Modeling hemodynamic variability with fuzzy features for detecting brain activation from fMR time-series
We propose to detect brain activation from fMR time-series of a group study by modeling fuzzy features. Five discriminating features are automatically extracted from fMRI data by a sequence of temporal-sliding-windows. A fuzzy model based on these features is first derived by a gradient method on a set of initial training data and then incrementally enhanced. The resulting fuzzy activation maps of all subjects are then combined to provide a measure of strength of activation of each voxel, based on the group of subjects. A two-way thresholding scheme is introduced to determine true activated voxels. The method is tested on both synthetic and real fMRI datasets. The method is less vulnerable to correlated noise and able to capture the key activation from a group of subjects by adapting to hemodynamic variability across subjects.
KeywordsIndependent Component Analysis Fuzzy Model Statistical Parametric Mapping Correlate Noise Incremental Learning
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