(1998) A critique of the use of the Kolmogorov-Smirnov (KS) statistic for the analysis of BOLD fMRI data. In the second part of our investigation, we will apply this technique to analyze a large fMRI dataset involving repeated presentation of sensory-motor response stimuli in young, elderly, and demented subjects.Īguirre, G., Zarahn, E., and D’Esposito, M. Finally, we propose a new model for statistical analysis of functional MRI data using this atlas-based wavelet space representation. We discovered that the Cauchy, Bessel K Forms, and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations.
The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data.
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