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help > RE: Different baselines and contrasts
Jun 29, 2016 03:06 AM | Alfonso Nieto-Castanon - Boston University
RE: Different baselines and contrasts
Hi Lucas/Jeff,
While I totally agree with Jeff on how you would do these analyses, I would like to double-check why exactly you would want to perform that masking. To give some context to this, when looking at a Group-by-Condition interaction (e.g. [1 -1] contrast across drug/placebo groups, and [1 -1] contrast across pre/post scans) those differences are always orthogonal to the corresponding main group and condition effects (i.e. differences between the drug/placebo groups during both the pre-intervention and post-intervention scans, or differences between the pre/post scans in both the drug and placebo groups), so an interaction effect cannot arise solely from a difference between the drug/placebo groups at baseline (those differences need either to not appear in the post-intervention scans, or need to appear in different magnitude in the post-intervention scans, for an interaction to be present). The conjunction analysis that Jeff describes allows you to exclude any regions where the connectivity might be different between your two groups in the pre-intervention scans, but just note that those differences alone are not "causing" or "explaining" the interaction effects, since those interactions indicate that those between-group differences are being modulated by the intervention, whether they were present at baseline or not. This raises the question of why exactly would you want to mask-out those regions that show pre-treatment differences in connectivity between your drug/placebo groups when analyzing the somewhat-orthogonal question of which regions show differences in the post- vs. pre-treatment changes in connectivity between your drug/placebo groups.
As a only-partially-related comment (feel free to skip/disregard this paragraph), it is true that in pre/post drug/placebo designs where the placebo/drug group assignment is random there are no reasons to expect a pre-treatment difference between the groups beyond sampling/random effects. In this case it is often argued that an ANCOVA analysis, where pre-treatment measures are used as a covariate when analyzing between-group differences in post-treatment measures, might be preferable over a repeated-measures/MANOVA analysis (both analyses will typically lead to similar results and interpretation, but the power in the former analysis is expected to be larger than in the latter, particularly in the case of low correlations between pre- and post- treatment measures; see for example Rausch et al. 2003, Analytic Methods for Questions Pertaining to a Randomized Pretest, Posttest, Follow-Up Design). If, on the other hand, the group assignment is not random then the two analyses can potentially lead to different results, and the differences arise from the different interpretation of these analyses: repeated-measures ANOVA/MANOVA allows you to look at the intervention "change" effects (differences in connectivity between post- and pre-intervention scans) and determine whether those changes are different in the control vs. placebo groups, while ANCOVA, on the other hand, allows you to look at the post-intervention connectivity values, and determine whether those are different in the control vs. placebo groups when comparing subjects at the same level of pre-intervention connectivity. In this case (non-random assignment) repeated-measures/MANOVA analyses are often recommended instead, though this may depend on the specifics of the research questions that one is interested in, and the source of pre-treatment differences between the groups. In practice, in fMRI analyses I have not really seen ANCOVA models used much in this context (perhaps mostly due to this sort of models requiring voxelwise regressors which are not that widely implemented/known?; just for reference using FSL's FEAT would allow you to do this, and we are working on an equivalent implementation in CONN) so most people seem to be using the (perhaps less powerful in some context but perhaps also more generally valid) repeated-measures/MANOVA approach instead irrespective of the potential source of pre-treatment differences.
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
While I totally agree with Jeff on how you would do these analyses, I would like to double-check why exactly you would want to perform that masking. To give some context to this, when looking at a Group-by-Condition interaction (e.g. [1 -1] contrast across drug/placebo groups, and [1 -1] contrast across pre/post scans) those differences are always orthogonal to the corresponding main group and condition effects (i.e. differences between the drug/placebo groups during both the pre-intervention and post-intervention scans, or differences between the pre/post scans in both the drug and placebo groups), so an interaction effect cannot arise solely from a difference between the drug/placebo groups at baseline (those differences need either to not appear in the post-intervention scans, or need to appear in different magnitude in the post-intervention scans, for an interaction to be present). The conjunction analysis that Jeff describes allows you to exclude any regions where the connectivity might be different between your two groups in the pre-intervention scans, but just note that those differences alone are not "causing" or "explaining" the interaction effects, since those interactions indicate that those between-group differences are being modulated by the intervention, whether they were present at baseline or not. This raises the question of why exactly would you want to mask-out those regions that show pre-treatment differences in connectivity between your drug/placebo groups when analyzing the somewhat-orthogonal question of which regions show differences in the post- vs. pre-treatment changes in connectivity between your drug/placebo groups.
