Multi-Site Neuroimage Harmonization for Personalized Brain Disorder Analysis

用于个性化脑疾病分析的多点神经图像调和方法 摘要 预测临床前阿尔茨海默病(AD)的未来进展，如主观认知能力下降 (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是必不可少的。多站点多- 多模式神经成像数据，虽然越来越多地被用于扩大样本量和改善统计能力 研究SCD和AD相关疾病(ADRD)，容易受到部位间和通道间数据的影响。 由于扫描仪/方案、研究人群和成像方式的不同而引起的异质性。减轻 站点间数据异构性、多模式数据的原则性融合和神经影像数据的精确解释 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 一套由先进的机器学习技术支持的计算工具，用于多站点数据协调- 多模式数据融合和个性化/特定对象fic神经图像解释用于SCD进展 预测。这些工具将使用多模式数据(例如，磁性数据)在5,300个对象上进行广泛评估 磁共振成像、正电子发射断层扫描和脑脊髓fl检查)，涉及79个成像中心。 我们提出了三个目标。在目标1中，我们将开发特征级别和图像级别的深度学习框架 多站点数据协调。许多研究忽略了站点间的数据异构性，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声学的功能级协调- 以更广泛的应用为目标的本地化。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，我们将制定一个框架，将多模式数据有效地融合在一起，以便随后进行分析，并- 丢弃缺乏特定模式的受试者。现有的研究通常需要完成通道的受试者， 限制了它们在多点研究中的实用性，在这些研究中，许多受试者可能由于患者原因而缺少一种或多种方法 辍学或扫描失败。我们的模型可以用缺少通道的对象进行训练，因此适用于 适应性要好得多。在目标3中，我们将开发一个用于快速准确的神经图像搜索的框架 便于对SCD和ADRD进行个性化分析。在受试者层面上解释神经成像数据是- 由于成像信息量的不断增加，这十个方面具有挑战性。我们的方法将有助于克服这一点 通过可伸缩的神经图像搜索DIFficulty来预测SCD和ADRD的主题组fic进展。

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.