Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting the difference of features between regions in healthy control (HC), mild cognitive impairment (MCI), and AD groups. We conducted the experiments on the ADNI database with imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET, and showed that the ROI features learned by our method were effective for enhancing the performances of both clinical score prediction and disease status identification. It also successfully identified biomarkers associated with AD and MCI.
翻译:确定与特定神经系统紊乱有关的大脑区域对于生物标志和诊断研究非常重要。在本文件中,我们提出一个可解释的图表革命网络框架,以便利用多式脑成像数据识别和分类阿尔茨海默氏病(AD),具体地说,我们推广了渐进级激活绘图技术,以量化GCN从大脑连接模式中发现的最具歧视性特征。然后,我们利用这些技术通过发现健康控制(HC)、轻度认知障碍(MCI)和AD团体之间特征的差异,找到值得注意的特征区域(ROIs)。我们利用来自三种模式的成像数据,包括VBM-MRI、FDG-PET和AV45-PET,在ADNI数据库中进行了实验,并表明我们从方法中学习的ROI特征对于提高临床评分预测和疾病状况识别的性能十分有效。它还成功地确定了与AD和MCI有关的生物标志。