Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects

• Published in: Journal of autism and developmental disorders Vol. 54; no. 7; pp. 2757 - 2768
• Main Author: Saha, Papri
• Format: Journal Article
• Language: English
• Published: New York Springer 01.07.2024; Springer Nature B.V
• Subjects: Algorithms; Autism; Autism Spectrum Disorders; Brain; Brain research; Budding; Classification; Control Groups; Database Management Systems; Diagnosis; Disease; Eigenvectors; Epilepsy; Females; Formalism; Functional magnetic resonance imaging; Gender differences; Genetic Disorders; Graph theory; Individualized Instruction; Literary Devices; Machine learning; Magnetic resonance imaging; Males; Mental Disorders; Methods; Network centrality; Networks; Neurobiology; Neuroimaging; Periodicals; Pervasive developmental disorders; Statistical analysis; Structure-function relationships; Suitability; Visual perception; India
• ISSN: 0162-3257; 1573-3432
• DOI: 10.1007/s10803-023-05922-x

With the budding interests of structural and functional network characteristics as potential parameters for abnormal brains, an essential and thus simpler representation and evaluations have become necessary. Eigenvector centrality measure of functional magnetic resonance imaging (fMRI) offer region wise network representations through fMRI diagnostic maps. The article investigates the suitability of network node centrality values to discriminate ASD subject groups compared to typically developing controls following a boxplot formalism and a classification and regression tree model. Region wise differences between normal and ASD subjects primarily belong to the frontoparietal, limbic, ventral attention, default mode and visual networks. The reduced number of regions-of-interests (ROI) clearly suggests the benefit of automated supervised machine learning algorithm over the manual classification method.