School of Life Sciences, Shanghai University, Shanghai, China.
School of Communication and Information Engineering, Shanghai University, Shanghai, China.
Hum Brain Mapp. 2023 Dec 1;44(17):6020-6030. doi: 10.1002/hbm.26493. Epub 2023 Sep 23.
Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG-PET and resting-state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback-Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC-FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD-related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p < .05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum.
异常的葡萄糖代谢和大脑中的血液动力学变化与认知功能密切相关，从不同的生化和生理过程提供了互补的信息。然而，目前尚不清楚如何有效地整合这两种模式跨越不同的大脑区域。在这项研究中，我们开发了一种基于连接组的稀疏耦合方法，用于混合 PET/MRI 成像，可以有效地提取早期阿尔茨海默病（AD）的成像标志物。我们从 SILCODE 项目（NCT03370744）中获得了 56 名健康对照（HC）、54 名主观认知下降（SCD）和 27 名认知障碍（CI）AD 患者的 FDG-PET 和静息状态 fMRI 数据。对于每个参与者，通过 Kullback-Leibler 散度相似性估计构建代谢连接组（MC），通过 Pearson 相关构建功能连接组（FC）。随后，我们在不同的稀疏水平上测量 MC 和 FC 之间的耦合强度，评估其稳定性，并探索 AD 连续体中异常的耦合强度。结果表明，稀疏 MC-FC 耦合指数在每个大脑网络中都是稳定的，在不同的研究对象中是一致的。它比其他传统指标更具有正态分布性，并且能够捕获更多与 SCD 相关的脑区，尤其是在边缘和默认模式网络中。与其他传统指标相比，该指标表现出最佳的分类性能。SCD/HC 的 AUC 值达到 0.748，CI/HC 的 AUC 值达到 0.992。值得注意的是，我们发现异常耦合强度与神经心理学量表之间存在显著相关性（p < 0.05）。这项研究为混合 PET/MRI 成像提供了一种具有临床相关性的工具，能够探索 AD 早期的成像标志物，并更好地理解 AD 连续体中的病理生理学。
