Assuming Worsley did not make any changes to his basic method since 2003 (the version I copied), then the difference here is probably, as you said, because of the resels.
If you use resels, then the cluster thresholds are in resels and the
surface area of two clusters with the same number of resels can be different (by design). If you want your threshold
to be a constant mm^2, don't use resels.
His function has an option to either supply the total surface area in
mm^2 or in resels. If you specify in resels, then you area supossed to set fwhm = 1. This is in the comments of his function that is buried in mine. I haven't used his most recent toolbox. Does it give the option to specify surface area and fwhm in mm^2?
By the way, when I made surface maps of resels I calculated for my own surface data, they looked very spiky and I decided not to use them, but instead made the simplifying assumption of uniform smoothness across the surface.
Date: Fri, 3 Apr 2009 11:03:04 +0800
From:
jidanz@gmail.comTo:
freesurfer@nmr.mgh.harvard.edu
Subject: [Freesurfer] determining sizes of clusters on surface data
Hi,
I was trying to figure out if there is any way to determine the exact size at which clusters becomes significant given a specified significance on the surface data.
Then i found there is a subject talking about this: https://mail.nmr.mgh.harvard.edu/pipermail//freesurfer/2007-August/005855.html
In that talk,Don Hagler mentioned Keith Worsley's "Random Field Theory" method (http://www.math.mcgill.ca/keith/fmristat/toolbox/stat_threshold.m) can be used to directly (in seconds) estimate the cluster size thresholds given:
..total surface area
..number of vertices
..fwhm smoothness.
function [cluster_threshold,peak_threshold] = fs_calc_cluster_thresh(nverts,area,fwhm,df,alpha,pval);
> % alpha: corrected p-value
> % {default = 0.05}
> % pval: uncorrected p-value
> % {default = 0.001}
I tried this function, and it does give me a cluster size with mm^2, say 100 mm^2. But I don't know whether this is the right thing to do.
As I'm doing some statistical analysis using GLM, so I also try the method from Keith Worsley's statistical toolbox, then I can get the cluster's No. of vertices and the corrected p-value(alpha=0.05) for the cluster.
Here is one example:
clusid nverts resels P
---------------------------------------
1 2205 18.2781 2.1299e-06
2 208 1.42105 0.013359
3 127 1.15533 0.026747
4 58 1.0443 0.036658
5 5 0.519762 0.20884
6 105 0.464429 0.2571
The thing I don't understand is , if we are looking for a cluster size threshold, then we can say that, the clusters above that threshold size is what we want to keep. And also, the p-value of the clusters above that threshold size should
be below alpha we set, say 0.05. But when checking the tale above, the first four clusters' p- value is smaller than 0.05, then we should keep it. But according to the no. of vertices, we can caculate the cluster size in mm^2. Then the 4th
cluster size is obviously below 100mm^2, the threshold we found using the function fs_calc_cluster_thresh. Oppositely, the 6th cluster has a large no. of vertices and its cluster size is above 100mm^2, while it's
not kept because its p value is above 0.05.
I know what happened here is because of the 'resels', but it really confuse me which one is the right one. Since you said we can use the function, then why is the result conflicted with each other?
Could you tell me what's wrong here or what did I miss ?
Thanks a lot,
Jidan
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