Song Tzu-An, Roy Chowdhury Samadrita, Yang Fan, Jacobs Heidi, El Fakhri Georges, Li Quanzheng, Johnson Keith, Dutta Joyita
Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA.
Gordon Center for Medical Imaging, Massachusetts General Hospital & Harvard Medical School, Boston, MA.
Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:414-417. doi: 10.1109/ISBI.2019.8759531. Epub 2019 Jul 11.
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.
图卷积神经网络（GCNNs）旨在将卷积神经网络的数据表示和分类能力扩展到非欧几里得域上定义的不规则图结构数据，卷积神经网络对定义在规则欧几里得域上的信号（如图像和音频信号）非常有效。使我们能够将大脑作为一个复杂系统进行研究的图论工具在脑连接性研究中具有重要意义。特别是在阿尔茨海默病（AD）的背景下，AD是一种与网络功能障碍相关的神经退行性疾病，基于图的工具对于疾病分类和分期至关重要。在此，我们实现并测试了一种多类GCNN分类器，用于将AD谱系中的受试者基于网络分类为四类：认知正常、早期轻度认知障碍、晚期轻度认知障碍和AD。我们使用从扩散张量成像数据获得的结构连接图来训练和验证网络。使用受试者工作特征曲线，我们表明GCNN分类器在依赖疾病类别的边际上优于支持向量机分类器。我们的研究结果表明，两种方法之间的性能差距随着疾病从CN发展到AD而增加。因此，我们证明GCNN是一种用于AD谱系受试者分期和分类的有竞争力的工具。
