Dear Alfonso,
I am running into a problem when using the confound-corrected timeseries for first-level analysis in SPM.
I have preprocessed and denoised my data using:
- bandpass filter 0 - Inf
- no detrending
- despiking after regression (in retrospect, I think I could have set this to no despiking because of the use of scrubbing)
My list of confounds includes WM, CSF, scrubbing and realignment parameters (I have removed the session-specific conditions from the list of confounders).
First level analysis on the confound-corrected timeseries yields extremely high beta estimates of up to 900, as opposed to ~10 when using the dataset prior to denoising.I have set the global normalisation to 'none' in SPM. I also tried setting the BOLD signal units to raw in the CONN options list, but this gives a similar result.
Do you know what could cause this kind of problem?
Best regards,
Bram
I am running into a problem when using the confound-corrected timeseries for first-level analysis in SPM.
I have preprocessed and denoised my data using:
- bandpass filter 0 - Inf
- no detrending
- despiking after regression (in retrospect, I think I could have set this to no despiking because of the use of scrubbing)
My list of confounds includes WM, CSF, scrubbing and realignment parameters (I have removed the session-specific conditions from the list of confounders).
First level analysis on the confound-corrected timeseries yields extremely high beta estimates of up to 900, as opposed to ~10 when using the dataset prior to denoising.I have set the global normalisation to 'none' in SPM. I also tried setting the BOLD signal units to raw in the CONN options list, but this gives a similar result.
Do you know what could cause this kind of problem?
Best regards,
Bram