Abstract: Classically, analysis of the time-varying data acquired during fMRI experiments is done using static activation maps obtained by testing voxels for the presence of significant activity using statistical methods. The models used in these analysis methods have a number of parameters, which profoundly impact the detection of active brain areas. Also, it is hard to study the temporal dependencies and cascading effects of brain activation from these static maps. In this paper, we propose a methodology to visually analyze the time dimension of brain function with a minimum amount of processing, allowing neurologists to verify the correctness of the analysis results, and develop a better understanding of temporal characteristics of the functional behaviour. The system allows studying time-series data through specific volumes-of-interest in the brain-cortex, the selection of which is guided by a hierarchical clustering algorithm performed in the wavelet domain. We also demonstrate the utility of this tool by presenting results on a real data-set.