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

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yanting Liu, Hao Wang, Yanrui Ding
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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.

Abstract Image

基于动态图嵌入的自闭症谱系障碍功能连通性动态生物标志物研究。
自闭症谱系障碍(ASD)是一种神经和发育障碍,其早期诊断是一项具有挑战性的任务。动态脑网络(DBN)为ASD的诊断和治疗提供了丰富的信息。挖掘DBN的时空特征对于发现大脑区域之间的动态交流并最终确定ASD诊断生物标志物至关重要。我们提出了dgEmbed-KNN和Aggregation-SVM诊断模型,它们利用DBN的时空信息和动态图嵌入表示的脑区间交互信息。分类精度表明,dgEmbed-KNN模型的分类精度略优于传统的机器学习和深度学习方法,而aggregation - svm模型以聚集脑网络连接为特征诊断ASD的能力非常好。我们在动态连接的水平上发现了ASD的过度连接和欠连接,涉及中央后回,岛,小脑,尾状核和颞极的大脑区域。我们还发现与ASD相关的功能子网络内部/之间的异常动态相互作用,包括默认模式网络、视觉网络、听觉网络和显著性网络。这些可以为ASD鉴定提供潜在的DBN生物标志物。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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