Using the Adaptive Smoothing Plugin

Using the Adaptive Smoothing Plugin

Overview

This plugin performs adaptive smoothing and inference for single subject task related fMRI experiments. It has to be executed after GLM analysis on a specific contrast, see Jörg Polzehl, Henning U. Voss, Karsten Tabelow (2010) 'Structural adaptive segmentation for statistical parametric mapping', NeuroImage 52(2), 515-523 for details on the method.

Before running the plugin

Please open a VMR or FMR document and run or load a general linear model single study. You may transform the data with percent transformation, but not with z-transformation, because the algorithm is not suited for z-transformed data.
Please make also sure that the data is not smoothed or interpolated in advance, because the algorithm assumes approximately uncorrelated data in space and therefore keeping correlation to a minimum is essential.

Parameter settings

The current GLM document and the format (VMR/FMR) is shown for information only.

Adjusting visualization

The plugin will automatically replace the result of the BrainVoyager GLM analysis. It shows the segments of voxels where the test detected a value significantly larger (red) or lower (blue) than +/- delta at significance level alpha. The color codes beta values (percent values) NOT p-values. As there might occur single significant voxels with very large values, you can adjust the color contrast in the "Analysis -> Overlay Volume Maps..."-dialog (see Map Options tab) of BrainVoyager.

Authors

Algorithm: Karsten Tabelow, Jörg Polzehl, WIAS
Implementation: Felix Anker, WIAS