Multi-Site Neuroimage Harmonization for Personalized Brain Disorder Analysis

用于个性化脑部疾病分析的多部位神经影像协调 摘要 预测临床前阿尔茨海默病（AD）的未来进展，如主观认知下降 (SCD)对于药物开发和及时干预以防止进一步的认知能力下降至关重要。多站点穆尔- 虽然越来越多地用于增加样本量和提高统计功效， 研究SCD和AD相关疾病（ADRD），易受研究中心间和模态间数据的影响， 由扫描仪/方案、研究人群和成像方式的差异引起的误差。减轻 研究中心间数据异质性、多模态数据的原则性融合以及神经影像学数据的精确解释 可以减少后续分析中的偏差，并有助于避免错误的结论。在这个项目中，我们将开发 一套计算工具，由先进的机器学习技术提供支持，用于多站点数据协调， SCD进展的多模式数据融合和个性化/受试者特异性神经影像解读 预测.这些工具将在5，300多名具有多模态数据的受试者中进行广泛评估（例如，磁 共振成像、正电子发射断层扫描和脑脊液），涉及79个成像中心。 我们提出三个目标。在目标1中，我们将开发特征级和图像级深度学习框架， 多站点数据统一。许多研究通过简单地假设一个共同的 数据源我们的方法将允许精确医学和图像级harmo的特征级协调， 针对更广泛的应用的nization。开发的模型将很容易通过无监督的方式进行训练 学习在目标2中，我们将开发一个框架，以有效地融合多模态数据，用于后续分析- 排除那些缺乏特定模式的实验对象现有的研究通常需要模态完整的受试者， 限制了其在多中心研究中的效用，其中许多受试者可能由于患者的原因而缺乏一种或多种模式 退出或扫描失败。我们的模型可以用模态缺失的主题进行训练，因此对于 更好的适应性。在目标3中，我们将开发一个快速准确的神经图像搜索框架 促进SCD和ADRD的个性化分析。在受试者水平上解释神经成像数据是- 由于成像信息量的不断增加，我们的方法将有助于克服这一点 通过可扩展的神经图像搜索预测SCD和ADRD的受试者特异性进展的难度。

Multi-Site Neuroimage Harmonization for Personalized Brain Disorder Analysis Abstract Predicting the future progression of preclinical Alzheimer's disease (AD) such as subjective cognitive decline (SCD) is essential for drug development and timely intervention to prevent further cognitive decline. Multi-site mul- timodal neuroimaging data, while increasingly employed to augment sample size and improve statistical power for investigating SCD and AD-related disorders (ADRD), are susceptible to inter-site and inter-modality data het- erogeneity caused by differences in scanners/protocols, studied populations, and imaging modalities. Mitigating inter-site data heterogeneity, principled fusion of multimodal data, and precise interpretation of neuroimaging data can reduce bias in subsequent analyses and help avoid erroneous conclusions. In this project, we will develop a set of computational tools, powered by advanced machine learning techniques, for multi-site data harmoniza- tion, multimodal data fusion, and personalized/subject-specific neuroimage interpretation for SCD progression prediction. These tools will be evaluated extensively on 5,300+ subjects with multimodal data (e.g., magnetic resonance imaging, positron emission tomography, and cerebrospinal fluid) involving 79 imaging centers. We propose three aims. In Aim 1, we will develop both feature-level and image-level deep learning frameworks for multi-site data harmonization. Many studies ignore inter-site data heterogeneity by simply assuming a common data source. Our methods will allow feature-level harmonization for precision medicine and image-level harmo- nization targeting a broader range of applications. The developed models will be easy to train via unsupervised learning. In Aim 2, we will develop a framework to effectively fuse multimodal data for subsequent analyses with- out discarding subjects who lack certain modalities. Existing studies usually require modality-complete subjects, limiting their utility in multi-site studies where many subjects may lack one or several modalities due to patient dropouts or failed scans. Our models can be trained with modality-missing subjects, and thus are practical with considerably better adaptability. In Aim 3, we will develop a framework for fast and accurate neuroimage search to facilitate personalized analysis of SCD and ADRD. Interpreting neuroimaging data at the subject level is of- ten challenging due to the ever-increasing amount of imaging information. Our method will help overcome this difficulty by scalable neuroimage search for subject-specific progression prediction of SCD and ADRD.