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

总结 阿尔茨海默病（AD）是神经退行性痴呆的最常见形式，并且具有 在个人和社会层面产生了巨大的影响。作为早期认知退化， 轻度认知障碍（MCI）有很高的机会转化为AD。有效和早日 这种转化的预测对于危险分层，患者管理， 以及可能的对症治疗。MCI-AD转换端点的识别也是 这对于临床试验更好地评估治疗干预的有效性非常重要。 最近的研究表明，多模态神经成像可以提供更全面的 MCI-AD转化的表征，揭示了MCI-AD转化的生理基础。 临床状态，并最终导致更高的预测精度的基础上，多模态 成像生物标志物。深度学习的进步，尤其是深度图卷积 网络为我们提供了强大的工具，在建模的多模态神经影像数据 在大脑网络上。然而，尽管在文献中AD的预测准确性很高，但多 模态成像诊断在处理来自 其他研究中心/人群，因为样本量相对较小， 样品标签中的潜在偏倚。 在这个建议中，我们将研究结构，功能和 MCI和AD患者的蛋白质病变网络，通过基于对比学习的多重图 MRI、fMRI和PET多模态神经成像数据的表示框架 方式。该框架将用于识别和评估多模态图像 MCI人群中AD转化的生物标志物。通过分析 空间和人口模式的识别多模态图像生物标志物，我们将能够 发现MCI-AD转换的新神经科学和生物学机制。

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.