Analysing fMRI experiments with structural adaptive smoothing procedures
- Tabelow, Karsten
- Polzehl, Jörg
- Spokoiny, Vladimir
2010 Mathematics Subject Classification
- 62P10 92C55 62G05 62G10
2008 Physics and Astronomy Classification Scheme
- functional MRI, spatially adaptive smoothing, signal detection
Data from functional magnetic resonance imaging (fMRI) consists of time series of brain images which are characterized by a high noise level and a low signal-to-noise ratio. We provide a complete procedure for fMRI analysis. In order to reduce noise and to improve signal detection the fMRI data is spatially smoothed. However, the common application of a Gaussian filter does this at the cost of loss of information on spatial extend and shape of the activation area. We suggest to use the propagation-separation procedures introduced by Polzehl and Spokoiny (2005) instead. We show that this significantly improves the information on the spatial extend and shape of the activation region with similar results for the noise reduction. Signal detection is based on locally varying thresholds defined by random field theory. Effects of adaptive and non adaptive smoothing are illustrated by artificial examples and an analysis of real data.
- NeuroImage, 33 (2006) pp. 55--62.