Sparse geostatistical analysis in clustering fMRI time series

Jun Ye, Nicole A. Lazar, Yehua Li

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)336-345
Number of pages10
JournalJournal of Neuroscience Methods
Volume199
Issue number2
DOIs
StatePublished - Aug 15 2011

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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