Statistical models for the integrative analysis of complex biomedical images with manifold structure

具有流形结构的复杂生物医学图像综合分析的统计模型

• 批准号: 10590469
• 负责人: Eardi Lila
• 金额: $ 7.54万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10590469
• 关键词: Address; Adopted; Adoption; Alzheimer' s Disease; Anatomy; Behavioral; Biological; Biological Process; Classification; Communities; Complex; Computer software; Coupled; Data; Data Analyses; Data Display; Data Pooling; Data Reporting; Data Set; Development; Diagnosis; Disease; Disease Progression; Ecosystem; Electroencephalography; Functional Imaging; Functional Magnetic Resonance Imaging; Genetic; Goals; Human; Image; Joints; Knowledge; Libraries; Maps; Measurement; Medical; Methodology; Methods; Modeling; Modernization; Names; Organ; Paper; Parkinson Disease; Property; Research Personnel; Scientist; Shapes; Signal Transduction; Software Tools; Statistical Data Interpretation; Statistical Methods; Statistical Models; Structure; Surface; Thick; Time; Variant; X-Ray Computed Tomography; anatomic imaging; biobank; biomedical imaging; biomedical scientist; computational anatomy; computerized tools; connectome; data analysis pipeline; diagnostic algorithm; genetic information; geometric structure; high resolution imaging; imaging biomarker; imaging software; improved; interest; interoperability; multimodality; neuroimaging; novel; open source; open source tool; predictive modeling; predictive tools; progression marker; software development; structural imaging; tool; user friendly software; web page

PROJECT SUMMARY Modern multimodal biomedical imaging data have the potential to advance our ability to diagnose medical conditions and to understand the biological mechanisms underlying disease progression. However, these data typically display non-linear geometric structure, i.e., manifold structure, which limits the applicability of classical statistical methods to gain further knowledge from the analysis of the contemporary biomedical datasets. This project will focus on the development of novel statistical methods for the analysis of biomedical images with manifold structure that are subject- specific anatomical objects coupled with ‘signals' that are structural or functional images. Examples of such data are fMRI signals, seed-based connectivity maps, or cortical thickness measurements located on the highly convoluted subject-specific cortical surfaces. The proposed methods will model these data as ‘functional data', i.e., without relying on oversimplified representations that could lead to the loss of relevant biological information. In practice, the proposed framework will allow researchers to relate anatomical, structural, and functional imaging features to other variables typically collected, such as disease status, treatment type, or genetic information, with the aim of validating scientific hypotheses or discovering novel imaging biomarkers. The models developed will be made available as free and open-source tools that can easily interface with the most popular data analysis software for them to become part of the larger imaging software ecosystem.

项目总结 现代多模式生物医学成像数据有可能提高我们的诊断能力 了解疾病进展的生物学机制。 然而，这些数据通常表现为非线性几何结构，即流形结构，其 限制了经典统计方法的适用性，以便从分析 当代生物医学数据集。这个项目将专注于小说的发展 用于分析具有流形结构的生物医学图像的统计方法 特定的解剖物体与结构或功能图像的‘信号’相结合。示例 这些数据包括功能磁共振成像信号、基于种子的连接图或皮质厚度测量 位于高度错综复杂的受试者特定皮质表面。建议的方法将 将这些数据建模为“功能数据”，即不依赖于过于简化的表示 可能导致相关生物信息的丢失。实际上，拟议的框架将 允许研究人员将解剖、结构和功能成像特征与其他 通常收集的变量，如疾病状态、治疗类型或遗传信息， 验证科学假说或发现新的成像生物标志物的目的。模特们 将以免费开放源码工具的形式提供，这些工具可以轻松地与 最流行的数据分析软件让他们成为更大的成像软件的一部分 生态系统。