Graph Convolutional Neural Networks For Alzheimer's Disease Classification

• Published in: Proceedings (International Symposium on Biomedical Imaging) Vol. 2019; pp. 414 - 417
• Main Authors: Song, Tzu-An, Chowdhury, Samadrita Roy, Yang, Fan, Jacobs, Heidi, Fakhri, Georges El, Li, Quanzheng, Johnson, Keith, Dutta, Joyita
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.04.2019
• Subjects: Alzheimer's disease; classification; Convolution; convolutional neural network; Convolutional neural networks; Diffusion tensor imaging; Graph CNN; Support vector machines; Training; Alzheimer’s disease; Graph CNN; classification; convolutional neural network
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI.2019.8759531

Graph convolutional neural networks (GCNNs) aim to extend the data representation and classification capabilities of convolutional neural networks, which are highly effective for signals defined on regular Euclidean domains, e.g. image and audio signals, to irregular, graph-structured data defined on non-Euclidean domains. Graph-theoretic tools that enable us to study the brain as a complex system are of great significance in brain connectivity studies. Particularly, in the context of Alzheimer's disease (AD), a neurodegenerative disorder associated with network dysfunction, graph-based tools are vital for disease classification and staging. Here, we implement and test a multi-class GCNN classifier for network-based classification of subjects on the AD spectrum into four categories: cognitively normal, early mild cognitive impairment, late mild cognitive impairment, and AD. We train and validate the network using structural connectivity graphs obtained from diffusion tensor imaging data. Using receiver operating characteristic curves, we show that the GCNN classifier outperforms a support vector machine classifier by margins that are reliant on disease category. Our findings indicate that the performance gap between the two methods increases with disease progression from CN to AD. We thus demonstrate that GCNN is a competitive tool for staging and classification of subjects on the AD spectrum.