Recent advances in analysis of fMRI have established the existence of functional sub-networks in the human brain that are active during the performance of visual, motor, language, and other tasks. We describe two computational methods of delineating functional sub-networks that are active when an individual performs an approach-avoidance paradigm. The paradigm consisted of presentation of images of pleasant and unpleasant faces that were shown to nine volunteers for 10 seconds after a preceding rest period of 50 seconds during which a green computer screen was displayed. The subjects were instructed to squeeze a ball with their right hand if they judged the face to be unpleasant, in which case the unpleasant face would disappear. An fMRI BOLD activation was created and used as input for two different kinds of clustering method: The MCODE algorithm based on graph-theoretical analysis and a Conscious Self-Organizing Map (CSOM). Clustering obtained with both methods was based on the temporal variations of the fMRI BOLD signal activity. Both methods identified distinct regions in the brain which were separated by long-range connections. The MCODE algorithm was supplied with time-courses for activated voxels when performing the paradigm, while the CSOM clustering used all voxels in the brain. Both yielded similar clusters for activated voxels. The combination of MCODE and CSOM presents a new approach in identifying functional subunits in the human brain and warrants further investigation into the subject.