School of Software, Henan University, Kaifeng 475004, People's Republic of China.
Institute for Data Engineering and Science, University of Saint Joseph, Macau 999078, People's Republic of China.
Phys Med Biol. 2024 Nov 19;69(23). doi: 10.1088/1361-6560/ad8c94.
Mild cognitive impairment (MCI) is a precursor stage of dementia characterized by mild cognitive decline in one or more cognitive domains, without meeting the criteria for dementia. MCI is considered a prodromal form of Alzheimer's disease (AD). Early identification of MCI is crucial for both intervention and prevention of AD. To accurately identify MCI, a novel multimodal 3D imaging data integration graph convolutional network (GCN) model is designed in this paper.The proposed model utilizes 3D-VGGNet to extract three-dimensional features from multimodal imaging data (such as structural magnetic resonance imaging and fluorodeoxyglucose positron emission tomography), which are then fused into feature vectors as the node features of a population graph. Non-imaging features of participants are combined with the multimodal imaging data to construct a population sparse graph. Additionally, in order to optimize the connectivity of the graph, we employed the pairwise attribute estimation （PAE） method to compute the edge weights based on non-imaging data, thereby enhancing the effectiveness of the graph structure. Subsequently, a population-based GCN integrates the structural and functional features of different modal images into the features of each participant for MCI classification.Experiments on the AD Neuroimaging Initiative demonstrated accuracies of 98.57%, 96.03%, and 96.83% for the normal controls (NC)-early MCI (EMCI), NC-late MCI (LMCI), and EMCI-LMCI classification tasks, respectively. The AUC, specificity, sensitivity, and F1-score are also superior to state-of-the-art models, demonstrating the effectiveness of the proposed model. Furthermore, the proposed model is applied to the ABIDE dataset for autism diagnosis, achieving an accuracy of 91.43% and outperforming the state-of-the-art models, indicating excellent generalization capabilities of the proposed model.This study demonstratethe proposed model's ability to integrate multimodal imaging data and its excellent ability to recognize MCI. This will help achieve early warning for AD and intelligent diagnosis of other brain neurodegenerative diseases.
轻度认知障碍 (MCI) 是痴呆症的前驱阶段，其特征是一个或多个认知领域的轻度认知下降，不符合痴呆症的标准。MCI 被认为是阿尔茨海默病 (AD) 的前驱形式。早期识别 MCI 对于 AD 的干预和预防都至关重要。为了准确识别 MCI，本文设计了一种新的多模态 3D 成像数据集成图卷积网络 (GCN) 模型。所提出的模型利用 3D-VGGNet 从多模态成像数据（如结构磁共振成像和氟代脱氧葡萄糖正电子发射断层扫描）中提取三维特征，然后将这些特征融合为特征向量作为群体图的节点特征。参与者的非成像特征与多模态成像数据相结合，构建群体稀疏图。此外，为了优化图的连接性，我们采用了成对属性估计 (PAE) 方法，根据非成像数据计算边权重，从而增强图结构的有效性。随后，基于群体的 GCN 将不同模态图像的结构和功能特征集成到每个参与者的特征中，用于 MCI 分类。在 AD 神经影像学倡议上的实验结果表明，对于正常对照组（NC）-早期 MCI（EMCI）、NC-晚期 MCI（LMCI）和 EMCI-LMCI 分类任务，该模型的准确率分别为 98.57%、96.03%和 96.83%。AUC、特异性、敏感性和 F1 分数也优于最先进的模型，证明了所提出模型的有效性。此外，该模型还应用于 ABIDE 数据集进行自闭症诊断，准确率达到 91.43%，优于最先进的模型，表明所提出模型具有出色的泛化能力。这项研究表明，所提出的模型能够整合多模态成像数据，并且具有出色的识别 MCI 的能力。这将有助于实现 AD 的早期预警和其他脑神经退行性疾病的智能诊断。
