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help > RE: separate group covariates vs one covariate
Oct 9, 2021 02:10 AM | Rui Li
RE: separate group covariates vs one covariate
Hi Alfonso,
Thanks for your answer.
But I have one question on this test you mentioned in this post
"1) creating two group-specific covariates 'GM_men' and 'GM_women' and centering both to the same value (e.g. if the average across all subjects is 100, then GM_men should contain GM-100 values for men, and 0's for women, and GM_women should contain GM-100 values for women, and 0's for men)
2) selecting 'men', 'women', 'GM_men', and 'GM_women' in your subject-effects list, and entering a [1 -1 0 0] between-subjects contrast"
If we perform this test, my understanding is that we are comparing the FC difference between two group men and women with GM controlled (separately in each group). If this is correct, we don't have to center GM_men or GM_women. Is this correct?
If we want to test the association strength (of FC and GM) difference between men and women, the contrast vector would be [0 0 1 -1]. In this case, we will need to center GM_men and GC_women (for example reference to 100 the average of GM of all). Since the association is of interest rather than the affect of the average difference of GM_men and GM_women.
Look forward to your reply. Many thanks.
Regards,
Rui.
Originally posted by Alfonso Nieto-Castanon:
Thanks for your answer.
But I have one question on this test you mentioned in this post
"1) creating two group-specific covariates 'GM_men' and 'GM_women' and centering both to the same value (e.g. if the average across all subjects is 100, then GM_men should contain GM-100 values for men, and 0's for women, and GM_women should contain GM-100 values for women, and 0's for men)
2) selecting 'men', 'women', 'GM_men', and 'GM_women' in your subject-effects list, and entering a [1 -1 0 0] between-subjects contrast"
If we perform this test, my understanding is that we are comparing the FC difference between two group men and women with GM controlled (separately in each group). If this is correct, we don't have to center GM_men or GM_women. Is this correct?
If we want to test the association strength (of FC and GM) difference between men and women, the contrast vector would be [0 0 1 -1]. In this case, we will need to center GM_men and GC_women (for example reference to 100 the average of GM of all). Since the association is of interest rather than the affect of the average difference of GM_men and GM_women.
Look forward to your reply. Many thanks.
Regards,
Rui.
Originally posted by Alfonso Nieto-Castanon:
Hi
Alice,
Jeff is exactly correct, just to expand a bit on his response:
Typically if you want to evaluate whether there are connectivity differences between groups after correcting/taking-into-account those differences that may be already explained (perhaps more simply?) by differences in GM volume between the two groups, the proper analysis would be:
1) selecting 'men', 'women', and 'GM_all' in your subject-effects list, and entering a [1 -1 0] between-subject contrast
The results of these analyses will be exactly the same whether 'GM_all' is centered or not.
Typically you will do these sort of analyses when you want to evaluate group differences in connectivity, and a) there are reasons to believe that your covariate (GM volume in this case) may be related to your main connectivity measures (e.g. functional correlations); and b) that covariate also shows group differences (e.g. GM volume is different in males vs. females).
If, in addition to these, you also have reasons to believe that the strength of the association between GM volume and connectivity may also vary between groups (note that this is different than simply saying that average GM volume and connectivity may be different between your groups) then that makes comparing the connectivity between your two groups conceptually harder / less-clear. Since the GM-connectivity associations are different between the two groups (think of two regression lines with different slopes in the same GM-by-connectivity scatter plot), the differences in connectivity between the groups will vary depending on your choice of GM reference value (think of the differences between those two regression lines as you move along the x-axis). That makes it difficult to characterize exactly what one means by differences in connectivity between the groups (the two groups may show the same connectivity at some level of GM, when the lines cross, and higher connectivity to one side vs. lower connectivity to the other side of this crossing point). One possible way to conceptually address this is to choose a meaningful a priori GM reference value for the between-groups comparison. In practice, this is often done by choosing the average GM level across all subjects (jointly across both groups) as a reference point for the comparison. These analyses can be implemented by:
1) creating two group-specific covariates 'GM_men' and 'GM_women' and centering both to the same value (e.g. if the average across all subjects is 100, then GM_men should contain GM-100 values for men, and 0's for women, and GM_women should contain GM-100 values for women, and 0's for men)
2) selecting 'men', 'women', 'GM_men', and 'GM_women' in your subject-effects list, and entering a [1 -1 0 0] between-subjects contrast
Note that the results of these analyses will typically depend on the choice of reference value of your covariate (e.g. GM=100 in our example above).
