Dear Varun,noise removal and dual regression happen at two different stages.
Here is a common pipeline:- preprocessing at the single subject level- noise removal at the single subject level (either regressing out motion, WM and CSF signal or, single subject ICA + manual classification or FIX or AROMA)- registration in standard space- group ICA with temporal concatenation (on the cleaned and registered data)- dual regression to test group differences
You can find more info on our FSL course material: https://fsl.fmrib.ox.ac.uk/fslcourse/in particular the ICA and dual regression practical covers the above steps:
Hope it helps,Ludovica
—
Ludovica Griffanti, PhD
Postdoctoral Researcher
Wellcome Centre for Integrative Neuroimaging (WIN)
Oxford Centre for Functional MRI of the Brain (FMRIB)
Nuffield Department of Clinical Neurosciences, University of Oxford
John Radcliffe Hospital
Oxford, OX3 9DU, UK
email: [log in to unmask]
On 27 Nov 2019, at 8:28 pm, Varun Arunachalam Chandran <[log in to unmask]> wrote:
Dear experts,
This may be a very basic question to post about the dual regression using multi-session temporal concatenated ICA.
I am trying to understand how does the time-series of the white matter and cerebrospinal fluid nuisance signals are being regressed out, when we run the dual regression using multi-session temporal concatenated ICA.
Otherwise, should we just add the time-series of WM and CSF as confounding variables in the GLM, when we run the dual regression after the multi-session temporal concatenated ICA.
Could someone please help me to clarify this doubt? Any help would be very much appreciated, thanks !
best,
Varun
PhD in Neurosciences,
School of Psychology and Language Sciences,
University of Reading,
Berkshire, UK.
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