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Abstract：
Due to the complex architectural diversity of biological networks, there is an increasing need to complement statistical analyses with a qualitative and local description of their spatial properties. One such network is the extracellular matrix (ECM), a biological scaffold for which changes in its spatial organization significantly impact tissue functions in health and disease. Quantifying variations in the fibrillar architecture of major ECM proteins should considerably advance our understanding of the link between tissue structure and function. Inspired by the analysis of functional magnetic resonance imaging (fMRI) images, we propose a novel statistical analysis approach embedded into a machine learning paradigm, to measure and detect local variations of meaningful ECM parameters. We show that parametric maps representing fiber length and pore directionality can be analyzed within the proposed framework to differentiate among various tissue states. The parametric maps are derived from graph-based representations that reflect the network architecture of fibronectin (FN) fibers in a normal, or disease-mimicking in vitro setting. Such tools can potentially lead to a better characterization of dynamic matrix networks within fibrotic tumor microenvironments and contribute to the development of better imaging modalities for monitoring their remodeling and normalization following therapeutic intervention.
摘要：
由于生物网络复杂的建筑多样性，越来越需要用对其空间属性的定性和局部描述来补充统计分析。一种这样的网络是细胞外基质(ECM)，一种生物支架，其空间组织的变化会显著影响健康和疾病中的组织功能。定量主要ECM蛋白的纤维结构的变化应大大促进我们对组织结构和功能之间联系的理解。受功能磁共振成像（fMRI）图像分析的启发，我们提出了一种嵌入机器学习范式的新统计分析方法，测量和检测有意义的ECM参数的局部变化。我们表明，可以在所提出的框架内分析表示纤维长度和孔方向性的参数图，以区分各种组织状态。参数图是从基于图形的表示中得出的，这些表示反映了正常纤维中纤维连接蛋白（FN）纤维的网络结构，或体外模拟疾病。这样的工具可以潜在地导致对纤维化肿瘤微环境内的动态矩阵网络的更好表征,并且有助于开发用于监测其在治疗干预之后的重塑和正常化的更好的成像模态。
