Automated Characterization of Autism Spectrum Disorder using Combined Functional and Structural MRI Analysis

• Published in: International journal of advanced computer science & applications Vol. 14; no. 7
• Main Authors: Mezrioui, Nour El Houda, Aloui, Kamel, Nait-Ali, Amine, Naceur, Mohamed Saber
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2023
• Subjects: Artificial neural networks; Autism; Automation; Biomarkers; Disorders; Functional analysis; Magnetic resonance imaging; Medical imaging; Principal components analysis; Social factors; Statistical analysis
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2023.0140775

Autism Spectrum Disorders (ASD) are among the most critical health concerns of our time. These disorders typically present challenges in social interaction, communication, and exhibit repetitive behaviors. To diagnose and customize medical treatments for ASD effectively, the development of robust neuroimaging biomarkers is indispensable. Although extensive studies have recently delved into this area, only a handful have explored the differences between ASD and NC. This study aspires to shed light on this relationship by analyzing both structural and functional brain data associated with ASD. We aim to provide an extensive characterization of ASD by combining techniques of structural and functional analysis. The framework we propose is based on analyzing the differences in structural and functional aspects between ASD and development control (DC) subjects. The study leverages a substantial dataset of 1114 T1-weighted structural and functional Magnetic Resonance Imaging comprising 521 individuals with ASD and 593 controls, ranging in age from 5 to 64 years. These subjects are divided into three broad age categories. Utilizing automated labeling, we compute the features from subcortical and cortical regions. Statistical analyses help identify disparities between ASD and DC subjects. Principal Component Analysis (PCA) is employed to select the most discriminative features, which are subsequently used for classifying the two groups via an Artificial Neural Network (ANN) analysis. Our preliminary findings reveal a significant difference in the distribution of all tested features and subcortical regions between ASD subjects and DC subjects. Through our work, we contribute towards an enhanced understanding of ASD, potentially paving the way for future research and therapeutic interventions.