Motivated by the need for a better understanding of post-stroke recovery and new biomarkers to improve stroke patient stratification and outcomes, this thesis investigated structure-function coupling and its role in post-stroke recovery. Furthermore, in order to increase data comparability between sessions and centers (a critical challenge in contemporary clinical research), this thesis assessed the reproducibility of microstructure-informed structural connectivity measures in a multi-center dataset. Stroke is one of the major sources of permanent impairment, frequently of motor origin. However the clinical picture is very heterogeneous: patients show divergent courses of recovery and the underlying mechanisms are still unclear. In addition, current treatments still have limited success and are restricted to a 'one-fits-all' approach, not considering the individual patient's phenotype. As efforts to stratify patients based on structural or functional biomarkers are still needed, we chose to investigate the potential of a multimodal biomarker, the Structural Decoupling Index (SDI) (Preti & Van De Ville, 2019b), a new metric assessing the structure-function coupling strength in the brain. The first part (Studies 1 and 2) of this thesis investigates the potential of the SDI as a biomarker. In Study 1, the goals were to evaluate the feasibility of applying the SDI on an individual level in healthy older adults and to investigate the effect of an acute stroke on it. Consistent with the literature, we found a network gradient of SDI in healthy older adults, from high coupling in lower-level sensory areas, to low coupling in higher-level cognitive areas. This confirmed the applicability of SDI on an individual level. Furthermore, we showed that stroke impacts the SDI, with a higher decoupling on the ipsi- compared to the contralesional side, and with network-specific effects in RH stroke patients. In Study 2, the goal was to see whether the SDI evolved over time post-stroke and whether it links to behavior. The longitudinal analysis revealed differential network effects of stroke at T1 and T3. Furthermore, we showed that impairment in cognitive and psychological behavioral domains significantly correlates with variations in SDI in a number of key areas including motor regions (e.g., primary motor cortex) and that the brain pattern associated to behavior changes between T1 and T2. Surprisingly, motor performance did not explain variability in key motor areas (e.g., primary motor cortex). The link between post-stroke behavioral performance and SDI underlines its potential clinical relevance. Driven by the quest for more reproducible analysis pipelines in the context of longitudinal and clinical studies, the second part of this thesis (Study 3) addressed the subject-specific reproducibility and repeatability of microstructure-informed tractography. Through a multi-center study, we demonstrated its high reproducibility and subject-specificity, and we found evidence for increased biological accuracy. My thesis made a contribution by showing that the SDI is sensitive to pathophysiological changes that occur following a stroke and that it links to clinically relevant behavioral measures. In addition, it confirms the reproducibility and subject-specificity of microstructure-informed tractography. Together, my findings pave the way towards patient stratification and more personalized treatments in stroke rehabilitation.