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help > RE: Are white matter and CSF confounds saved as files?
Mar 13, 2020 02:03 PM | Alfonso Nieto-Castanon - Boston University
RE: Are white matter and CSF confounds saved as files?
Hi Matt,
Well, you may find that information in the ROI_Subject*_Session*.mat files (typically the 2nd and 3rd entries in the 'names' and 'data' variables contain the principal components of the White/CSF noise areas), but, particularly if you are planning to use this for task-based activation analyses, I would instead recommend to get those regressors using directly a conn_module command like:
conn_module( 'preprocessing', ...
'steps', {'functional_regression'}, ...
'reg_names', {'realignment','scrubbing','White Matter','CSF'}, ...
'reg_dimensions',[inf, inf, 5, 5], ...
'reg_skip', true, ...
'reg_deriv', [1, 0, 0, 0]);
That will create a new file per subject/session named dp_*.txt in the same directories as your functional data and containing the denoising parameters, which you may then directly use for your task-based analyses (e.g. adding this file as an additional multiple-covariate in SPM). When using the syntax above, those dp_*.txt files will contain the following timeseries:
constant + linear session effects (2 timeseries)
realignment + derivative effects (12 timeseries)
scrubbing parameters (### timeseries)
white matter effects (5 timeseries)
CSF effects (5 timeseries)
The syntax above assumes that you have already preprocessed your data either within a CONN project or using other conn_module commands (see https://web.conn-toolbox.org/fmri-methods/denoising-pipeline for additional information)
Hope this helps
Alfonso
Originally posted by Matthew Heard:
Well, you may find that information in the ROI_Subject*_Session*.mat files (typically the 2nd and 3rd entries in the 'names' and 'data' variables contain the principal components of the White/CSF noise areas), but, particularly if you are planning to use this for task-based activation analyses, I would instead recommend to get those regressors using directly a conn_module command like:
conn_module( 'preprocessing', ...
'steps', {'functional_regression'}, ...
'reg_names', {'realignment','scrubbing','White Matter','CSF'}, ...
'reg_dimensions',[inf, inf, 5, 5], ...
'reg_skip', true, ...
'reg_deriv', [1, 0, 0, 0]);
That will create a new file per subject/session named dp_*.txt in the same directories as your functional data and containing the denoising parameters, which you may then directly use for your task-based analyses (e.g. adding this file as an additional multiple-covariate in SPM). When using the syntax above, those dp_*.txt files will contain the following timeseries:
constant + linear session effects (2 timeseries)
realignment + derivative effects (12 timeseries)
scrubbing parameters (### timeseries)
white matter effects (5 timeseries)
CSF effects (5 timeseries)
The syntax above assumes that you have already preprocessed your data either within a CONN project or using other conn_module commands (see https://web.conn-toolbox.org/fmri-methods/denoising-pipeline for additional information)
Hope this helps
Alfonso
Originally posted by Matthew Heard:
Hello experts,
I was wondering if the white matter and CSF confounds generated by CONN and used for denoising are saved as files. Ideally, I wanted to try and use the first principal component from these regressors to de-noise a task-based dataset by including them as regressors in my task-based general linear model.
Does anyone know if these files are saved? And if so, where are they located?
Thanks,
Matt
I was wondering if the white matter and CSF confounds generated by CONN and used for denoising are saved as files. Ideally, I wanted to try and use the first principal component from these regressors to de-noise a task-based dataset by including them as regressors in my task-based general linear model.
Does anyone know if these files are saved? And if so, where are they located?
Thanks,
Matt
Threaded View
| Title | Author | Date |
|---|---|---|
| Matthew Heard | Mar 13, 2020 | |
| Alfonso Nieto-Castanon | Mar 13, 2020 | |
| Matthew Heard | Mar 16, 2020 | |
| Alfonso Nieto-Castanon | Mar 19, 2020 | |
| Matthew Heard | Mar 18, 2020 | |