Dear Alfonso,
For the needs of my current project, I dwelved more in-depth in the intricacies of multiple comparison correction and the various solutions currently available, as well as the latest debates on the matter.
I have a few questions following my reading of Eklund et al's paper "Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates" and the follow-up blog post "[url=https://www.ohbmbrainmappingblog.com/blog/keep-calm-and-scan-on]Keep Calm and Scan On[/url]":
1. Since CONN is using SPM under the hood, I guess CONN also relies on RFT for inference, and thus is subject to the inflated rate described in the Eklund et al's paper when one is using parametric cluster-wise correction, right?
2. According to Eklund et al's, this inflated rate should be mainly due to assumption about the constancy of smoothing across the brain, particularly wrong for voxels that are far apart. Knowing that CONN has an additional "Denoising" step that Eklund et al did not test, and that this denoising step clearly corrects for signal variation across voxels distances (as shown by the middle preview plot in the Denoising tab, see attached picture below), does this mean that CONN is significantly less affected by this issue, since the denoising ensures more constant signal across the whole brain?
Thank you very much in advance!
Warm regards!
Stephen
[img]https://i.imgur.com/l1xXAKr.jpg[/img]
For the needs of my current project, I dwelved more in-depth in the intricacies of multiple comparison correction and the various solutions currently available, as well as the latest debates on the matter.
I have a few questions following my reading of Eklund et al's paper "Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates" and the follow-up blog post "[url=https://www.ohbmbrainmappingblog.com/blog/keep-calm-and-scan-on]Keep Calm and Scan On[/url]":
1. Since CONN is using SPM under the hood, I guess CONN also relies on RFT for inference, and thus is subject to the inflated rate described in the Eklund et al's paper when one is using parametric cluster-wise correction, right?
2. According to Eklund et al's, this inflated rate should be mainly due to assumption about the constancy of smoothing across the brain, particularly wrong for voxels that are far apart. Knowing that CONN has an additional "Denoising" step that Eklund et al did not test, and that this denoising step clearly corrects for signal variation across voxels distances (as shown by the middle preview plot in the Denoising tab, see attached picture below), does this mean that CONN is significantly less affected by this issue, since the denoising ensures more constant signal across the whole brain?
Thank you very much in advance!
Warm regards!
Stephen
[img]https://i.imgur.com/l1xXAKr.jpg[/img]