基于图同构网络的自闭症功能磁共振影像诊断算法

Graph representations are usually used to model and analyze structured data on an individual or population level，and are successfully applied in network analysis，traffic forecasting，recommendation systems and other fields. With the development of imaging equipment，learning connectivity characteristics of brain from neuroimaging data has received widespread attention for brain disorders diagnosis (such as Autism Spectrum Disorder (ASD)，Alzheimer's Disease，etc.) based on brain network. Graph representations are able to model the structural or functional connections between a group of brain regions，and to reveal patterns related to brain development and disease. However，evaluating the similarity between theses brain connectivity networks is non⁃trivial. Traditional deep learning methods cannot adapt to graph structures and discard some useful information for graph classification tasks. Therefore，we propose a model based on graph isomorphic network (GIN) to ASD diagnosis using fMRI. This model contains four isomorphic layers，each of which obtains the feature representation of the brain functional connectivity network through spatial⁃based convolutions. To account for the medical meaning of nodes in the brain network，the node features are transformed into graph features by a flatten layer. Compared with Graph Convolutional Network (GCN) and Deep Neural Network (DNN)，the experimental results on ABIDE database show that our proposed method is effective and significantly improves the accuracy of ASD diagnosis.

Keywords： Autism Spectrum Disorder (ASD) ; brain functional connectivity ; Graph Isomorphism Network (GIN) ; fMRI