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aws4SPM - Structure adaptive smoothing in SPM

Introduction

A very popular software tools for analyzing functional Magnetic Resonance Imaging data is SPM by the Functional Imaging Laboratory at the Wellcome Department of Imaging Neuroscience. In order to enhance the signal-to-noise ratio it provides the possibility to smooth the data in a pre-processing step by a Gaussian filter. However, this comes at the cost of reducing the effective resolution, which is especially disturbing at high spatial resolutions. In a series of recent papers we have shown, that using a structural adaptive smoothing algorithm based on the Propagation-Separation method allows for signal detection while preserving the shape and spatial extent of the activation areas. Here, we provide our implementation of this algorithm as a toolbox for SPM.

Signal detection in fMRI may be based on the Statistical Parametric Map. We consider a voxel as activated if the corresponding parameter value exceeds a critical value or threshold. The definition of the threshold involves a severe multiple test problem. Voxelwise thresholds based on a t-distribution for the parameter lead to a large number of false positive activations due to the large number of voxels in the data cube. On the other hand the application of the Bonferroni correction does not account for the spatial correlation structure of the data. Random Field Theory setting a family-wise error (FWE) can therefore be used to correct the thresholds for the number of independent tests in the data. However, due to the low signal-to-noise ratio, this may fail to detect many of the activations: The high noise level may lead to t-values below the threshold, although there is activation at this voxel. In situations where activations have a spatial extent, spatial smoothing and thus reducing noise has the potential to improve both overall sensitivity and specificity of signal detection.

The application of a Gaussian filter to the three dimensional images of the fMRI scan as implemented in SPM has mainly two effects. Firstly, if activation has spatial extent adjacent voxel will contain similar time courses and thus the averaging lowers the noise level while conserving the signal. Secondly, it increases the smoothness of the random t-field and thus lowers the number of independent tests as well as the corresponding global thresholds. However, with Gaussian smoothing we can achieve a reduction in the signal-to-noise ratio at the cost of a possible bias at the border of the activated regions only. This leads to an increased power of the tests and therefore more sensitivity and specificity of signal detection in all voxel except in a neighborhood of the border of the region. The size of this neighborhood depends on the amount of smoothing that is applied. In this neighborhood specificity of signal detection may decrease. The optimal amount of smoothing depends on both signal strength and spatial extent.

In [1] we designed an algorithm for structure adaptive smoothing fMRI data based on the Propagation Separation approach. The idea behind structural adaptive smoothing is to use information on the structures of interest in the smoothing algorithm for locally modeling the data. If, for example an anatomical image is disturbed by noise, the interesting structures may be the different tissues, which are characterized by a certain grey level value. Thus locally the data can be described by a constant. This information can be used in our structural adaptive smoothing algorithm, which preserves shape and border of this interesting structures. For fMRI the structures of interest are not found in a single three dimensional image of the time series, but in the value of the BOLD parameter or the estimated effect. We expect this parameter to differ significantly from zero in activated areas and to be consistent with zero elsewhere. [1] therefore suggested to apply the structural adaptive smoothing to the Statistical Parametric Map after estimating the parameters from the linear model. This smoothing algorithm is implemented in this toolbox

Our Project

The toolbox has been developed by Devy Hoffmann and Karsten Tabelow at the Weierstrass Institute for Applied Analysis and Stochastics within the Project "A3 - Image and signal processing in medicine and biosciences" of the DFG Research Center MATHEON.

Note

This toolbox comes with absolutely NO WARRANTY! It is not intended for any purpose! It is especially not intended for any clinical use, but for evaluation purpose only.

Getting started

The toolbox aws4SPM requires Matlab and SPM2 to be installed on your computer. After downloading the toolbox archive, extract the files into your SPM working directory. The archive contains a C-file "smAd.c" which has to be compiled before using. In order to do this launch Matlab in your working directory. Start the compilation by entering the command

mex smAd.c

Now the installation is complete and you can start SPM and use the toolbox.

Documentation can be found in WIAS Technical Report No. n/a and together with the downloaded toolbox. This may contain a newer version corresponding to the version of the toolbox.

Related software

Here, we want to point the attention towards an implementation of the structure adaptive smoothing algorithm as described in [1] in R Language and Environment for Statistical Computing which has more features and is under constant development. The package is called fmri and freely available for download at CRAN as well as at NITRC.org.

Download

Note, that the tar-files of version 0.4.1 had some problems with missing bytes! Please upgrade!

SPM Version	Download
SPM2	version 0.4.3
	version 0.4.2
SPM5	version 0.4.3
	version 0.4.2
SPM8	version 0.4.4
	version 0.4.3
	version 0.4.2

Literature

1. K. Tabelow, J. Polzehl, H.U. Voss, and V. Spokoiny. Analyzing fMRI experiments with structural adaptive smoothing procedures, NeuroImage 33(1), 55-62 (2006).
2. J. Polzehl, K. Tabelow. fmri: A package for analyzing fmri data, RNews 7(2), 13-17 (2007).
3. H.U. Voss, K. Tabelow, J. Polzehl, O. Tchernichovski, K. Maul, D. Salgado-Commissariat, D. Ballon, and S.A. Helekar, Functional MRI of the zebra finch brain during song stimulation suggests a lateralized response topography, Proceedings of the National Academy of Sciences (PNAS) 104(25), 10667-10672 (2007).
4. K. Tabelow, J. Polzehl, A. M. Ulug, J. P. Dyke, R. Watts, L. A. Heier, and H. U. Voss. Accurate Localization of Brain Activity in Presurgical fMRI by Structure Adaptive Smoohting, IEEE Trans. Med. Imaging , 27(4), 531-537 (2007).
5. D. Hoffmann, K. Tabelow. Structural adaptive smoothing for single-subject analysis in SPM: the aws4SPM-toolbox, WIAS Technical Report No. 11,(2008)
6. K. Tabelow, V. Piech, J. Polzehl, and H. U. Voss. High-resolution fMRI: Overcoming the signal-to-noise problem, Journal of Neuroscience Methods 178(2), 357-365 (2009).

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Karsten Tabelow

Last modified: Fri May 28 12:43:50 CEST 2010