Analyzing fMRI experiments with structural adaptive smoothing procedures
Please always quote using this URN:urn:nbn:de:0296-matheon-3134
- 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.
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| Author: | Karsten Tabelow, Jörg Polzehl, Vladimir Spokoiny |
|---|---|
| URN: | urn:nbn:de:0296-matheon-3134 |
| Referee: | Christof Schütte |
| Document Type: | Preprint, Research Center Matheon |
| Language: | English |
| Date of first Publication: | 2006/05/31 |
| Release Date: | 2005/12/20 |
| Tag: | |
| Institute: | Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS) |
| MSC-Classification: | 62-XX STATISTICS / 62Gxx Nonparametric inference / 62G05 Estimation |
| 62-XX STATISTICS / 62Gxx Nonparametric inference / 62G10 Hypothesis testing | |
| 62-XX STATISTICS / 62Pxx Applications [See also 90-XX, 91-XX, 92-XX] / 62P10 Applications to biology and medical sciences | |
| 92-XX BIOLOGY AND OTHER NATURAL SCIENCES / 92Cxx Physiological, cellular and medical topics / 92C55 Biomedical imaging and signal processing [See also 44A12, 65R10] | |
| Preprint Number: | 330 |
