAIA), Kaisar Kushibar holds a FI-DGR2017 grant from the Catalan Government with reference number 2017FI_B00372, and Mariano Cabezas holds a Juan de la Cierva - Incorporación grant from the Spanish Government with reference number IJCI-2016-29240. The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN-V GPU used in this research
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