I clicked in a corresponding location and your activation appears to fall within area IFSp. The inferior frontal gyrus contains multiple areas, as does the inferior frontal sulcus. We have only directly commented on the function of the areas in relation to the 7 HCP acquired tasks (See the supplementary neuroanatomical results).
Peace, Matt. From: "Michael F.W. Dreyfuss" <mid2...@med.cornell.edu<mailto:mid2...@med.cornell.edu>> Date: Monday, September 26, 2016 at 5:59 PM To: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>>, "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>, Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Subject: Re: [HCP-Users] ROIs and Betas from Cifti Data Yes, but none of these regions is exactly what's typically called IFG, so to limit comparisons I was wondering if you knew of a reason that one of them should be considered a locus of response inhibition based on your data that would warrant using that region as an a priori ROI to look for neural correlates of differences in response inhibition. ________________________________ From: Glasser, Matthew <glass...@wustl.edu<mailto:glass...@wustl.edu>> Sent: Monday, September 26, 2016 6:22:50 PM To: Michael F.W. Dreyfuss; hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>; NEUROSCIENCE tim Subject: Re: [HCP-Users] ROIs and Betas from Cifti Data Can’t you just click which area is under that cluster? I didn’t realize you already had the data registered. Peace, Matt. From: "Michael F.W. Dreyfuss" <mid2...@med.cornell.edu<mailto:mid2...@med.cornell.edu>> Date: Monday, September 26, 2016 at 4:09 PM To: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>>, "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>, Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Subject: Re: [HCP-Users] ROIs and Betas from Cifti Data Thank you, I've attached an image with a dot on the cluster I'm interested in. I could send you the dscalar file too, but prefer not to distribute to the whole listserv. Since we have a priori hypotheses about the IFG being involved in this task (it is a go/nogo task), I want test for an effect specifically in that region similar to the one shown that I am seeing. In volumetric analysis I would use an anatomically or functionally defined mask and either A) look for a region within that mask that survives cluster thresholding to localize where in that region is involved or B) test if average betas are different within that region between conditions. I was wondering if one of the parcels from your parcellation was something you know to be where response inhibition on tasks like go/nogo may be localized as a reason for using that parcellation or set of parcellations. Otherwise I would be interested in something like a combined mask of BA44 and BA45 as representing IFG. Also, is it possible to do something like TFCE or fdr correction within a region like this within palm as that would reduce the area I am looking over for an effect and reduce the cluster size needed compared to a whole brain analysis? Thank you, Michael ________________________________ From: Glasser, Matthew <glass...@wustl.edu<mailto:glass...@wustl.edu>> Sent: Monday, September 26, 2016 2:18:26 PM To: Michael F.W. Dreyfuss; NEUROSCIENCE tim Cc: hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org> Subject: Re: [HCP-Users] ROIs and Betas from Cifti Data Is there a surface-based map of this task contrast somewhere that I could look at? Without that I would just be guessing… Peace, Matt. From: "Michael F.W. Dreyfuss" <mid2...@med.cornell.edu<mailto:mid2...@med.cornell.edu>> Date: Monday, September 26, 2016 at 1:13 PM To: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>>, Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Cc: "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: Re: [HCP-Users] ROIs and Betas from Cifti Data Hi Matt, I'm interested in using part of your parcellation to look at response inhibition on a go/nogo task. Do you have a sense of which region in your parcellation corresponds best to the part of inferior frontal gyrus typically implemented in successful nogo response inhibition, and how I could isolate that parcel to do an anlysis? Thank you, Michael ________________________________ From: Glasser, Matthew <glass...@wustl.edu<mailto:glass...