The blood oxygen level-dependent (BOLD) signal in response to brief periods of stimulus can be detected using event-related functional magnetic resonance imaging (ER-fMRI). In this paper, we propose a new approach for the analysis of ER-fMRI data. We regard the time series as vectors in a high dimensional space (the dimension is the number of time samples). We believe that all activated times series share a common structure and all belong to a low dimensional manifold. On the other hand, we expect the background time series (after detrending) to form a cloud around the origin. We construct an embedding that reveals the organization of the data into an activated manifold and a cluster of non-activated time series. We use a graph partitioning technique-the normalized cut to find the separation between the activated manifold and the background time series. We have conducted several experiments with synthetic and in-vivo data that demonstrate the performance of our approach.