Classification of Autism Spectrum Disorder Through the Graph Fourier Transform of fMRI Temporal Signals Projected on Structural Connectome

• Published in: Computer Analysis of Images and Patterns Vol. 1089; pp. 45 - 55
• Main Authors: Brahim, Abdelbasset, Hajjam El Hassani, Mehdi, Farrugia, Nicolas
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Autism disorder; Graph signal processing; Machine learning; Resting-state analysis
• ISBN: 9783030299293; 3030299295
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-030-29930-9_5

Graph Fourier Transform (GFT) could be a key tool for analyzing brain signals. In this sense, we evaluate the application of Graph signal processing (GSP) for the analysis of neuroimaging data. Thus, a GSP-based approach is proposed and validated for the classification of autism spectrum disorder (ASD). More specifically, the resting state functional magnetic resonance imaging (rs-fMRI) time series of each brain subject are characterized by several statistical metrics. Then, these measures are projected on a structural graph, which is computed from a healthy brain structural connectivity of the human connectome project. Further analysis proves that the combination of the structural connectivity with the standard deviation of fMRI temporal signals can lead to more accurate supervised classification for 172 subjects from the biggest site of the Autism Brain Imaging Data Exchange (ABIDE) datasets. Moreover, the proposed approach outperforms several approaches, based on using functional connectome or complex functional network measures.