Resting state functional connectivity as the name suggests is defined as significant temporal correlation between spatially distinct regions of the brain during rest. In this thesis, fMRI resting state dataset was analyzed using different available processing techniques with the same fMRI data to study differences between the various methods. All the imaging data from each of the subjects was processed in an identical fashion. The same method was used for detecting connectivity. The number of independent components in the data was used as the base to differentiate the effect of each of these methods. Independent component analysis was performed on each step after and before converting each dataset into MNI space to see the effect of normalization. In resting state fMRI study, different algorithms of motion correction showed no significant difference in the results. Temporal filtering by rectangular filter for particular bands of frequency showed no significant difference in the data analysis. Gaussian and Hamming windows however, work well for the required purpose. In case of spatial smoothing, Unsharp and Sobel filters which emphasize on the edges resulted in an abnormally high increase in number of components which suggested low pass filters like Gaussian and Average are more suitable for fMRI preprocessing.