pTFCE for analyses in R

[dcdeaniii]

Hi,

Thank you for creating this tool! I'm thinking about how to integrate this into some analyses/tools that we are currently using and I want to make sure I'm thinking about using pTFCE the correct way. If performing a voxel-based analysis (e.g. 2-sample t-test or regression) in R, for example, it seems like the workflow would be to create the t-stat map for the contrast of interest, convert this to a Z map and then apply the pTFCE as described in the usage instructions (similar to SPM). Is this correct? It also seems like pTFCE could be used beyond voxel-based analyses, for example surface or tract based analyses or even analyses of multiple regions of interest? (keeping in mind to use multiple comparison corrected results). Has there been any work done using pTFCE in these areas? Given pTFCE can directly output the p-values without permutation testing, are there disadvantages of pTFCE compared TFCE and permutation testing?

Thanks in advance! Much appreciated!
Doug

[spisakt]

Hi Doug,

Sorry for the late reply!
Yes, what you write about the usage is 100% correct.

You are also right that pTFCE could be used for other topologies, besides 3d images (like surfaces, TBSS-like skeletons, networks, etc).
All this is work in progress, and unfortunately, the progress is right now slow due to a shift in priorities. Collaborations would be very welcome here.
As a first step, we have extended pTFCE to account for spatial inhomogeneities in images smoothness. We are in the process of drafting the manuscript about that (basically it shows that pTFCE is quite robust to this even is we don't account for inhomogeneous smoothness).

Re.: comparison to permutation test-based p-values: I don't really know of any serious disadvantages with "regular" neuroimages... Of course permutation testing may be more robust to violations of Gaussianity.

Hope this is helpful,
Tamas

[dcdeaniii]

Thank you Tamas for this reply. This is very helpful! Thanks again! Doug

…

On Jul 1, 2022, 10:10 AM -0500, Tamas Spisak ***@***.***>, wrote: Hi Doug, Sorry for the late reply! Yes, what you write about the usage is 100% correct. You are also right that pTFCE could be used for other topologies, besides 3d images (like surfaces, TBSS-like skeletons, networks, etc). All this is work in progress, and unfortunately, the progress is right now slow due to a shift in priorities. Collaborations would be very welcome here. As a first step, we have extended pTFCE to account for spatial inhomogeneities in images smoothness. We are in the process of drafting the manuscript about that (basically it shows that pTFCE is quite robust to this even is we don't account for inhomogeneous smoothness). Re.: comparison to permutation test-based p-values: I don't really know of any serious disadvantages with "regular" neuroimages... Of course permutation testing may be more robust to violations of Gaussianity. Hope this is helpful, Tamas — Reply to this email directly, view it on GitHub, or unsubscribe. You are receiving this because you authored the thread.Message ID: ***@***.***>