Analysis of Event-Related fMRI Data Using Diffusion Maps

• Published in: Information Processing in Medical Imaging Vol. 19; pp. 652 - 663
• Main Authors: Shen, Xilin, Meyer, François G.
• Format: Book Chapter Journal Article
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005
• Series: Lecture Notes in Computer Science
• Subjects: Algorithms; Brain Mapping - methods; Evoked Potentials, Visual - physiology; fMRI Dataset; Hemodynamic Response; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Signal Time Series; Spectral Graph Theory; Time Series; Visual Cortex - physiology
• ISBN: 9783540265450; 3540265457
• ISSN: 0302-9743; 1011-2499; 1611-3349
• DOI: 10.1007/11505730_54

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.