Graph Convolutional Neural Networks For Alzheimer's Disease Classification

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 o...

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Bibliographic Details
Published in:2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) 2019-04, Vol.2019, p.414-417
Main Authors: Song, Tzu-An, Chowdhury, Samadrita Roy, Yang, Fan, Jacobs, Heidi, Fakhri, Georges El, Li, Quanzheng, Johnson, Keith, Dutta, Joyita
Format: Article
Language:English
Subjects:
Alzheimer's disease
classification
Convolution
convolutional neural network
Convolutional neural networks
Diffusion tensor imaging
Graph CNN
Support vector machines
Training
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Summary: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.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2019.8759531