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 from 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.