The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding

• Published in: Interdisciplinary sciences : computational life sciences 2024-03, Vol.16 (1), p.141-159
• Main Authors: Liu, Yanting, Wang, Hao, Ding, Yanrui
• Format: Article
• Language: English
• Subjects: Autism; Autism Spectrum Disorder; Biomarkers; Biomedical and Life Sciences; Brain; Brain - diagnostic imaging; Brain Mapping - methods; Caudate nucleus; Cerebellum; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Deep learning; Diagnosis; Diagnostic systems; Embedding; Graphical representations; Health Sciences; Humans; Life Sciences; Machine learning; Magnetic Resonance Imaging - methods; Mathematical and Computational Physics; Medicine; Neural networks; Neural Pathways; Original Research Article; Postcentral gyrus; Sensory integration; Statistics for Life Sciences; Support vector machines; Theoretical; Theoretical and Computational Chemistry
• ISSN: 1913-2751; 1867-1462
• DOI: 10.1007/s12539-023-00592-w

Autism spectrum disorder (ASD) is a neurological and developmental disorder and its early diagnosis is a challenging task. The dynamic brain network (DBN) offers a wealth of information for the diagnosis and treatment of ASD. Mining the spatio-temporal characteristics of DBN is critical for finding dynamic communication across brain regions and, ultimately, identifying the ASD diagnostic biomarker. We proposed the dgEmbed-KNN and the Aggregation-SVM diagnostic models, which use the spatio-temporal information from DBN and interactive information among brain regions represented by dynamic graph embedding. The classification accuracies show that dgEmbed-KNN model performs slightly better than traditional machine learning and deep learning methods, while the Aggregation-SVM model has a very good capacity to diagnose ASD using aggregation brain network connections as features. We discovered over- and under-connections in ASD at the level of dynamic connections, involving brain regions of the postcentral gyrus, the insula, the cerebellum, the caudate nucleus, and the temporal pole. We also found abnormal dynamic interactions associated with ASD within/between the functional subnetworks, including default mode network, visual network, auditory network and saliency network. These can provide potential DBN biomarkers for ASD identification. Graphical Abstract