Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks

自闭症谱系障碍 功能磁共振成像 图形 自闭症 计算机科学 卷积神经网络 人工智能 静息状态功能磁共振成像 模式识别(心理学) 相关性 图论 机器学习 心理学 数学 神经科学 理论计算机科学 精神科 几何学 组合数学
作者
Tianren Yang,Mai Alduailij,Serdar Bozdag,Fahad Saeed
标识
DOI:10.1109/bigdata55660.2022.10021070
摘要

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.
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