Hi everyone,

I have only very basic knowledge about statistics, I apologize in advance if the answer to my question is obvious.
Regarding the settings used for statistical analysis in Conn (voxel threshold uncorrected <0.001, cluster threshold <0.05 with cluster-size p-FDR correction), I don't really understand what are the implications of the articles of Eklund et al regarding parametric cluster size inference (Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates (2016) and Correction for this paper ;Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates (2018)).
I have read the previous topic about the question (below) but I don't understand one point.
In the most recent article, the authors said "Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings".
However, the threshold used in Conn is 0.05 (but with FDR correction).
For a common analysis of 2 independent groups with ICA, should we prefer non-parametric stats in any case? Should we use a less liberal cluster threshold of 0.001 if we use parametrical stats?

Thanks a lot for your help and sorry if I got something wrong,

Mickael


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Previous topic 
"Hi Jeff&Mike,

My reading from the Eklund et al. and Flanding&Friston papers is that if you are using a voxel-wise height threshold p<.001 (this is the default threshold both in SPM and in CONN) then using parametric statistics is perfectly fine, while nonparametric statistics are recommended when you want to use higher (i.e. more liberal) voxel-wise height thresholds (e.g. p<.01, in order to focus on perhaps weaker but large/distributed responses). So yes, either one of the following two approaches should be perfectly fine (the former being more sensitive for strong localized effects, while the latter being more sensitive for weak distributed effects):
height threshold p-unc<.001, cluster-size threshold p-FDR<.05, parametric statistics
height threshold p-unc<.01, cluster-mass threshold p-FDR<.05, non-parametric statistics
Regarding how to use non-parametric statistics in CONN, simply select 'non-parametric statistics' in the explorer window top-right corner menu. Everything works as in the 'parametric statistics' case, only now the choice of statistics being displayed and the associated thresholds that you can use are slightly different. In particular, the information displayed for each cluster when selecting parametric statistics is:
cluster position: MNI coordinates of largest peak within this cluster
cluster size: number of voxels in this cluster
cluster p-FWE: family-wise error corrected p-value (probability under the null hypothesis of observing one or more clusters of at least this size across the entire brain)
cluster p-FDR: false discovery rate corrected p-value (expected proportion under the null hypothesis of false discoveries among clusters of at least this size)
cluster p-unc: uncorrected p-value (probability under the null hypothesis of a randomly-selected cluster having at least this size)
peak p-FWE: family-wise error corrected p-value (probability under the null hypothesis of observing one or more peaks of at least this height across the entire brain)
peak p-unc: uncorrected p-value (probability under the null hypothesis of a randomly-selected peak having at least this height)
all of the above p-values are obtained using random-field-theory (RFT) assumptions.
When selecting instead non-parametric statistics (and after the corresponding permutation/randomization tests are run) you will then get the following information for each cluster:
cluster position: MNI coordinates of largest peak within this cluster
cluster size: number of voxels in this cluster
cluster p-FWE: family-wise error corrected p-value (probability under the null hypothesis of observing one or more clusters of at least this size across the entire brain)
cluster p-FDR: false discovery rate corrected p-value (expected proportion under the null hypothesis of false discoveries among clusters of at least this size)
cluster p-unc: uncorrected p-value (probability under the null hypothesis of a randomly-selected cluster having at least this size)
cluster mass: sum of statistics (F-values or T^2 values) across all voxels within this cluster
cluster p-FWE: family-wise error corrected p-value (probability under the null hypothesis of observing one or more clusters of at least this mass across the entire brain)
cluster p-FDR: false discovery rate corrected p-value (expected proportion under the null hypothesis of false discoveries among clusters of at least this mass)
cluster p-unc: uncorrected p-value (probability under the null hypothesis of a randomly-selected cluster having at least this mass)
and all of the above p-values are obtained using non-parametric assumptions (permutation/randomization analyses). Cluster mass statistics combine information about each cluster size as well as each cluster height/strength, so they are generally considered more sensitive than either cluster-size or peak-level statistics. Typically for non-parametric statistics I would recommend using a cluster-mass p-FDR<.05 threshold by default, since that should typically be one of the most sensitive tests, but your preferences might vary.
Hope this helps
Alfonso"