Awesome, I will do that, thanks very much!!

2018-03-07 18:01 GMT+01:00 Glasser, Matthew <glass...@wustl.edu>:

> Yes they should be in that same package:
>
> ${StudyFolder}/${Subject}/MNINonLinear/Results/${
> fMRIName}/${fMRIName}_hp2000.ica/.fix — Tells you which are the noise
> components (so you can use setdiff to find the signal components from a
> list of all components) so that you can exclude the noise component from
> the regression below.
> ${StudyFolder}/${Subject}/MNINonLinear/Results/${
> fMRIName}/${fMRIName}_hp2000.ica/filtered_func_data.ica/melodic_mix — ICA
> component timeseries (you should remove the mean of each ICA component
> timeseries before doing the regression).
>
> Probably the time to read in and write the file will be longer than the
> time to do the regression if you do it in matlab.  Here is some example
> code:
>
> betaICA = pinv(ICA) * TCS; #TCS is timepoints x space, ICA is timepoints x
> components and should include only the signal components (since the noise
> components were already removed).
> UnstructNoiseTCS = TCS - (ICA * betaICA);
>
> You then compute the temporal standard deviation of the unstructured noise
> timeseries and divide the data by it to get the variance normalized data.
>
> Peace,
>
> Matt.
>
> From: David Hofmann <davidhofma...@gmail.com>
> Date: Wednesday, March 7, 2018 at 10:47 AM
>
> To: Matt Glasser <glass...@wustl.edu>
> Cc: hcp-users <hcp-users@humanconnectome.org>
> Subject: Re: [HCP-Users] Concatenating resting state runs
>
> Ah I understand. However, I'm not sure how to do this practically for the
> FIX extended data. I'd need all the signal component timeseries and run a
> regression for each voxel which might take a while. I'm not sure if the
> signals are supplied in the dataset, or are they?
>
> Thanks for the support!
>
> 2018-03-07 17:07 GMT+01:00 Glasser, Matthew <glass...@wustl.edu>:
>
>> The unstructured noise variance is the standard deviation of the
>> timeseries after you regress out all of the signal component timeseries.
>> By doing this you make the unstructured noise equal in magnitude across the
>> brain.
>>
>> I wouldn’t do smoothing unless it is constrained to the greymatter.
>> Really you won’t get an obvious benefit if you will be averaging voxels in
>> an ROI anyway and that is a more accurate way to do things.
>>
>> I guess I don’t know enough about your study to know if the order
>> matters.  If you are interested in effects that might be related to order
>> (e.g. drowsiness being higher in later scans, then order might matter).
>>
>> Peace,
>>
>> Matt.
>>
>> From: David Hofmann <davidhofma...@gmail.com>
>> Date: Wednesday, March 7, 2018 at 10:02 AM
>>
>> To: Matt Glasser <glass...@wustl.edu>
>> Cc: hcp-users <hcp-users@humanconnectome.org>
>> Subject: Re: [HCP-Users] Concatenating resting state runs
>>
>> Hey Matthew,
>>
>> not sure I understood where to get the unstructured noise variance from,
>> i.e. is it even possible to apply this to the FIX extended datasets?
>>
>> I thought about using 4mm smoothing (maybe 2mm) before extracting the
>> VOIs / ROI timecourses for each subject. This is then fed into the DCMs for
>> each subject. I experimented with some HCP data before and it seems
>> smoothing increases the effect sizes a little bit. What is smoothing
>> between parcellations btw.?
>>
>> Also, any comments on the order of concatenation? I concatenate all of
>> the data RL and then LR.
>>
>> 2018-03-07 16:17 GMT+01:00 Glasser, Matthew <glass...@wustl.edu>:
>>
>>> I typically variance normalize before concatenation, but do this based
>>> on the unstructured noise variance.
>>>
>>> I would take the mean time course over an ROI that I thought to be
>>> representative of a meaningful neuroanatomical subunit.
>>>
>>> My understanding of how SPM’s DCM is typically implemented is that there
>>> are large amounts of spatial smoothing, cross-subject alignment is done in
