Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Support "Voxelwise confound list" #111

Open
cmaumet opened this issue Feb 6, 2017 · 2 comments
Open

Support "Voxelwise confound list" #111

cmaumet opened this issue Feb 6, 2017 · 2 comments

Comments

@cmaumet
Copy link
Member

cmaumet commented Feb 6, 2017

screen shot 2017-02-06 at 13 47 43

"PNM is a tool used in conjunction with the GLM (via FEAT) that can be used to model (or "regress out") the effects of physiological noise in functional MRI data. That is, it creates EVs (regressors) that can be used to model the physiological noise within the GLM, alongside other stimulus-related regressors." (Excerpt from the "Physiological Noise Modelling" (PNM) manual)

We need to determine how many evs are created and add them to the list of regressor names.

@cmaumet
Copy link
Member Author

cmaumet commented Mar 2, 2017

We could do this by using @pauldmccarthy's fsl.data.featdesign (part of fslpy).

@cmaumet
Copy link
Member Author

cmaumet commented Mar 2, 2017

designfsf = featanalysis.loadSettings('MY_PATH/nidmresults-examples/fsl_gamma_basis/')
designmat = featdesign.FEATFSFDesign('MY_PATH/fsl_gamma_basis/', designfsf)
evs = designmat.getEVs()
evs[0].title

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant