Spatiotemporal Denoising and Clustering of fMRI Data

This paper examines combined spatiotemporal denoising and clustering of functional magnetic resonance imaging (fMRI) time series. Most fMRI denoising methods are implemented either in spatial or temporal domain without taking into account both space and time information. In this work, a spatiotemporal denoising method is developed where spatial denoising is implemented by Bayesian shrinkage that uses temporal prior information obtained by statistical testing on all voxel time courses. After the denoising, a set of spatiotemporal features are extracted and characterized by a Gaussian mixture model, which is applied to detect activated areas. The proposed methods have been tested on both synthetic and experimental data, and the results demonstrate their effectiveness.