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
Jeff is exactly correct, just to expand a bit on his response:
Typically if you want to evaluate whether there are connectivity differences between groups after correcting/taking-into-account those differences that may be already explained (perhaps more simply?) by differences in GM volume between the two groups, the proper analysis would be:
1) selecting 'men', 'women', and 'GM_all' in your subject-effects list, and entering a [1 -1 0] between-subject contrast
The results of these analyses will be exactly the same whether 'GM_all' is centered or not.
Typically you will do these sort of analyses when you want to evaluate group differences in connectivity, and a) there are reasons to believe that your covariate (GM volume in this case) may be related to your main connectivity measures (e.g. functional correlations); and b) that covariate also shows group differences (e.g. GM volume is different in males vs. females).
If, in addition to these, you also have reasons to believe that the strength of the association between GM volume and connectivity may also vary between groups (note that this is different than simply saying that average GM volume and connectivity may be different between your groups) then that makes comparing the connectivity between your two groups conceptually harder / less-clear. Since the GM-connectivity associations are different between the two groups (think of two regression lines with different slopes in the same GM-by-connectivity scatter plot), the differences in connectivity between the groups will vary depending on your choice of GM reference value (think of the differences between those two regression lines as you move along the x-axis). That makes it difficult to characterize exactly what one means by differences in connectivity between the groups (the two groups may show the same connectivity at some level of GM, when the lines cross, and higher connectivity to one side vs. lower connectivity to the other side of this crossing point). One possible way to conceptually address this is to choose a meaningful a priori GM reference value for the between-groups comparison. In practice, this is often done by choosing the average GM level across all subjects (jointly across both groups) as a reference point for the comparison. These analyses can be implemented by:
1) creating two group-specific covariates 'GM_men' and 'GM_women' and centering both to the same value (e.g. if the average across all subjects is 100, then GM_men should contain GM-100 values for men, and 0's for women, and GM_women should contain GM-100 values for women, and 0's for men)
2) selecting 'men', 'women', 'GM_men', and 'GM_women' in your subject-effects list, and entering a [1 -1 0 0] between-subjects contrast
Note that the results of these analyses will typically depend on the choice of reference value of your covariate (e.g. GM=100 in our example above).
Hope this helps
Alfonso
Originally posted by Jeff Browndyke:
Are there significant differences between male
and female GM values? My understanding of the total covariate
vs. separate covariate use depends upon your research question and
the possible presence of significant differences between groups in
those covariates. Also, I would recommend mean centering the
covariates, because not doing so may make it difficult to interpret
the results. There is really no such thing as zero GM volume
(uncentered situation). You would want to control for the
average GM volume in your sample (centered situation).
Hope this helps,
Jeff
Hope this helps,
Jeff
Threaded View
| Title | Author | Date |
|---|---|---|
| Alice Yo | Feb 19, 2017 | |
| Jeff Browndyke | Feb 19, 2017 | |
| Alfonso Nieto-Castanon | Feb 20, 2017 | |
| Rui Li | Oct 9, 2021 | |
| Alfonso Nieto-Castanon | Oct 9, 2021 | |
| Rui Li | Dec 3, 2021 | |
| Alice Yo | Feb 22, 2017 | |
| Alfonso Nieto-Castanon | Mar 1, 2017 | |