title: Sparse geostatistical analysis in clustering fMRI time series.
abstract: Clustering is used in fMRI time series data analysis to find the active regions in the brain related to a stimulus. However, clustering algorithms usually do not work well for ill-balanced data, i.e., when only a small proportion of the voxels in the brain respond to the stimulus. This is the typical situation in fMRI--most voxels do not, in fact, respond to the specific task. We propose a new method of sparse geostatistical analysis in clustering, which first uses sparse principal component analysis (SPCA) to perform data reduction, followed by geostatistical clustering. The proposed method is model-free and data-driven; in particular it does not require prior knowledge of the hemodynamic response function, nor of the experimental paradigm. Our data analysis shows that the spatial and temporal structures of the task-related activation produced by our new approach are more stable compared with other methods (e.g., GLM analysis with geostatistical clustering). Sparse geostatistical analysis appears to be a promising tool for exploratory clustering of fMRI time series.
authors.affiliation: Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA. athensye@yahoo.com
authors.forename: Jun
authors.initials: J
authors.lastname: Ye
authors.forename: Nicole A
authors.initials: NA
authors.lastname: Lazar
authors.forename: Yehua
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods* / Q000706:statistics & numerical data; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods; D012984:Software; D013997:Time Factors