Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks
Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks
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DOI：
10.1109/bigdata55660.2022.10021070
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发表时间：
2022-12
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影响因子：
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通讯作者：
Tianren Yang;Mai A. Al-Duailij;S. Bozdag;Fahad Saeed
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作者：
Tianren Yang;Mai A. Al-Duailij;S. Bozdag;Fahad Saeed
自闭症谱系障碍（ASD）在美国以及ASD的早期诊断中会影响大量的儿童和成人。共振成像（MRI）数据从具有ASD暴露区别特征的个体收集的数据，这些特征在局部和全局，空间和临时中性因此，大脑的模式可用于各种精神疾病的诊断目的。基于可以利用静止状态fMRI（RS-FMRI）数据的图形卷积网络（GCN），除了使用传统相关性外，还可以从健康控制中对ASD受试者进行分类。 Matrics，我们提出的GCN模型作为训练特征之一，我们的结果表明，图形可以保留从fMRI数据中获得的图形的拓扑信息，这些图形与我们的GCN合并在一起，仅保留了拓扑信息以区分拓扑信息。 ASD和HC的平均准确性为64.27％准确性为75.9％，与其他最先进的方法相媲美，同时可能与更容易解释。
Autism spectrum disorder (ASD) affects large number of children and adults in the US, and worldwide. Early and quick diagnosis of ASD can improve the quality of life significantly both for patients and their families. Prior research provides strong evidence that structural and functional magnetic resonance imaging (MRI) data collected from individuals with ASD exhibit distinguishing characteristics that differ in local and global, spatial and temporal neural patterns of the brain – and therefore can be used for diagnostic purposes for various mental disorders. However, the data from MRI are high-dimensional and advanced methods are needed to make sense out of these datasets. In this paper, we present a novel model based on graph convolutional network (GCN) that can utilize resting state fMRI (rs-fMRI) data to classify ASD subjects from health controls (HC). In addition to using the graph from traditional correlation matrices, our proposed GCN model incorporates graphlet topological counting as one of the training features. Our results show that graphlets can preserve the topological information of the graphs obtained from fMRI data. Combined with our GCN, the graphlets retain enough topological information to differentiate between the ASD and HC. Our proposed model gives an average accuracy of 64.27% on the whole ABIDE-I data sets (1035 subjects) and highest site-specific accuracy of 75.9%, which is comparable to other state-of-the-art methods – while potentially open to being more interpretable.