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help > RE: Effect of condition included in list of ROIs?
May 27, 2015 02:05 PM | Alfonso Nieto-Castanon - Boston University
RE: Effect of condition included in list of ROIs?
Hi Nicola,
Any first-level covariates (as well as task main effects) that are not entered in the 'Confounds' list during Denoising are included in the list of potential 'Sources' for your first-level analyses (the reason is to allow users to enter their own manually-defined seed timeseries if they wish). The main condition effects (named "Effect of *" after each of your conditions) model the main effects of each condition in the BOLD timeseries, and they are typically entered in the 'Confounds' list in the Denoising step. In the case of a single 'rest' condition the 'effect of rest' covariate simply models a potential 'ramping-up' effect of the BOLD signal at the beginning of your scanning session. If you are finding a significant effect that means that your data shows these sort of 'ramping-up' effects and that is the more reason you should consider including it in the 'Confounds' list (in order to remove these effects from the BOLD signal so that they do not affect the estimated connectivity measures). So, summarizing, I would suggest to enter the 'effect of rest' (and its first temporal derivative) as an additional confounding effect during Denoising, and that will also remove this covariate from your list of potential first-level analysis sources (alternatively, another common procedure to avoid these sort of transient effects is to simply remove the first few scans from your scanning session).
Hope this helps
Alfonso
Originally posted by Nicola Toschi:
Any first-level covariates (as well as task main effects) that are not entered in the 'Confounds' list during Denoising are included in the list of potential 'Sources' for your first-level analyses (the reason is to allow users to enter their own manually-defined seed timeseries if they wish). The main condition effects (named "Effect of *" after each of your conditions) model the main effects of each condition in the BOLD timeseries, and they are typically entered in the 'Confounds' list in the Denoising step. In the case of a single 'rest' condition the 'effect of rest' covariate simply models a potential 'ramping-up' effect of the BOLD signal at the beginning of your scanning session. If you are finding a significant effect that means that your data shows these sort of 'ramping-up' effects and that is the more reason you should consider including it in the 'Confounds' list (in order to remove these effects from the BOLD signal so that they do not affect the estimated connectivity measures). So, summarizing, I would suggest to enter the 'effect of rest' (and its first temporal derivative) as an additional confounding effect during Denoising, and that will also remove this covariate from your list of potential first-level analysis sources (alternatively, another common procedure to avoid these sort of transient effects is to simply remove the first few scans from your scanning session).
Hope this helps
Alfonso
Originally posted by Nicola Toschi:
Hi list,
a basic question I could not find the answer to: when looking at ROI-to-ROI or seed-to-voxel effects in a two-group block design, the effect of my conditions (rest/task) is included under the list of ROIs, and often appears to have a significant "connection" to "real" (atlas-defined) ROIs.
What exactly does this refer to? Howe are those additional ROIs, named after the effects of conditions, defined?
Thanks a lot
Nicola
a basic question I could not find the answer to: when looking at ROI-to-ROI or seed-to-voxel effects in a two-group block design, the effect of my conditions (rest/task) is included under the list of ROIs, and often appears to have a significant "connection" to "real" (atlas-defined) ROIs.
What exactly does this refer to? Howe are those additional ROIs, named after the effects of conditions, defined?
Thanks a lot
Nicola
Threaded View
| Title | Author | Date |
|---|---|---|
| Nicola Toschi | May 27, 2015 | |
| Alfonso Nieto-Castanon | May 27, 2015 | |
| Nicola Toschi | May 27, 2015 | |
| Alfonso Nieto-Castanon | May 27, 2015 | |
| Nicola Toschi | May 28, 2015 | |