Fair enough. And the movement signal regression? From: Glasser, Matthew [mailto:glass...@wusm.wustl.edu] Sent: Thursday, February 05, 2015 11:31 AM To: Hoptman, Matthew; 'hcp-users@humanconnectome.org' Cc: Elam, Jennifer Subject: Re: [HCP-Users] probably obvious question, but . . .
You don't need to rerun ICA+FIX, the HCP has already done that for you in the cleaned packages. If you want to check over the classification results, you can download the extended packages. Note I didn't actually state an opinion for or against on global signal regression there, so if you read one, it was your own bias on the topic. ;) Peace, Matt. From: <Hoptman>, Matthew <hopt...@nki.rfmh.org<mailto:hopt...@nki.rfmh.org>> Date: Thursday, February 5, 2015 at 10:23 AM To: Matt Glasser <glass...@wusm.wustl.edu<mailto:glass...@wusm.wustl.edu>>, "'hcp-users@humanconnectome.org<mailto:'hcp-users@humanconnectome.org>'" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Cc: "Elam, Jennifer" <el...@pcg.wustl.edu<mailto:el...@pcg.wustl.edu>> Subject: RE: [HCP-Users] probably obvious question, but . . . Hi, This is very helpful, and I did find some of the info on the wb_command in the text: https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ#HCPUsersFAQ-1.Whatare*dtseries.nii,*.dconn.nii,*dlabel.nii,*.dscalar.niifiles I'm running FIX on the raw data found in MNINonlinear/Results/rfMRI_REST1_LR{RL}/rfMRI_REST_LR.nii.gz. Is the regression of the 24 movement parameters an option of wb_command? I'm not seeing it as one. I know well about the issue of global signal, and agree with you . . . Your point about not needing to do WM or CSF time series regression after ICA FIX is quite helpful. Thanks, Matt From: Glasser, Matthew [mailto:glass...@wusm.wustl.edu] Sent: Thursday, February 05, 2015 10:55 AM To: Hoptman, Matthew; 'hcp-users@humanconnectome.org<mailto:'hcp-users@humanconnectome.org>' Cc: Elam, Jennifer Subject: Re: [HCP-Users] probably obvious question, but . . . You'll want to make sure you're using the latest version of the main HCP public release, rather than tutorial data. You can run wb_command -cifti-correlate to compute functional connectivity from the dense timeseries data. If you want to combine across runs, you should demean the data first using the following approach: wb_command -cifti-reduce <in dtseries> MEAN <dtseries_mean> wb_command -cifti-math "dtseries - dtseries_mean" <out dtseries> -var dtseries <in dtseries> -var dtseries_mean <dtseries_mean> -select 1 1 -repeat Then use wb_command -cifti-merge to merge the runs. You'll want to use the data that have been cleaned by ICA+FIX (linear trends removed with high pass of 2000s, 24 motion parameters removed, and structured noise ICA components automatically classified and removed). As for additional preprocessing, like spatial or further temporal filtering, I personally don't use any of that. Global signal (or mean gray signal) also has not been removed from the data (though as we know whether or not to do that is a matter of debate in which the two sides largely talk past each other). Things like white matter or CSF mask covariates are handled better by regressing out the structured noise ICA components, so there isn't any reason to worry about those. Peace, Matt. P.S. Jenn, could the above info find a home somewhere on the HCP website? From: <Hoptman>, Matthew <hopt...@nki.rfmh.org<mailto:hopt...@nki.rfmh.org>> Date: Thursday, February 5, 2015 at 9:17 AM To: "'hcp-users@humanconnectome.org<mailto:'hcp-users@humanconnectome.org>'" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: [HCP-Users] probably obvious question, but . . . Hi all, I have run the tutorial data through the HCP pipelines (v.3.4.0), but I wish to further process the resting state data. I am not sure how to proceed, however. The preprocessed data are in two places (Pipelines_ExampleData/100307/rfMRI_REST1_RL{LR}, and Pipelines_ExampleData/100307/MNINonLinear/Results/rfMRI_REST1_RL{LR}), albeit the files are different in the two places. I presume I want to use the latter to compute functional connectivity and related measures. I further assume the data there are motion-corrected and spatially normalized, but that no covariates have been removed and the data are not smoothed or filtered yet. I'm also not sure which files to use or how to merge the two resting state encodings. Is there a script that handles these next steps so that I can compute functional connectivities, etc.? I didn't find anything in the pipeline manual about this. Thanks, Matt IMPORTANT NOTICE: This e-mail is meant only for the use of the intended recipient. It may contain confidential information which is legally privileged or otherwise protected by law. 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If you have received this email in error, please immediately notify the sender via telephone or return mail. IMPORTANT NOTICE: This e-mail is meant only for the use of the intended recipient. It may contain confidential information which is legally privileged or otherwise protected by law. If you received this e-mail in error or from someone who is not authorized to send it to you, you are strictly prohibited from reviewing, using, disseminating, distributing or copying the e-mail. PLEASE NOTIFY US IMMEDIATELY OF THE ERROR BY RETURN E-MAIL AND DELETE THIS MESSAGE FROM YOUR SYSTEM. Thank you for your cooperation. _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
