help
help > RE: Masking
Jul 10, 2020 04:07 PM | Mayron Pereira Picolo Ribeiro
RE: Masking
Hi Alfonso,
I was wondering if this has been added to any later version of CONN
Thanks
Mayron
Originally posted by Alfonso Nieto-Castanon:
I was wondering if this has been added to any later version of CONN
Thanks
Mayron
Originally posted by Alfonso Nieto-Castanon:
Dear
Julia,
That is a good idea. I will see if we can add masking and the associated small volume correction to the second-level analysis options. In the meantime there are two possibilities for you to run these analyses. Both start by creating a mask in your 'default mode network' analyses with the supra-threshold results (just click on 'export mask' on the corresponding CONN 'seed-to-voxel results explorer' analyses). After this you could:
1) In SPM 'Results' load the SPM.mat file generated by CONN for your "new covariate" analysis, and when prompted click on the 'apply masking: image' option and select the previously generated mask file. That will compute your seed-to-voxel analyses for your "new covariate" effects reduced to only 'default mode network' areas.
2) Launch REX (just type rex in Matlab command window). On 'Sources' select the "new covariate" analysis SPM.mat file. On 'ROIs' select the previously-generated mask file. Then select the option 'Cluster-level' (to perform the analyses separately for each "default mode network" cluster), and click on 'Extract' and (when the first step finishes) on 'Results'. That will perform ROI-level analyses for your "new covariate" effects, using as ROIs each of the individual clusters that appeared in your original "default mode network" analyses.
Last, regarding the 'validity' of this sort of analyses, as long as your original analysis (the 'default mode network' contrast) and your second analyses (the "new covariate" effects) are orthogonal/independent this sort of analyses are perfectly valid. If they are not, then these analyses may include selection biases and are to be considered with care.
Hope this helps
Alfonso
Originally posted by Julia Landsiedel:
That is a good idea. I will see if we can add masking and the associated small volume correction to the second-level analysis options. In the meantime there are two possibilities for you to run these analyses. Both start by creating a mask in your 'default mode network' analyses with the supra-threshold results (just click on 'export mask' on the corresponding CONN 'seed-to-voxel results explorer' analyses). After this you could:
1) In SPM 'Results' load the SPM.mat file generated by CONN for your "new covariate" analysis, and when prompted click on the 'apply masking: image' option and select the previously generated mask file. That will compute your seed-to-voxel analyses for your "new covariate" effects reduced to only 'default mode network' areas.
2) Launch REX (just type rex in Matlab command window). On 'Sources' select the "new covariate" analysis SPM.mat file. On 'ROIs' select the previously-generated mask file. Then select the option 'Cluster-level' (to perform the analyses separately for each "default mode network" cluster), and click on 'Extract' and (when the first step finishes) on 'Results'. That will perform ROI-level analyses for your "new covariate" effects, using as ROIs each of the individual clusters that appeared in your original "default mode network" analyses.
Last, regarding the 'validity' of this sort of analyses, as long as your original analysis (the 'default mode network' contrast) and your second analyses (the "new covariate" effects) are orthogonal/independent this sort of analyses are perfectly valid. If they are not, then these analyses may include selection biases and are to be considered with care.
Hope this helps
Alfonso
Originally posted by Julia Landsiedel:
Dear all & Alfonso,
I have a question whether it would be possible to include the following feature in a new release of the toolbox:
Is it possible and also valid to mask second level results just as in SPM? I mean, for example I first identify the default mode network, then save a binary mask of the clusters and then mask another contrast, e.g. looking at the effect of a covariate with this binary mask.
Thanks a lot.
Best,
Julia
I have a question whether it would be possible to include the following feature in a new release of the toolbox:
Is it possible and also valid to mask second level results just as in SPM? I mean, for example I first identify the default mode network, then save a binary mask of the clusters and then mask another contrast, e.g. looking at the effect of a covariate with this binary mask.
Thanks a lot.
Best,
Julia
Threaded View
| Title | Author | Date |
|---|---|---|
| Julia Landsiedel | Jul 21, 2014 | |
| Alfonso Nieto-Castanon | Jul 22, 2014 | |
| Kaitlin Cassady | Sep 5, 2023 | |
| Mayron Pereira Picolo Ribeiro | Jul 10, 2020 | |
| Julia Landsiedel | Jul 22, 2014 | |
| Alfonso Nieto-Castanon | Jul 29, 2014 | |
| Julia Landsiedel | Jul 30, 2014 | |
| Yifei Zhang | Aug 1, 2014 | |
| Yifei Zhang | Aug 5, 2014 | |
| Alfonso Nieto-Castanon | Aug 6, 2014 | |