Hi,
We're pleased to annouce the next release of SnPM, version 3 beta. In
the new version we have improved cluster size methods and "under the
hood" changes that make SnPM easier to use with other programs
(specifically, all results are written out as Analyze images, not as
.mat files.)
See below for a summary of changes. For more info and to download,
see
http://www.sph.umich.edu/ni-stat/SnPM/
Please contact [log in to unmask] with any problems, issues, or
ideas on how to improve SnPM.
-The SnPM development team
Jun Ding, Satoru Haysaka, Andrew Holmes, Tom Nichols
# Improved cluster size inference
Previously in SnPM, cluster size inference was very slow and
resulting in a gigantic SnPM_ST.mat (2GB or more!) being
created. Now, a cluster-defining threshold can be set before the
computation stage, avoiding the gigantic data file. The result is
the capability of analyzing much larger images and faster 'Results'
processing.
Note, however, if you prefer, you can still use the old method; in the
'Configure' step, if you elect to "Collect Supra-Threshold stats" and
choose not to "Define threshold now", then an SnPM_ST.mat file will be
created and you may set the threshold in the Results step.
# Nonparametric uncorrected cluster-level P-values
Previously SnPM only had corrected P-values for cluster size
inference. Based on an assumption of stationarity, or homogeneous
smoothness, SnPM now offers cluster-level uncorrected P-values.
# Parameter estimates and statistic values saved in images
A significant improvement of SnPM3 is that all parameter estimates
and statistic images are written out as images instead of being
stored in .mat files. This makes it easier to take the results of
SnPM and interrogate the results in other programs.
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