26 February 2008 Volumetric fMRI data analysis using an iterative classification method
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We have previously developed a novel framework for the analysis of single-slice functional magnetic resonance imaging (fMRI) data that identifies multi-pixel regions of activation through iterative segmentation-based optimization over hemodynamic response (HDR) estimates, generated at the level of both individual pixels and regional groupings. Through the addition of a correction for the disparate sampling times associated with multi-slice acquisitions in fMRI, the algorithm has been extended to permit analysis of full volumetric data. Additional improvement in performance is achieved through inclusion of an estimate of the covariance matrix of the fMRI data, previously assumed to be proportional to the identity matrix across all regions. Simulations using synthetic activation embedded in autoregressive noise reveal the proposed procedure to be more sensitive and selective than conventional fMRI analysis methods (reference set: general linear model test, GLM; independent component analysis, ICA; principal component analysis, PCA) in identification of active regions over the range of average contrast-to-noise ratios of 0.7 to 2.
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Liang Liu, Kihwan Han, and Thomas M. Talavage "Volumetric fMRI data analysis using an iterative classification method", Proc. SPIE 6814, Computational Imaging VI, 68140E (26 February 2008); doi: 10.1117/12.775035; https://doi.org/10.1117/12.775035

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