2026-04-17T12:46:34Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/193642024-05-22T09:50:35Zcom_2072_452955com_2072_2054col_2072_452957

Improving the detection of autism spectrum disorder by combining structural and functional MRI information Rakić, Mladen Cabezas Grebol, Mariano Kushibar, Kaisar Oliver i Malagelada, Arnau Lladó Bardera, Xavier Ministerio de Economía y Competitividad (Espanya) Imatges -- Processament Image processing Cervell -- Imatgeria per ressonància magnètica Brain -- Magnetic resonance imaging Imatgeria mèdica Imaging systems in medicine Autisme -- Imatgeria per ressonància magnètica Autism -- Magnetic resonance imaging 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 2020 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion peer-reviewed http://hdl.handle.net/10256/19364 http://hdl.handle.net/10256/19364 eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.nicl.2020.102181 info:eu-repo/semantics/altIdentifier/issn/2213-1582 info:eu-repo/grantAgreement/MINECO//TIN2015-73563-JIN/ES/SEGMENTACION AUTOMATICA DE LAS ESTRUCTURAS CEREBRALES PARA SU USO COMO BIOMARCADORES DE IMAGEN/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86696-R/ES/MODELOS PREDICTIVOS PARA LA ESCLEROSIS MULTIPE USANDO BIOMARCADORES DE RESONANCIA MAGNETICA DEL CEREBRO/ Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess application/pdf Elsevier NeuroImage: Clinical, 2020, vol. 25, art.núm.102181 Articles publicats (D-ATC)