That seems like a good reason -- can you refer me somewhere for the better alternatives? We're on a bit of a timeline so I am hesitant to wait for the HCP version.
Thanks again for all the help! On Mon, Apr 13, 2015 at 12:34 PM, Glasser, Matthew <glass...@wusm.wustl.edu> wrote: > 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. > > From: Kimberly Stachenfeld <k...@princeton.edu> > Date: Monday, April 13, 2015 at 6:48 AM > To: Matt Glasser <glass...@wusm.wustl.edu> > Cc: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org> > Subject: Re: rsfc preprocessing > > 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. >> >> From: Kimberly Stachenfeld <k...@princeton.edu> >> Date: Sunday, April 12, 2015 at 4:22 PM >> To: "hcp-users@humanconnectome.org" <hcp-users@humanconnectome.org> >> Subject: [HCP-Users] rsfc preprocessing >> >> 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 >> >> k...@princeton.edu >> >> _______________________________________________ >> HCP-Users mailing list >> HCP-Users@humanconnectome.org >> http://lists.humanconnectome.org/mailman/listinfo/hcp-users >> >> >> ------------------------------ >> >> 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. >> > > > -- > Kimberly Stachenfeld, BS > Graduate Student > 236A Princeton Neuroscience Institute > Washington Road > Princeton, NJ 08544 > > (973) 270-3473 > k...@princeton.edu > > > ------------------------------ > > 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. > -- Kimberly Stachenfeld, BS Graduate Student 236A Princeton Neuroscience Institute Washington Road Princeton, NJ 08544 (973) 270-3473 k...@princeton.edu _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
