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help > RE: Movement
Mar 31, 2015 04:03 PM | Xiaozhen You
RE: Movement
Hi Alfonso,
After reading this again today I'm a little more confused regarding the ART effect on first level connectivity analysis.
"the added first-level covariate will act to effectively remove the outlier scans from consideration when computing any functional connectivity measures."
Do you mean it's regressing them out(deweighting) or essentially like scrubbing (discard the bad volumes when computing FC)?
Thank you!
Xiaozhen
Originally posted by Alfonso Nieto-Castanon:
After reading this again today I'm a little more confused regarding the ART effect on first level connectivity analysis.
"the added first-level covariate will act to effectively remove the outlier scans from consideration when computing any functional connectivity measures."
Do you mean it's regressing them out(deweighting) or essentially like scrubbing (discard the bad volumes when computing FC)?
Thank you!
Xiaozhen
Originally posted by Alfonso Nieto-Castanon:
Hi
Kaylah,
Yes, the .mat file produced by ART can be entered directly into CONN as a first-level covariate. After doing this, entering the resulting first-level covariate into the 'Confounds' list during the Preprocessing step will effectively remove the outlier scans from consideration. In fact, the latest version of the toolbox already incorporates ART as part of the standard preprocessing pipeline, so all of this will be done automatically for you if you run your data through CONN spatial preprocessing steps. But of course you can also do that manually (just following the steps above). When you do this you do not need to change the length of each scan, you would still select your entire functional run, and the added first-level covariate will act to effectively remove the outlier scans from consideration when computing any functional connectivity measures.
Regarding your last question this process is not redundant with aCompCor. While it is true that, if not performing ART, CompCor may still be able to automatically detect the effect of some of these outlier scans purely from their expression on the BOLD signal at white matter and CSF areas, the outlier detection in ART uses additional sources of information (e.g. estimated movement parameters) to detect potential outliers, and the resulting outlier scans will then by default be also removed prior to the PCA estimation in CompCor from the white matter and CSF BOLD signals. This allows the CompCor components to focus on other less obvious physiological and movement effects, as it would normally do in the absence of outlier scans, resulting in a more consistent and thorough removal of potential confounding effects.
Hope this helps
Alfonso
Originally posted by Kaylah Curtis:
Yes, the .mat file produced by ART can be entered directly into CONN as a first-level covariate. After doing this, entering the resulting first-level covariate into the 'Confounds' list during the Preprocessing step will effectively remove the outlier scans from consideration. In fact, the latest version of the toolbox already incorporates ART as part of the standard preprocessing pipeline, so all of this will be done automatically for you if you run your data through CONN spatial preprocessing steps. But of course you can also do that manually (just following the steps above). When you do this you do not need to change the length of each scan, you would still select your entire functional run, and the added first-level covariate will act to effectively remove the outlier scans from consideration when computing any functional connectivity measures.
Regarding your last question this process is not redundant with aCompCor. While it is true that, if not performing ART, CompCor may still be able to automatically detect the effect of some of these outlier scans purely from their expression on the BOLD signal at white matter and CSF areas, the outlier detection in ART uses additional sources of information (e.g. estimated movement parameters) to detect potential outliers, and the resulting outlier scans will then by default be also removed prior to the PCA estimation in CompCor from the white matter and CSF BOLD signals. This allows the CompCor components to focus on other less obvious physiological and movement effects, as it would normally do in the absence of outlier scans, resulting in a more consistent and thorough removal of potential confounding effects.
Hope this helps
Alfonso
Originally posted by Kaylah Curtis:
Hello,
Usually we exclude subjects with movement greater than 2 mm. However, we have a dataset with very few subjects due to increased movement. We want to somehow remove bad volumes due to movement greater than 2 mm before running them through Conn. We have used your ART toolbox to obtain outliers as well as the 0/1 matrix to input as covariates within a GLM. Is it possible to input that mat file into conn as a first-level covariate? Will it remove those bad volumes? Also, if this is possible, what should we set as the duration of the scan?
Would carrying out this process be redundant with the current aCompCor function of Conn?
Any help will be appreciated!!
Thanks,
Kaylah
Usually we exclude subjects with movement greater than 2 mm. However, we have a dataset with very few subjects due to increased movement. We want to somehow remove bad volumes due to movement greater than 2 mm before running them through Conn. We have used your ART toolbox to obtain outliers as well as the 0/1 matrix to input as covariates within a GLM. Is it possible to input that mat file into conn as a first-level covariate? Will it remove those bad volumes? Also, if this is possible, what should we set as the duration of the scan?
Would carrying out this process be redundant with the current aCompCor function of Conn?
Any help will be appreciated!!
Thanks,
Kaylah
Threaded View
| Title | Author | Date |
|---|---|---|
| Kaylah Curtis | Jul 23, 2014 | |
| Mary Newsome | Apr 2, 2015 | |
| Alfonso Nieto-Castanon | Apr 6, 2015 | |
| Alfonso Nieto-Castanon | Jul 29, 2014 | |
| Xiaozhen You | Mar 31, 2015 | |
| Fred Uquillas | Mar 31, 2015 | |
| Xiaozhen You | Apr 1, 2015 | |
| Fred Uquillas | Apr 1, 2015 | |
| Alfonso Nieto-Castanon | Apr 2, 2015 | |
| Xiaozhen You | Apr 2, 2015 | |
| Ekaterina Shcheglova | Mar 26, 2023 | |
| Fred Uquillas | Apr 24, 2015 | |
| Alfonso Nieto-Castanon | Apr 28, 2015 | |
| Fred Uquillas | May 6, 2015 | |
| Arkan A | May 6, 2015 | |
| Alfonso Nieto-Castanon | May 6, 2015 | |
| Arkan A | May 7, 2015 | |
| Alfonso Nieto-Castanon | May 8, 2015 | |
| Arkan A | May 8, 2015 | |
| Bradley Taber-Thomas | Sep 30, 2014 | |
| Alfonso Nieto-Castanon | Oct 1, 2014 | |
| Bradley Taber-Thomas | Oct 1, 2014 | |
| Alfonso Nieto-Castanon | Nov 19, 2014 | |
| Kaylah Curtis | Jul 29, 2014 | |
| Alfonso Nieto-Castanon | Jul 30, 2014 | |
| Alexander Drobyshevsky | Oct 21, 2014 | |
| Alfonso Nieto-Castanon | Nov 19, 2014 | |
| Kaylah Curtis | Jul 30, 2014 | |
| Aleksandra Herman | Oct 23, 2014 | |