Many ask “how to do Topological FDR” in SPM ML. Below are the posts I found useful.
Originally Guillaume Flandin showed how to do that.
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;44838216.0904
The height threshold you define during the Results procedure is the feature-inducing threshold (used to define local maxima and clusters).
For topological FDR, I would typically choose “none” and a small T-value, for example 3.
The MIP will display all voxels whose T-value is above 3 and the Results table will display all local maxima whose T-value is above 3 (actually, at most 3 local maxima per cluster, more than 8mm apart) and the corresponding FWE p-values and FDR q-values for peaks and clusters.If you want to threshold the MIP to a particular significance level, go through the Results procedure again and enter the corresponding height and extent thresholds (the 0.05 critical thresholds are indicated at the bottom left of the Results table):
(height) FWEp and FDRp for peak-FWE and -FDR.
(extent) FWEc amd FDRc for cluster-FWE and -FDR.
Jonathan Peele also gave a detail instruction of how to do that. This time he focused more on cluster-thresholded FDR.
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;6f4e0139.1110
> How can I see images with a threshold at the cluster-level instead of the peak level with the graphical user interface?
I’m not aware of a way to set a cluster-level threshold when running results the first time (i.e., “show everything that is cluster-level corrected p < .05"). What I would do is this: 1) Run results using some voxelwise threshold. This threshold defines your clusters, and it's fine to use an uncorrected threshold (e.g., p< .001, uncorrected). 2) In the results table, for each cluster you will see the cluster extent (i.e. how many voxels) and the cluster-corrected p value. In SPM8 you will see 2 cluster-corrected p values, one for topological FDR (Chumbley & Friston, 2009), one for random field theory (labeled FWE). You can then look at the size of those clusters, and figure out the cluster extent that divides p < .05 from the others. For example, if a cluster of 300 voxels is p = .10, and a cluster of 400 voxels is p = .04, then you know that any cluster 400 voxels or larger would be p < .05. 3) Re-run the results, selecting the same voxelwise threshold. But now, in the minimum cluster extent, specify the cluster size you just figured out—in the above example, you could specify 400.
4) Now all of the displayed results should be cluster-level corrected.
5) If you want to save this thresholded image for displaying with another program, just click the “save” button, and you can write out a thresholded t map as a nifti file.
In practice this is actually fairly quick to do once you’ve done it a couple of times.
Note the difference between these two. Two-step approach is the same, but Guillaume’s way deals with peak and cluster threshold and Jonathan’s way deals with cluster threshold.
