Study subnetwork developing pattern of autism children by non-negative matrix factorization

• Published in: Computers in biology and medicine Vol. 158; p. 106816
• Main Authors: Zheng, JinLin, Shao, LiCheng, Yan, Zheng, Lai, XiaoFei, Duan, Fang
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2023; Elsevier Limited
• Subjects: Algorithms; Autism; Autistic Disorder; Brain; Brain network; Child; Children; Cognitive ability; Decomposition; Developmental disabilities; Entropy; Factorization; Hemispheric laterality; Humans; Magnetic Resonance Imaging; Magnetoencephalography; Magnetoencephalography - methods; Network developing; Networks; Neurodevelopmental disorders; NMF; Subnetwork; Autism; Network developing; Subnetwork; Brain network; NMF
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.106816

As a developmental disorder, the brain networks of autism children show abnormal patterns compared with that of typically developing. The differences between them are not stable due to the developing progress of children. It has become a choice to study the differences of developing trajectories between autistic and typically developing children by investigating the change of each group respectively. Related researches studied the developing of brain network by analyzing the relationship between network indices of the entire or sub brain networks and the cognitive developing scores. As a matrix decomposition algorithm, non-negative matrix factorization (NMF) was applied to decompose the association matrices of brain networks. By NMF, we can obtain subnetworks in an unsupervised way. The association matrices of autism and control children were estimated by their magnetoencephalography data. NMF was applied to decompose the matrices to obtain common subnetworks of both groups. Then we calculated the expression of each subnetwork in each child’s brain network by two indices, energy and entropy. The relationship between the expression and the cognitive and development indices were investigated. We found a subnetwork with left lateralization pattern in α band showed different expression tendency in two groups. The expression indices of two groups were correlated with cognitive indices in autism and control group in an opposite way. In γ band, a subnetwork with strong connections on right hemisphere of brain showed a negative correlation between the expression indices and development indices in autism group. NMF algorithm can effectively decompose brain network to meaningful subnetworks. The finding of α band subnetworks confirms the results of abnormal lateralization of autistic children mentioned in relevant studies. We assume the results of decrease of expression of the subnetwork may relate to the dysfunction of mirror neuron. The decrease expression of γ subnetwork of autism may be related to the weaken process of high-frequency neurons in the neurotrophic competition. •Non-negative matrix factorization (NMF) algorithm is applied to identify abnormal connectivity of ASD brains.•The K-ABC assessment provides additional insight into the search for abnormal brain networks in ASD brains.•The brain lateralization patterns showed up in α and γ bands play pivotal roles in neurodevelopment of autistic spectrum disorder (ASD).