School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
Department of Nuclear Medicine, the Second Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang, China.
Alzheimers Res Ther. 2024 Mar 14;16(1):60. doi: 10.1186/s13195-024-01425-8.
Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.
This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan-Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.
The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.
This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.
功能连接（FC）生物标志物在阿尔茨海默病（AD）的早期诊断和机制研究中起着至关重要的作用。然而，识别有效的 FC 生物标志物仍然具有挑战性。在这项研究中，我们引入了一种新的方法，即时空图卷积网络（ST-GCN）与基于梯度的类激活映射（Grad-CAM）模型（STGC-GCAM）相结合，以有效地识别 AD 的 FC 生物标志物。
这是一项多中心跨种族回顾性研究，涉及 2272 名参与者，包括 1105 名认知正常（CN）受试者、790 名轻度认知障碍（MCI）个体和 377 名 AD 患者。所有参与者均接受了功能磁共振成像（fMRI）和 T1 加权磁共振成像（MRI）扫描。在这项研究中，首先，我们优化了 STGC-GCAM 模型以提高分类准确性。其次，我们使用优化后的模型识别新的 AD 相关生物标志物。第三，我们使用 Kaplan-Meier 分析验证了成像生物标志物。最后，我们进行了相关性分析和因果中介分析，以确认所识别生物标志物的生理意义。
STGC-GCAM 模型表现出出色的分类性能（不同类别下的平均曲线下面积（AUC）值分别为：CN 与 MCI = 0.98，CN 与 AD = 0.95，MCI 与 AD = 0.96，稳定 MCI 与进展性 MCI = 0.79）。值得注意的是，该模型识别出了一些关键的大脑区域，包括感觉运动网络（SMN）、视觉网络（VN）和默认模式网络（DMN），这些区域在患者与 CN 个体之间存在显著差异。这些大脑区域与认知障碍的严重程度显著相关（p < 0.05）。此外，重要大脑区域的拓扑特征表现出对从 MCI 向 AD 转化的出色预测能力（风险比 = 3.885，p < 0.001）。此外，我们的研究结果表明，这些大脑区域的拓扑特征介导了淀粉样β（Aβ）沉积（bootstrap 平均因果中介效应：β= -0.01 [-0.025，0.00]，p < 0.001）和大脑葡萄糖代谢（bootstrap 平均因果中介效应：β= -0.02 [-0.04，-0.001]，p < 0.001）对认知状态的影响。
本研究提出了 STGC-GCAM 框架，该框架使用大型多站点 fMRI 数据集识别 FC 生物标志物。
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