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

基本信息

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
项目类别：
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&apos 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

项目摘要

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.

项目概要现代多模态生物医学成像数据有潜力提高我们的诊断能力医疗状况并了解疾病进展的生物学机制。然而，这些数据通常显示非线性几何结构，即流形结构，这限制了经典统计方法从分析中获取更多知识的适用性该项目将重点关注新颖的生物医学数据集的开发。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对象与结构或功能图像的“信号”相结合。此类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值位于高度复杂的特定主题皮层表面。将这些数据建模为“功能数据”，即不依赖于过度简化的表示在实践中，所提出的框架将导致相关生物信息的丢失。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，例如疾病状态、治疗类型或遗传信息，验证科学假设或发现新的成像生物标志物的目的。开发的工具将作为免费和开源工具提供，可以轻松地与最流行的数据分析软件，使他们成为更大的成像软件的一部分生态系统。