>>> the volume, and ROIs are spheres of some radius.  All this would lead to a
>>> lot of mixing of timecourses.  My suggestion was to use parcel timecourses
>>> from some kind of parcellation.  If you have a good amygdala parcellation
>>> that might be fine, though I would avoid smoothing the data between the
>>> parcels.
>>>
>>> Peace,
>>>
>>> Matt.
>>>
>>> From: David Hofmann <davidhofma...@gmail.com>
>>> Date: Wednesday, March 7, 2018 at 9:12 AM
>>> To: Matt Glasser <glass...@wustl.edu>
>>> Cc: hcp-users <hcp-users@humanconnectome.org>
>>> Subject: Re: [HCP-Users] Concatenating resting state runs
>>>
>>> Hi Matthew,
>>>
>>> ok, so temporal filtering separately for each run. Any comments on
>>> concatenation and z-standardization?
>>>
>>> I think there might be a work-around to supplying a custom ROI
>>> timecourse to the DCM VOI-files somehow, but which values to input as
>>> alternative to the eigenvariate? The mean over all voxels in the ROI would
>>> also be an option but not sure what you had in mind.
>>>
>>> Can you elaborate on the issue of spatial localization you mention
>>> please, not sure I understood? I'm using mask files to extract the time
>>> courses and I am especially interested in amygdala subregions.
>>>
>>> Also, what do you mean by areal ROIs and that they give a purer signal?
>>>
>>> Thanks :)
>>>
>>> 2018-03-07 14:51 GMT+01:00 Glasser, Matthew <glass...@wustl.edu>:
>>>
>>>> You would want to apply temporal filtering separately to each run.  I
>>>> wonder if there is a way you could just provide the ROI timecourses to
>>>> SPM’s DCM model without using its tools for extracting the ROIs so that you
>>>> could avoid the issues spatial localization that SPM has.  If you used
>>>> areal ROIs, you likely wouldn’t even need the eigenvariate approach as you
>>>> would be getting a much purer signal.
>>>>
>>>> Peace,
>>>>
>>>> Matt.
>>>>
>>>> From: <hcp-users-boun...@humanconnectome.org> on behalf of David
>>>> Hofmann <davidhofma...@gmail.com>
>>>> Date: Wednesday, March 7, 2018 at 2:32 AM
>>>> To: hcp-users <hcp-users@humanconnectome.org>
>>>> Subject: [HCP-Users] Concatenating resting state runs
>>>>
>>>> Hi all,
>>>>
>>>> for a later analysis where I extract ROIs with SPM, I need to
>>>> concatenate the resting state runs and want to make sure I'm doing it
>>>> correctly. SPM extracts the first eigenvariate of a ROI, i.e. the component
>>>> that explains the most variance.
>>>>
>>>> I'm using the* Resting State fMRI 1 FIX-Denoised (Extended)* and *Resting
>>>> State fMRI 2 FIX-Denoised (Extended)* datasets.  That is, the
>>>> files: rfMRI_REST1_LR_hp2000_clean.nii, rfMRI_REST1_RL
>>>> _hp2000_clean.nii asf.
>>>>
>>>> I chose the following approach:
>>>>
>>>> 1.  z-standardize each session (each voxel timecourse), i.e. RL, LR
>>>> separately
>>>> 2. Then concatenate them
>>>> 3. Run the SPM routines which will also apply a high-pass filter of
>>>> about 128s on the already concatenated data (it's for the processing of a
>>>> DCM rather than functional connectivity)
>>>>
>>>> I have the following questions:
>>>>
>>>> 1. Is this approach correct?
>>>> 2. Does the order of concatenation matter? That is, (RL/LR or LR/RL) or
>>>> is it important to concatenate it in the order it was acquired in each
>>>> subject? I read that it sometimes changes between subjects such that LR
>>>> came first in one subject and RL first in another.
>>>> 3. Since SPM will run a hp-filter on the concatenated data, would it be
>>>> better to hp filter each run *separately* before concatenation?
>>>> 4. Is this approach also applicable to the task data (i.e. standardize
>>>> and filter separately before concatenation)?
>>>>
>>>> Thanks in advance
>>>>
>>>> David
>>>>
>>>>
>>>> _______________________________________________
>>>> HCP-Users mailing list
>>>> HCP-Users@humanconnectome.org
>>>> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>>>>
>>>
>>>
>>
>>
>

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