AbstractWe aimed to compare the ability of diffusion tensor imaging and multi‐compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy‐six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid‐attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate–severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R‐squared value of .57, significantly outperforming the R‐squared value of .24 for FA (p‐value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS‐related impairments.