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help > RE: Discrepancy between subnetwork and raw connectivity data
Oct 14, 2020 10:10 PM | Andrew Zalesky
RE: Discrepancy between subnetwork and raw connectivity data
Hi Jean,
if the direction reverses with the inclusion of confounds, I think it is reasonable to assume that the confounds are having a substantial impact.
I would check your design matrix, since it does seem a bit odd that the direction reverses with inclusion of confounds.
Regarding your last question, all the edges in the subnetwork should show the same direction as the subnetwork as a whole. If the subnetwork shows Disease > Control, it should not be possible for edges in the subnetwork to show Disease < Control.
I think that you should check your design matrix.
Andrew
Originally posted by YaeJi Kim:
if the direction reverses with the inclusion of confounds, I think it is reasonable to assume that the confounds are having a substantial impact.
I would check your design matrix, since it does seem a bit odd that the direction reverses with inclusion of confounds.
Regarding your last question, all the edges in the subnetwork should show the same direction as the subnetwork as a whole. If the subnetwork shows Disease > Control, it should not be possible for edges in the subnetwork to show Disease < Control.
I think that you should check your design matrix.
Andrew
Originally posted by YaeJi Kim:
Dear Andrew,
Thank you for your kind response!
I just ran the NBS without covariates, and it shows significant subnetworks for the contrast of Disease > Control.
However, with the covariates, it shows opposite subnetworks of Control > Disease.
In this case, is it okay to interpret confounds have a significant impact?
One more thing,
As I ran the t-test for each edge from the significant subnetworks with controlling the covariates, still some edges show opposite directionality, which is Disease > Control.
Is this also possible, even though I controlled all the covariates?
Thank you!
Sincerely,
Jean
Thank you for your kind response!
I just ran the NBS without covariates, and it shows significant subnetworks for the contrast of Disease > Control.
However, with the covariates, it shows opposite subnetworks of Control > Disease.
In this case, is it okay to interpret confounds have a significant impact?
One more thing,
As I ran the t-test for each edge from the significant subnetworks with controlling the covariates, still some edges show opposite directionality, which is Disease > Control.
Is this also possible, even though I controlled all the covariates?
Thank you!
Sincerely,
Jean
Threaded View
| Title | Author | Date |
|---|---|---|
| YaeJi Kim | Oct 13, 2020 | |
| Andrew Zalesky | Oct 13, 2020 | |
| YaeJi Kim | Oct 14, 2020 | |
| Andrew Zalesky | Oct 14, 2020 | |