Identification of Multi-modal Imaging Biomarkers for Early Prediction of MCI-AD Conversion via Multigraph Representation

摘要 阿尔茨海默病(AD)是最常见的神经退行性痴呆形式，具有 在个人和社会层面产生了惊人的影响。作为早期的认知退化， 轻度认知障碍(MCI)有很高的机会转化为AD。见效快、早 这种转化的预测对于风险分层、患者管理、 以及可能的对症治疗。还识别MCI-AD转换终点 对于更好地评估治疗干预措施的有效性的临床试验很重要。 最近的研究表明，多模式神经成像可以提供更全面的 MCI-AD转换的表征，揭示了MCI-AD转换的生理基础 临床状态，并最终导致更高的基于多模式的预测精度 成像生物标志物。深度学习，特别是深度图卷积的研究进展 网络为我们提供了对多模式神经成像数据进行建模的强大工具 在大脑网络上。然而，尽管文献中AD的预测准确率很高，但多个 在处理数据方面，模式成像诊断仍然缺乏泛化能力和健壮性 由于相对较小的样本量和 样品标签中的潜在偏差。 在本提案中，我们将研究结构、功能和 MCI和AD患者的蛋白质病变网络--基于对比学习的多重图表 磁共振、功能磁共振和正电子发射计算机断层成像多模式神经影像数据的表征框架 医疗模式。建议的框架将用于识别和评估多模式图像 多点数据集MCI人群AD转换的生物标志物。通过分析 识别的多模式图像生物标志物的空间和种群模式，我们将能够 探索MCI-AD转化的神经科学和生物学新机制。

Summary Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and has an astounding impact at individual and societal levels. As the early-stage cognitive degeneration, mild cognitive impairment (MCI) has a high chance to convert to AD. Effective and early prediction of such conversion is of great importance for risk stratification, patient management, and possible symptomatic treatments. Identification of an MCI-AD conversion end point is also important for clinical trials for better evaluating the effectiveness of therapeutic interventions. Recent studies have shown that multi-modalities neuroimaging can offer a more comprehensive characterization for the MCI-AD conversion, revealing the physiologic underpinning of the clinical states, and ultimately result in higher prediction accuracy based on the multi-modal imaging biomarker. Advancement in deep learning, especially deep Graph Convolutional Networks, has provided us with powerful tools in modeling the multi-modal neuroimaging data on the brain networks. However, despite the high prediction accuracy of AD in literature, multi- modal imaging diagnostic still lacks generalizability and robustness in dealing with data from other sites/populations due to the combined effect of relatively smaller sample sizes and potential bias in the sample labels. In this proposal, we will investigate the interaction among structural, functional, and proteinopathies networks in MCI and AD patients via a contrastive learning-based, multigraph representation framework on the multi-modal neuroimaging data of MRI, fMRI and PET modalities. The proposed framework will be used to identify and evaluate a multi-modal image biomarker for the AD conversion in MCI population from a multi-site dataset. By analyzing the spatial and populational patterns of the identified multi-modal image biomarker, we will be able to discover novel neuroscientific and biological mechanisms of the MCI-AD conversion.