By Satoru Hayasaka ANSIR Lab
Wake Forest University School of Medicine
Winston-Salem, North Carolina, USA

This toolbox implements the random field theory (RFT) version of cluster size inference under non-stationarity [1].

Non-stationarity, or non-uniform smoothness, is a problem in cluster size inference of brain imaging data. This is because, under non-stationarity, clusters tend to be large in smooth areas and small in rough areas within the image data. Non-stationarity is particularly problematic in VBM (voxel-based morphometry) data, and a use of cluster p-values has been discouraged in analyses of such data [2].

A solution for this non-stationarity problem was proposed by Worsley et al [3], and has been implemented in the FMRISTAT package. Accounting for non-stationarity is important as it could lead to an erroneous outcome of a VBM analysis, as demonstrated in Moorhead et al [4]. We have ported the function for non-stationary cluster size inference from FMRISTAT to SPM, so that it can interface with the SPM output.

Disclaimer This toolbox is still a beta version. The toolbox is distributed as is, without any warranty. Installation This package comes in a tar-gzip file [17KB] (for Unix/Linux) or in a zip file [19KB] (for PC). To install, you save the downloaded archive file under the toolbox folder in your SPM. Then untar / unzip the file. A folder named "ns" will be created. Usage This toolbox functions much like the RESULTS button in SPM. See README for more details.

Please note that this release is still a beta version. Thus it is quite possible that there are some bugs, typos, and other errors in the scripts. If you find any bugs, or if you have any questions / complaints / suggestions, you can contact me (Satoru Hayasaka) at shayasak_at_wfubmc_dot_edu.

DATE: Feb 19, 2007
BUG: Variable nvar not integer.
DETAILS: Variable nvar in stat_thresh function is treated as a double rather than an integer. This causes some warnings and errors in the program.
FIX: In stat_thresh function, nvar is rounded so that it will be treated as an integer.
UPDATE: stat_thresh has been updated. The latest distribution includes the latest version (ver 0.76b) of stat_thresh function.

DATE: Mar 28, 2007
BUG: Cluster p-values not available for F-images
DETAILS: This is not a bug, but a shortcoming in SPM. SPM cannot calculate cluster p-values for F-tests.
FIX: Cluster p-values from from stat_thresh function [2,3] are passed on and displayed in the output. The p-values are corrected for non-sationarity.
UPDATE: spm_list_nS function has been updated to implement the change described above. The latest distribution includes the latest version (ver 0.8b) of spm_list_nS.

DATE: Mar 28, 2007
BUG: Toolbox pull-down menu
DETAILS: Now this package can be invoked from the 'toolbox' pull-down menu if installed appropriately.
UPDATE: The latest distribution includes spm_ns.m, which drives this whole package.

DATE: Apr 17, 2007
BUG: Variables hReg, xSPM, SPM are not available
DETAILS: After running the non-stationarity extension, the variables hReg, xSPM, and SPM are not available.
UPDATE: The latest distribution outputs these variables in the workspace, so users can click on other buttons ('volume', 'cluster', etc) after running the non-stationarity extension.

DATE: Apr 17, 2007
BUG: Set-level results taking too much space
DETAILS: In the SPM output, the set-level inference results are taking up two columns, although very few people use the set-level p-value.
UPDATE: The updated version of spm_list_nS (ver 0.81b) eliminated the first two columns of the set-level results. The set-level results are now displayed in the footer for interested users.

DATE: Feb 11, 2008
BUG: Cluster p-values unavailable for F-contrasts under stationarity
DETAILS: The NS toolbox assumes non-stationarity. Cluster sizes are adjusted based on RPV no matter what.
UPDATE: The updated version of spm_list_nS (ver 0.82b) can calculate cluster p-values, both for T- and F-contrasts under stationarity, provided that a global variable ASSUME_STATIONARY=1. Otherwise cluster p-values are calculated assuming non-stationarity. In the results, the table header for clusters displays whether cluster p-values are calculated under stationarity or non-stationarity.

[1]. Hayasaka S, Phan K L, Liberzon I, Worsley K J & Nichols T E. Nonstationary cluster-size inference with random field and permutation methods. NeuroImage 22: 676-687 (2004)
[2]. Ashburner J & Friston K J. Voxel-based morphometry --- the methods. NeuroImage 11: 805-821 (2000)
[3]. Worsley K J, Andermann M, Koulis T, MacDonald D & Evans A C. Detecting changes in nonisotropic images. Human Brain Mapping 8: 98-101 (1999)
[4]. Moorhead T W J, Job D E, Spencer M D, Whalley H C, Johnstone E C & Lawrie S M. Empirical comparison of maximal voxel and non-isotropic adjusted cluster extent results in a voxel-based morphometry study of comorbid learning disability with schizophrenia. NeuroImage 28: 544-552 (2005)

The theoretical work, as well as the main statistical programming was done by Dr. Keith Worsley of McGill University. I would like to thank him for the permission to port his STAT_THRESHOLD function into SPM.

Also I would like to thank Dr. Tom Nichols for useful discussions during the development of this toolbox.

This Human Brain Project / Neuroinformatics research (R01-MH069326) is supported by the National Institute of Mental Health, the National Institute on Aging, and the National Institute of Biomedical Imaging and Bioengineering.