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

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Abstract

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.

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Data availability

The data that support the findings of this study are openly available in ABIDE online repository (ref. [24]).

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Acknowledgements

We thank Professor Li Liu from the Big Data Center of Affiliated Hospital of Jiangnan University for her support and assistance in data collection, preprocessing, and analysis of potential DBN biomarkers identified in this study. This work is supported in part by the National Natural Science Foundation of China under Grant 62161050, and in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX22_2419.

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  1. School of Science, Jiangnan University, Wuxi, 214122, China

    Yanting Liu, Hao Wang & Yanrui Ding

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Correspondence to Yanrui Ding.

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Liu, Y., Wang, H. & Ding, Y. The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding. Interdiscip Sci Comput Life Sci 16, 141–159 (2024). https://doi.org/10.1007/s12539-023-00592-w

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