This digital poster session highlights emerging methods for characterizing tissue biology, metabolism, and clinical outcomes in multiple sclerosis using advanced MRI contrasts and data-driven approaches. Presentations span innovative techniques such as CEST MRI, MR spectroscopy, MR fingerprinting, calibrated fMRI, ultra-high-field imaging, and AI-accelerated acquisition, alongside machine learning models for disease classification and prognosis. Together, the posters emphasize the integration of biophysical imaging, computational innovation, and multimodal biomarkers to improve understanding of lesion heterogeneity, neuroinflammation, metabolic dysfunction, and individualized outcome prediction in MS.