As a only-partially-related comment (feel free to skip/disregard this paragraph), it is true that in pre/post drug/placebo designs where the placebo/drug group assignment is random there are no reasons to expect a pre-treatment difference between the groups beyond sampling/random effects. In this case it is often argued that an ANCOVA analysis, where pre-treatment measures are used as a covariate when analyzing between-group differences in post-treatment measures, might be preferable over a repeated-measures/MANOVA analysis (both analyses will typically lead to similar results and interpretation, but the power in the former analysis is expected to be larger than in the latter, particularly in the case of low correlations between pre- and post- treatment measures; see for example Rausch et al. 2003, Analytic Methods for Questions Pertaining to a Randomized Pretest, Posttest, Follow-Up Design). If, on the other hand, the group assignment is not random then the two analyses can potentially lead to different results, and the differences arise from the different interpretation of these analyses: repeated-measures ANOVA/MANOVA allows you to look at the intervention "change" effects (differences in connectivity between post- and pre-intervention scans) and determine whether those changes are different in the control vs. placebo groups, while ANCOVA, on the other hand, allows you to look at the post-intervention connectivity values, and determine whether those are different in the control vs. placebo groups when comparing subjects at the same level of pre-intervention connectivity. In this case (non-random assignment) repeated-measures/MANOVA analyses are often recommended instead, though this may depend on the specifics of the research questions that one is interested in, and the source of pre-treatment differences between the groups. In practice, in fMRI analyses I have not really seen ANCOVA models used much in this context (perhaps mostly due to this sort of models requiring voxelwise regressors which are not that widely implemented/known?; just for reference using FSL's FEAT would allow you to do this, and we are working on an equivalent implementation in CONN) so most people seem to be using the (perhaps less powerful in some context but perhaps also more generally valid) repeated-measures/MANOVA approach instead irrespective of the potential source of pre-treatment differences.
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
Hi, Lucas,
You can accomplish what you want by using an exclusion mask from a simple baseline comparison of groups in the SPM GUI. Run a simple CONN baseline comparison of groups (i.e., [1 -1][1 0]) and set your threshold a bit more liberal (i.e., p<0.005) and then save any resulting blobs as a mask image. Then, run your desired group x time comparison (i.e., [1 -1][1 -1]) in CONN and threshold appropriately. In the results window you can click on SPM view and then when the SPM GUI results pop-up it will ask you if you wish to mask the results. It is here where you would select your ROI/mask from the [1 -1][1 0] group x baseline contrast and select exclusion. This will then show you which of your results from the group x time contrast were not a result of reasonably significant baseline differences.
Hope this helps,
Jeff
You can accomplish what you want by using an exclusion mask from a simple baseline comparison of groups in the SPM GUI. Run a simple CONN baseline comparison of groups (i.e., [1 -1][1 0]) and set your threshold a bit more liberal (i.e., p<0.005) and then save any resulting blobs as a mask image. Then, run your desired group x time comparison (i.e., [1 -1][1 -1]) in CONN and threshold appropriately. In the results window you can click on SPM view and then when the SPM GUI results pop-up it will ask you if you wish to mask the results. It is here where you would select your ROI/mask from the [1 -1][1 0] group x baseline contrast and select exclusion. This will then show you which of your results from the group x time contrast were not a result of reasonably significant baseline differences.
Hope this helps,
Jeff
Threaded View
| Title | Author | Date |
|---|---|---|
| Lucas Moro | Jun 27, 2016 | |
| Jeff Browndyke | Jun 28, 2016 | |
| Alfonso Nieto-Castanon | Jun 29, 2016 | |
| msc_22 | May 12, 2020 | |
| Nick Bray | Dec 17, 2020 | |
| Jeff Browndyke | Jun 29, 2016 | |
| Lucas Moro | Jun 29, 2016 | |
| Lucas Moro | Jun 28, 2016 | |
