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Well, I wouldn’t do it that way since there are a variety of better alternatives that deal with the issue in a more targeted way. In any case, we should have a better solution for dense analyses in the future.
Peace,
Matt.
Thanks Matt! This is really helpful.
We weren't necessarily going to use a parcellated analysis, since we have the disk space for voxel by voxel -- although it seems like that's what many of the sub graph detection results are for. However, perhaps we should do that if we are going to skip
filtering.
Is it a *bad* idea to use just fsl's bandpass filter instead and use voxel by voxel instead of using a regression paradigm? The reason I ask is that we are looking to replicate voxel by voxel community detection results with a different type of factor analysis.
I will check out fslnets, that seems really perfect.
Thanks again!
Kim
On Sunday, April 12, 2015, Glasser, Matthew < glass...@wusm.wustl.edu> wrote:
Unfortunately the really good way to deal with these issues for dense data has not yet been released (basically improvements upon the MIGP algorithm). I’m not sure what the release plan is on that, so perhaps someone else can comment on that.
Generally though, if you can set up your problem as a regression problem rather than a correlation problem (and your regression design matrix is from ROI(s) or a weighted component (s)), you don’t need to worry about spatial or temporal filtering (assuming
you’ve properly cleaned your data using movement parameter regression and ICA+FIX—something the HCP does for you in the FIX cleaned packages) to get high quality spatial maps.
Additionally, if you parcellate your data, you don’t need to do spatial or temporal filtering even when doing correlation, as parcellation is a great way to reduce unstructured noise in a neurobiologically valid way. It sounds like you might already be
doing parceallated analyses.
As for combining across subjects, generally one simply averages across runs after converting to Z scores and does a t-test across subjects. I’d have a look at FSLNets, as it has a good implementation of these things.
Peace,
Matt.
Hi hcp-users,
I'm new to resting state connectivity analysis (and this list-serve), and I have a few basic questions about applying it to the HCP data. I'm using the minimally preprocessed REST1 data.
1. The low-pass filtering seems "controversial", though commonly employed -- is there at this point an agreed-upon way to remove deleterious high frequency noise?
In addition, I'm having difficulty with temporally filtering the data in fsl. To bandpass data from .009 - .08 Hz, I'm running:
fslmaths nii_in -bptf 77.168.68 nii_out
where 77.16 = sigma_hipass = 1/(2 * TR * F_hicutoff), for TR = .72 and F_cutoff = .009
and 8.68 = sigma_lopass = 1/(2 * TR * F_locutoff), for TR = .72 and F_locutoff = .08
This seems correct (I at least confirmed with the feat gui that the conversion from cycle time to sigma is 1/(2*TR)). However, when I look at the data in the frequency domain, it looks like there is significant response left for frequencies below
.009 Hz (picture attached) and very little between .01-.08. Does anyone know if I'm doing something incorrectly, or if the frequency cutoff for a Gaussian filter is just very gradual?
2. Any additional preprocessing is recommended, besides temporal filter and what the minimal pre-processing has already enacted?
3. What is an intelligent way to combine correlation matrices? Averaging (Power et al, 2011)? Binarizing the correlation matrix by setting the top 10% of voxels to 1 and the rest to 0, and averaging the binarized matrices (Yeo et al, 2011)? Either? Something
fancier?
Any advice or additional resources would be enormously appreciated -- thanks very much!!
Kim
--
Kimberly Stachenfeld, BS
Graduate Student
236A Princeton Neuroscience Institute
Washington Road
Princeton, NJ 08544
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Kimberly Stachenfeld, BS
Graduate Student
236A Princeton Neuroscience Institute
Washington Road
Princeton, NJ 08544
The materials in this message are private and may contain Protected Healthcare Information or other information of a sensitive nature. If you are not the intended
recipient, be advised that any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited. If you have received this email in error, please immediately notify the sender via telephone
or return mail.
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