Hi Maarten,
That's great, thanks!
Do you know, if the purpose is to do seed-based analysis and not dual
regression, would the resulting denoised_func_data_2 be suitable for
this purpose (maybe with the mean global signal as an additional
nuisance regressor)?
Best,
Anders
2015-12-01 14:40 GMT+01:00 Maarten Mennes <[log in to unmask]>:
> Hello,
>
> yes, this looks like a correct pipeline,
>
> Maarten
>
> On Mon, Nov 30, 2015 at 9:33 AM, Anders Hougaard <[log in to unmask]>
> wrote:
>>
>> Hi Daniel and all,
>>
>> i found this thread very helpful but could one of the experts please
>> confirm that Daniel's pipeline in the last post is correct?
>> Thank you so much.
>>
>> Best,
>> Anders
>>
>> 2015-09-03 0:09 GMT+02:00 Yang, Daniel <[log in to unmask]>:
>> > Hi Christian and all,
>> >
>> > Thanks so much! I am putting together along with what you suggested. Can
>> > you
>> > please comment again the following?
>> >
>> > (1) nuisance regression (WM, CSF)
>> >
>> > 1-a). With the WM, we can do:
>> >
>> > fslmaths WM_mask_in_highres_space -ero WM_mask_in_highres_space_eroded
>> >
>> > flirt -in WM_mask_in_highres_space_eroded \
>> > -applyxfm \
>> > -init rest.feat/reg/highres2example_func.mat\
>> > -ref example_func.nii.gz \
>> > -out WM_in_func_space
>> >
>> > fslmeants -i denoised_func_data.nii.gz -m WM_mask_in_func_space --no_bin
>> > -o
>> > WM_in_func_bin_timeseries
>> >
>> > Note: (i) The WM_mask_highres_space can be obtained in several ways
>> > (e.g.,
>> > FSL/fast, SPM12/segment). I use SPM12/segment here and I obtain a
>> > weighted
>> > WM_mask_in_highres_space. This weighted information is actually good
>> > stuff
>> > because it allows more certainty for WM voxels (vs. non-WM voxels). To
>> > use
>> > the information, I use --no_bin in fslmeants. (ii) Also I want to erode
>> > the
>> > WM mask to begin with, in order to increase the certainty (e.g.,
>> > reducing
>> > some boundaries with GM). (iii) Finally, I use denoised_func_data
>> > (rather
>> > than filtered_func_data) as input for fslmeants because I want to add
>> > further denoising to it.
>> >
>> > 1-b). We can use similar procedure as above to obtain
>> > CSF_in_func_bin_timeseries
>> >
>> > 1-c). To regress out these two nuisance regressors:
>> >
>> > paste WM_in_func_bin_timeseries CSF_in_func_bin_timeseries >
>> > nuisance_timeseries
>> >
>> > fslmaths denoised_func_data –Tmean tempMean
>> > fsl_glm –i denoised_func_data –d nuisance_timeseries –demean
>> > –res_out=residual
>> >
>> > Note: I use —demean to mean-center the nuisance timeseries so that the
>> > residual makes sense. I don’t use —des_norm or —dat_norm because I am
>> > looking at the residual (but not the GLM betas) here. I also want to add
>> > the
>> > mean back so that it is a firm data set (see step (3) below).
>> >
>> > (2) Linear detrending
>> >
>> > The standard procedure of linear detrending in FSL is done via high pass
>> > filtering (below).
>> >
>> > (3) highpass filtering
>> >
>> > Since my TR = 2.0 s and I want highpass filtering of 100 second, this
>> > means
>> > 50 TR (volume), and thus highpass sigma is 25.0 TR (volumes).
>> >
>> > fslmaths residual -bptf 25.0 -1 –add tempMean denoised_func_data_2
>> >
>> > Note: Finally, I will use the denoised_func_data_2 as input for doing
>> > participant-level statistics (postprocessing).
>> >
>> > Thanks again!!
