SCH: Novel and Interpretable Statistical Learning for Brain Images in AD/ADRDs

SCH：针对 AD/ADRD 大脑图像的新颖且可解释的统计学习

• 批准号: 10816764
• 负责人: XIAO SONG
• 金额: $ 30万
• 依托单位: GEORGE MASON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10816764
• 关键词: 3-Dimensional; 4D Imaging; Address; Adopted; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease related dementia; Biological Markers; Brain; Brain imaging; Brain region; Clinical; Computer software; Data; Diagnosis; Disease; Effectiveness; Ensure; Foundations; Four-dimensional; Friends; Functional Imaging; Genetic; Genetic Markers; Goals; Health; Healthcare; Heterogeneity; Image; Imaging technology; Institution; Magnetic Resonance Imaging; Measures; Medical; Medical Research; Methods; Modeling; Modernization; Multimodal Imaging; Outcome; Performance; Phenotype; Positron-Emission Tomography; Prognosis; Public Health; Research; Research Personnel; Resolution; Sampling; Science; Shapes; Signal Transduction; Statistical Methods; Statistical Models; Study Subject; Technology; Testing; Three-Dimensional Imaging; Uncertainty; Variant; X-Ray Computed Tomography; biomarker development; biomarker discovery; biomedical imaging; computational platform; diagnostic accuracy; disease diagnosis; disease prognosis; flexibility; genome wide association study; high dimensionality; image processing; imaging biomarker; imaging study; improved; innovation; insight; interest; learning strategy; novel; parallel computer; rapid growth; sound; statistical learning; structural imaging; tool; two-dimensional

Biomedical imaging technology has undergone rapid advancements over the last several decades, producing large volumes of multimodal imaging data that hold great promise as biomarkers for agingrelated diseases such as Alzheimer’s. Current imaging biomarkers are primarily based on specific extracted one-dimensional measures that may not fully capture the richness of imaging data. Utilizing three-dimensional (3D) or higher imaging information directly may facilitate the identification of more effective disease biomarkers to inform diagnosis, prognosis, and treatment. However, this also brings significant challenges, such as analyzing ir-regularly shaped 3D objects, managing high-dimensional and high-resolution data, addressing noisiness and complexity, quantifying uncertainty, and ensuring the interpretability of the results. Our multi-institutional, inter-disciplinary team of investigators will develop efficient statistical learning approaches and scalable computing tools to extract and assess biomarkers from large-scale brain imaging studies. We will also incorporate genetic and clinical information in constructing the biomarkers. Specifically, our proposal comprises five interrelated research aims carried out by investigators with complementary expertise from three institutions. Aim 1 focuses on developing an interpretable model for genome-wide association studies (GWAS) with brain imaging pheno-types and non-visual contextual information. Aim 2 targets to develop novel nonparametric distributed learning methods for analyzing 3D brain imaging data using an innovative domain decomposition strategy to improve computing performance. Aim 3 quantifies the bias effect in image processing and develops inference methods to reveal the underlying signal from brain imaging data and identify significant brain regions among different diagnosis groups. Aims 4-5 aim to develop statistical methods for obtaining and evaluating imaging-adjusted biomarkers for disease diagnosis and prognosis and assess the incremental value of imaging information over genetic biomarkers on diagnosis and prediction accuracy. The efficacy of the methods developed in this pro-posal will be tested by data collected from studies in Alzheimer’s disease and brain sciences. The proposed research will address critical gaps in current biomarker development and analysis by utilizing advanced sta-tistical learning approaches and computing tools to directly utilize the 3D or higher imaging information. This innovative approach holds the potential to provide more effective disease biomarkers, leading to improved accuracy in diagnosis, prognosis, and treatment for Alzheimer’s disease and related dementias.

在过去的几十年中，生物医学成像技术经历了快速的进步， 产生大量的多模式成像数据，这些数据具有巨大的希望，作为老龄化疾病（例如阿尔茨海默氏症）的生物标志物。当前的成像生物标志物主要基于特定 提取的一维测量值可能无法完全捕获成像数据的丰富性。利用 三维（3D）或更高的成像信息可能会直接促进更多 有效的疾病生物标志物，以告知诊断，预后和治疗。但是，这也带来了 重大挑战，例如分析的IR规范形状的3D对象，管理高维和 高分辨率数据，解决噪声和复杂性，量化不确定性并确保 结果的解释性。我们的多机构跨学科研究人员将发展 有效的统计学习方法和可扩展的计算工具来提取和评估生物标志物 来自大型脑成像研究。我们还将在 构建生物标志物。具体而言，我们的建议包括五个相互关联的研究目的 由三个机构提供完整专业知识的调查员。 AIM 1专注于开发 全基因组关联研究（GWAS）的可解释模型与脑成像现象类型和 非视觉上下文信息。 AIM 2目标以开发新颖的非参数分布式学习 使用创新域分解策略分析3D脑成像数据的方法 改善计算性能。 AIM 3量化图像处理中的偏差效应并发展 推理方法以揭示来自脑成像数据的潜在信号并识别重要的大脑 不同诊断组之间的区域。目的4-5旨在开发获取和 评估疾病诊断和预后的成像调整生物标志物的增量 成像信息的价值超过遗传生物标志物在诊断和预测准确性上。效率 在此Pro-Posal中开发的方法将通过阿尔茨海默氏症的研究收集的数据来测试 疾病和脑科学。拟议的研究将解决当前生物标志物的关键差距 通过利用先进的Sta-tistic学习方法和计算工具来开发和分析 直接利用3D或更高成像信息。这种创新的方法具有提供的潜力 更有效的疾病生物标志物，从而提高了诊断，预后和治疗的准确性 对于阿尔茨海默氏病和相关痴呆症。