This email to the FSL mailserver list may be of interest to local YNiC FSL users
Gary
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Dear David,
The short answer is, no, there is no way to justify Z=1.7 as a cluster-forming threshold in FEAT.
The problem is that the cluster size P-values are based on Random Field Theory (RFT), and RFT makes various approximation that are only valid for high thresholds. While one early reference (Petersson et al, 1999) specified cluster-forming threshold P=0.01 / Z=2.33 as a practical lower limit for accurate results, later work (Hayasaka & Nichols, 2003; Silver et al, 2010; K. Worsley personal communication) found even that level was unstable, and instead recommended P=0.001 / Z=3.09 as a lower limit on a cluster-forming threshold to ensure accurate inferences.
On the other hand, if you are using randomise, you can safely use any cluster-forming threshold (though if you go too low, the clusters might be too extended and spindly to be interpretable). If using randomise, though, check out TFCE as away to avoid specifying any particular cluster-forming threshold.
To answer your second question, the cluster-forming threshold used at lower-levels is only used to make inferences *at* the lower level, and is ignored at higher levels. Only the cluster-forming threshold specified in the top-level FEAT analysis matters for the top-level results.
-Tom
Petersson, K. M., Nichols, T. E., Poline, J.-B., & Holmes, A. P. (1999). Statistical limitations in functional neuroimaging II. Signal detection and statistical inference. /Phil. Trans. R. Soc. Lond. B/, /354/, 1261-1281.
Hayasaka, S., & Nichols, T. (2003). Validating cluster size inference: random field and permutation methods. /NeuroImage/, /20/, 2343-2356.
Silver, M., Montana, G., & Nichols, T. E. (2010). False positives in neuroimaging genetics using voxel-based morphometry data. /NeuroImage/. Elsevier Inc. doi: 10.1016/j.neuroimage.2010.08.049.
On Mon, May 9, 2011 at 1:06 PM, David Soto <d.soto.b@gmail.com mailto:d.soto.b@gmail.com> wrote:
Hello,
I am finding that when I use a Z<1.7 to define the cluster size at he highest level analyses, I am getting some interesting activations in regions that I don't see when I use Z<2.3. In both cases I use cluster thresholding and p<0.05 whole brain corrected.....
I know the Z value used to define cluster size prior to correction for multiple comparisons is arbitrary, but is it there any paper that I can use to justify in my study why a Z<1.7 was used instead of Z<2.3?
Would it be right to say that poststat results with a Z<1.7 are more lenient than with a Z<2.3? I feel it this is not necessarily right but can you please advise?
A second question I have is about poststats as implemented in FEAT.... Say that I have done a lower level analyses - for session, a 2nd level -across session within subjects- and 3rd level -across subjects-
do I need to set up the Z<1.7 at the first and second level and third level analyses - or does FEAT bring the unthresholded data from lower level analyses to the higher levels and then use the Z score specified at the highest level analyses? in other words, will I get the same results if I specify Z<1.7 across all levels or whether I do Z<2.3 for first and second level and Z<1.7 at the higher level?
Many thanks,
David