Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 7; pp. 2847 - 2861
• Main Authors: Huang, Zhi-An, Zhu, Zexuan, Yau, Chuen Heung, Tan, Kay Chen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Autism; Autism spectrum disorder (ASD); Autism Spectrum Disorder - classification; Autism Spectrum Disorder - diagnostic imaging; Belief networks; Biomarkers; Brain; Brain Mapping; Brain modeling; Brain-Computer Interfaces; Children; Classification; Complexity; computational diagnostic model (CDM); Computational modeling; Computational neuroscience; Computer Simulation; Data exchange; Data management; Data models; Databases, Factual; deep belief network (DBN); Deep Learning; functional connectivity (FC); Functional magnetic resonance imaging; functional magnetic resonance imaging (fMRI); Graph theory; Humans; Magnetic Resonance Imaging - methods; Medical imaging; Neural networks; Neural Networks, Computer; Neuroimaging; Oversampling; Search algorithms; Structure-function relationships

With the increasing prevalence of autism spectrum disorder (ASD), it is important to identify ASD patients for effective treatment and intervention, especially in early childhood. Neuroimaging techniques have been used to characterize the complex biomarkers based on the functional connectivity anoma...