>> > Daniel
>> >
>> > On 9/2/15, 2:27 PM, "FSL - FMRIB's Software Library on behalf of
>> > Christian
>> > F. Beckmann" <[log in to unmask] on behalf of [log in to unmask]>
>> > wrote:
>> >
>> > Hi
>> >
>> >
>> > (1) nuisance regression (WM, CSF)
>> > 1-a). With the WM, we can do:
>> > flirt -in WM_mask_in_highres_space \
>> > -applyxfm \
>> > -init rest.feat/reg/highres2example_func.mat\
>> > -ref example_func.nii.gz \
>> > -out WM_in_func_space
>> > fslmaths WM_in_func_space –bin WM_mask_in_func_space
>> > fslmeants -i filtered_func_data.nii.gz -m WM_mask_in_func_space -o
>> > WM_in_func_bin_timeseries
>> >
>> >
>> >
>> > 2 comments: first, there is no necessity to binarise the WM mask per se.
>> > If
>> > you start with the pve map for WM thenthe relative proportions reflect
>> > the
>> > confidence at each voxel of it being WM and you might want to
>> > incorporate
>> > that information by using a weighted mask so that voxels where you’re
>> > very
>> > confident that it’s a WM voxel weight more strongly than a voxel where
>> > segmentation is less clear.
>> >
>> > Secondly you may wont to erode the mask, in order to remove the
>> > influence of
>> > voxels at the GM/WM boundary. You can do this with fslmaths in -ero out
>> >
>> >
>> > Question: We should use filtered_func_data (but not
>> > ICA-AROMA-denoised_func_data) as input, correct?
>> >
>> >
>> > No, you would want to use the AROMA-denoised data and then add further
>> > denoising to it, not to the filtered_func_data
>> >
>> > 1-b). We can use similar procedure as above to obtain
>> > CSF_in_func_bin_timeseries
>> >
>> >
>> > Yes, but the same comments apply
>> >
>> > 1-c). To regress out these two nuisance regressors:
>> > paste WM_in_func_bin_timeseries CSF_in_func_bin_timeseries >
>> > nuisance_timeseries
>> > fsl_glm –i denoised_func_data –d nuisance_timeseries –dat_norm
>> > –res_out=residual
>> > Questions: The residual is the file with these nuisance regressed out,
>> > correct? We should use –dat_norm, correct? (Or –des_norm? What’s their
>> > difference?)
>> >
>> >
>> > You also want to switch on demeaning. The des/dat norm is irrelevant as
>> > you’re not using the betas but the residuals for further processing.
>> > Depending on what you want to do with the residuals later you also need
>> > to
>> > re-add the mean so it looks like a firm data set
>> >
>> >
>> > (2) Linear detrending
>> > The standard procedure of linear detrending in FSL is done via highpass
>> > filtering. How to perform linear detrending as suggested in the
>> > ICA-AROMA
>> > paper? Many thanks!!
>> >
>> >
>> > use fslmaths with the -bptf option, it will also remove the linear trend
>> >
>> > hth
>> > Christian
>> >
>> > (3) highpass filtering
>> > Since my TR = 2.0 s and I want highpass filtering of 100 second, this
>> > means
>> > 50 TR (volume), and thus highpass sigma is 25.0 TR (volumes).
>> > fslmaths input -bptf 25.0 -1 output
>> > Is that correct?
>> > Best,
>> > Daniel
>> > From: "[log in to unmask]" <[log in to unmask]> on behalf of Daniel Yang
>> > <[log in to unmask]>
>> > Reply-To: "[log in to unmask]" <[log in to unmask]>
>> > Date: Wednesday, September 2, 2015 at 1:13 AM
>> > To: "[log in to unmask]" <[log in to unmask]>
>> > Subject: [FSL] ICA-AROMA questions: the preprocessing steps after
>> > ICA-AROMA
>> > Dear all,
>> > In ICA-AROMA steps, after ICA-AROMA, it is suggested that we do:
>> > Nuisance regression: WM, CSF, & linear trend
>> > Highpass filtering
>> > Could we please provide details how these two can be properly done
>> > (e.g.,
>> > via bash scripts)?
>> > Many thanks!
>> > Daniel
>> >
>> >
>
>
>
>
> --
> Maarten Mennes, Ph.D.
> Senior Researcher
> Donders Institute for Brain, Cognition and Behaviour
> Radboud University Nijmegen
> Nijmegen
> The Netherlands
>
> Google Scholar Author Link
|