@wustl.edu>> Sent: Tuesday, September 20, 2016 11:06:10 AM To: Michael F.W. Dreyfuss; NEUROSCIENCE tim Cc: hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org> Subject: Re: [HCP-Users] ROIs and Betas from Cifti Data As Tim mentions, it sounds like you might want to use a parcellated analysis, as this will be more sensitive/powerful and you’ll know exactly what areas you are finding. The HCP’s multi-modal parcellation is available here: https://balsa.wustl.edu/study/show/RVVG<https://urldefense.proofpoint.com/v2/url?u=https-3A__balsa.wustl.edu_study_show_RVVG&d=DQMF-g&c=lb62iw4YL4RFalcE2hQUQealT9-RXrryqt9KZX2qu2s&r=rPclmYysc_z1plf99IoNsmxWf1JolkKMmL6bXnYFSwg&m=41tIMVdkyw4FbaKBCuAemq30kaX7FtpSn1fT4mnNgb4&s=LSobQp1e_k82tOjzxrgaFuayanqOIOB0FPCI128My64&e=> Also, the HCP’s task analysis pipeline will allow you to parcellate before fitting the GLM, rather than afterwards to get the addition SNR benefits from averaging across a parcel. Peace, Matt. From: <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of "Michael F.W. Dreyfuss" <mid2...@med.cornell.edu<mailto:mid2...@med.cornell.edu>> Date: Monday, September 19, 2016 at 9:40 PM To: Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Cc: "Michael F.W. Dreyfuss" <mid2...@med.cornell.edu<mailto:mid2...@med.cornell.edu>>, "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: Re: [HCP-Users] ROIs and Betas from Cifti Data Thank you, Are there any examples available for how to use this, possibly? I have been trying to figure these out, but there are a lot of options and I am just not able to decipher how to use these from the help page alone. The errors I am getting also do not clarify what I need to do to get the output I am looking for. Before using this kind of multi-band data I had been using afni. To give an example of what I would want to do in terms of afni commands (if that’s any help), I would have saved all ROIs and then used 3dmaskave to extract mean beta weights for a given GLM beta for each subject and then I would relate those beta weights so subject’s behavior in R or another stats package. Definitely agree that there’s not much meaning to a peak coordinate per se. I’m just trying to figure out how to report the clusters I am finding. In previous reports we would typically focus on broadmann areas or more general regional nomenclature (i.e. vmPFC, mid temporal lobe, etc.). Some of the clusters I’m finding also cover large areas from motor to visual cortex, so I am trying to consider good ways to report that. At this point I would prefer to use TFCE or some other thresholding method to identify contiguous swaths of volumetric and surface activation. Thank you very much again, Michael On Sep 19, 2016, at 6:41 PM, Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> wrote: On Mon, Sep 19, 2016 at 4:51 PM, Michael F.W. Dreyfuss <mid2...@med.cornell.edu<mailto:mid2...@med.cornell.edu>> wrote: Thank you, How can I turn the ROIs into a label file? You can use -cifti-find-clusters if you just want spatial contiguity to define where ROIs should be considered separate, then use -cifti-label-import to make them into a dlabel file. Also, how can I simply get a list of the ROIs with some information like cluster extent and peak voxel to be able to identify what part(s) of the brain each ROI is covering? A single coordinate isn't a faithful representation of the cluster. You can make a figure showing the clusters displayed on the brain (for instance, choose two of: beta maps, significance outlines, area outlines), and hopefully also provide the unthresholded beta and z maps for others to use. You can get cluster extent info with -cifti-weighted-stats. If the question you want to ask is "which areas are involved", you could do a parcellated analysis instead of a cluster analysis. Thank you, Michael ________________________________ From: Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Sent: Monday, September 19, 2016 4:48:28 PM To: Michael F.W. Dreyfuss Cc: hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org> Subject: Re: [HCP-Users] ROIs and Betas from Cifti Data The wb_command -cifti-weighted-stats command with -mean is probably what you want (outputs a number to the command line), though you'll need to have each ROI as a separate file and run it separately for each of them. Alternatively, if you turned the ROIs into a label file, you could get -cifti-parcellate to make a file where each parcel contains the answer for an ROI. Tim On Mon, Sep 19, 2016 at 3:26 PM, Michael F.W. Dreyfuss <mid2...@med.cornell.edu<mailto:mid2...@med.cornell.edu>> wrote: Hello, I have run palm with TFCE on my group level data successfully for a task based fMRI study (yay!), and I would like to be able to identify ROIs from my cifti data (both surface and volume). I then want to extract subject level beta weights for a given condition from those ROIs to relate those betas to behavior (offline). Are there simple ways to: 1) identify regions implicated on the group level and 2) extract subject-level beta weights from them, such as with wb_command? 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