title: Analysis of event-related fMRI data using diffusion maps.
abstract: 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.
authors.affiliation: University of Colorado at Boulder, Boulder, CO 80309, USA.
authors.forename: Xilin
authors.initials: X
authors.lastname: Shen
authors.forename: François G
authors.initials: FG
authors.lastname: Meyer
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D014793:Visual Cortex / Q000502:physiology*