Sparse geostatistical analysis in clustering fMRI time series

作者：Ye Jun^*; Lazar Nicole A; Li Yehua

来源：Journal of Neuroscience Methods, 2011, 199(2): 336-345.

DOI：10.1016/j.jneumeth.2011.05.016

• Autocovariance
• Data-driven analysis
• LASSO
• Silhouette values

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.

• 出版日期2011-8-15