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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.
Journal of neuroscience methods 05/2011; 199(2):336-45. · 2.30 Impact Factor
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ABSTRACT: In this paper, we conduct an investigation of the null hypothesis distribution for functional magnetic resonance imaging (fMRI) time series using multiscale analysis tools, SiZer (significance of zero crossings of the derivative) and wavelets. Most current approaches to the analysis of fMRI data assume simple models for temporal (short term or long term) dependence structure. Such simplifications are to some extent necessary due to the complex, high-dimensional nature of the data, but to date there have been few systematic studies of the dependence structures under a range of possible null hypotheses, using data sets gathered specifically for that purpose. We aim to address some of these issues by analyzing the detrended data with a long enough time horizon to study possible long-range temporal dependence. Our multiscale approach shows that even for resting-state data, data, i.e. "null" or ambient thought, some voxel time series cannot be modeled by white noise and need long-range dependent type error structure. This finding suggests the use of different time series models in different parts of the brain in fMRI studies.
Journal of neuroscience methods 11/2010; 193(2):334-42. · 2.30 Impact Factor
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ABSTRACT: Clustering of functional magnetic resonance imaging (fMRI) time series--either directly or through characteristic features such as the cross-correlation with the experimental protocol signal--has been extensively used for the identification of active regions in the brain. Both approaches have drawbacks; clustering of the time series themselves may identify voxels with similar temporal behavior that is unrelated to the stimulus, whereas cross-correlation requires knowledge of the stimulus presentation protocol. In this paper we propose the use of autocorrelation structure instead--an idea borrowed from geostatistics; this approach does not suffer from the deficits associated with previous clustering methods. We first formalize the traditional classification methods as three steps: feature extraction, choice of classification metric and choice of classification algorithm. The use of different characteristics to effect the clustering (cross-correlation, autocorrelation, and so forth) relates to the first of these three steps. We then demonstrate the efficacy of autocorrelation clustering on a simple visual task and on resting data. A byproduct of our analysis is the finding that masking prior to clustering, as is commonly done, may degrade the quality of the discovered clusters, and we offer an explanation for this phenomenon.
Statistics in Medicine 07/2009; 28(19):2490-508. · 1.88 Impact Factor
