论文标题
自闭症谱系障碍的多个脑脑部切除术中的时空注意力
Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes for Detection of Autism Spectrum Disorder
论文作者
论文摘要
检测自闭症谱系障碍（ASD）的传统方法是昂贵，主观且耗时的，通常需要数年的时间才能进行诊断，许多儿童在青春期甚至成年后都成长良好，然后才能确认该疾病。最近，基于图的学习技术在自闭症脑成像数据交换（ABIDE）的静息状态功能磁共振成像（RS-FMRI）数据上表现出了令人印象深刻的结果。我们介绍了Imagin，这是一种多粒，多ATLAS时空注意图同构网络，我们用它来学习动态功能性脑连接性（Chronnectome）的图形表示，而不是静态连接性（Connectome）。实验结果表明，Imagin的交叉验证精度达到79.25％，超过了当前的最新时间1.5％。此外，对空间和时间注意力评分的分析为自闭症的神经基础提供了进一步的验证。
The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing well into adolescence and even adulthood before finally confirming the disorder. Recently, graph-based learning techniques have demonstrated impressive results on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). We introduce IMAGIN, a multI-granular, Multi-Atlas spatio-temporal attention Graph Isomorphism Network, which we use to learn graph representations of dynamic functional brain connectivity (chronnectome), as opposed to static connectivity (connectome). The experimental results demonstrate that IMAGIN achieves a 5-fold cross-validation accuracy of 79.25%, which surpasses the current state-of-the-art by 1.5%. In addition, analysis of the spatial and temporal attention scores provides further validation for the neural basis of autism.
