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

项目摘要 现代多模态生物医学成像数据有可能提高我们的诊断能力 医学条件和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，歧管结构， 限制了经典统计方法的适用性，无法从分析中获得进一步的知识， 当代生物医学数据集。该项目将侧重于小说的发展 统计方法的生物医学图像的分析与歧管结构，是主题- 特定的解剖对象与作为结构或功能图像的“信号”相耦合。示例 这些数据包括功能磁共振成像信号、基于种子的连通性图或皮层厚度测量 位于高度复杂的主体特定皮质表面上。所提出的方法将 将这些数据建模为“功能数据”，即，而不依赖于过于简化的表示， 可能会导致相关生物信息的丢失。实际上，拟议框架将 使研究人员能够将解剖、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， 目的是验证科学假设或发现新的成像生物标志物。模型 开发的工具将作为免费和开源的工具提供，可以很容易地与 最流行的数据分析软件，使其成为大型成像软件的一部分 生态系统

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.