Functional magnetic resonance imaging (fMRI) is one of the finest modality to measure brain activity. Two main steps in the analysis of fMRI data are pre-processing and the statistical analysis. Pre-processing is equally an important part because it takes raw data from the scanner and prepares it for the statistical analysis. This study first explains the realignment during preprocessing and then the importance of realignment parameters (one of nuisance parameters) in General Linear model (GLM). Nuisance regressors are used to reduce noise only and are effect of no interest. In this study, it is concluded that realignment parameters have a significant effect in the model estimation because the results are improved with these parameters especially when large head movement is found during data acquisition. © 2015 IEEE.