Temporal signal drift is one of the significant artifacts in functional Magnetic Resonance Imaging (fMRI) data that is not given as much attention as motion or physiological artifacts. However, signal drift if not accounted for, can introduce spurious correlation between different regions in resting state fMRI data. Hence detection and removal of signal drift is an important preprocessing step in fMRI data analysis. Here we propose an automated data driven approach that makes use of Principal Component Analysis (PCA) to eliminate not only low frequency signal drift but also spontaneous high frequency global signal fluctuations. This approach is also able to identify the most dominant component for each voxel separately. For task fMRI, this can help us identify regions that respond in a time locked manner to the experiment paradigm. Such regions can be thought of as activation regions. The dominant principal components corresponding to such regions can also be used to investigate intra-region Hemodynamic Response (HR) variability within subjects and across subjects.