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 > <javascript:_e(%7B%7D,'cvml','k...@princeton.edu');>> > Date: Sunday, April 12, 2015 at 4:22 PM > To: "hcp-users@humanconnectome.org > <javascript:_e(%7B%7D,'cvml','hcp-users@humanconnectome.org');>" < > hcp-users@humanconnectome.org > <javascript:_e(%7B%7D,'cvml','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 <javascript:_e(%7B%7D,'cvml','k...@princeton.edu');> > > _______________________________________________ > HCP-Users mailing list > HCP-Users@humanconnectome.org > <javascript:_e(%7B%7D,'cvml','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 _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
