Thank you Tim and Matt for all the information.
I currently am not using the HCP and unfortunately do not have the time to reprocess the data. I greatly appreciate the detailed explanation of the metric files and the difference between that and the label files. I am very new to surfaces and so everything is helpful! Just a couple more questions please. How do I know whether I want to use the 'COLUMN' or 'ROW' option? Is there a message board that I can look through other previously asked questions for future help? Thanks again, Jeanette ________________________________ From: Glasser, Matthew Sent: Wednesday, November 28, 2018 6:47 PM To: NEUROSCIENCE tim; Kenley, Jeanette Cc: hcp-users; Kaplan, Sydney Subject: Re: [HCP-Users] average dconn from individual dconns To be more specific: In the HCP we use a technique called MIGP to make group fMRI data and generate dense connectomes from that. Concatenated dense timeseries get very large when you have a lot of subjects and timepoints. Matt. From: <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>> on behalf of Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> Date: Wednesday, November 28, 2018 at 2:54 PM To: "Kenley, Jeanette" <jkken...@wustl.edu<mailto:jkken...@wustl.edu>> Cc: hcp-users <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>, "Kaplan, Sydney" <sydney.kap...@wustl.edu<mailto:sydney.kap...@wustl.edu>> Subject: Re: [HCP-Users] average dconn from individual dconns The HCP pipelines deliberately resample the subcortical data in such a way that the subcortical voxels used in each subject are the same, this is how we handle the problem you are having. If you concatenate your timeseries across subjects before correlation, you don't need to generate a dconn for every subject, so this strategy uses less disk space (but you have to be careful about normalization before concatenation). Label files are not ROI files, though they might work in this case (you could instead use the .shape.gii files here: https://github.com/Washington-University/HCPpipelines/tree/master/global/templates/91282_Greyordinates). An ROI file is supposed to be a metric file (.func.gii, .shape.gii) of just 1's and 0's (the actual logic used is "greater than zero"). Label files can be used as metric files, the warning is just to let you know that you did something unusual. I'd need to see the actual error message to comment on it. If you don't want to go back to the timeseries (or reprocess with the HCP pipelines), -cifti-restrict-dense-map may be the simplest solution. Tim On Wed, Nov 28, 2018 at 2:00 PM Kenley, Jeanette <jkken...@wustl.edu<mailto:jkken...@wustl.edu>> wrote: I am still new to the wb_command suite and trying to understand how to best use them. I have created an individual cifti (dconn.nii) for each of my subjects in 32kfslr and would like to make an average. I would like to use wb_command -cifti-average output.dconn.nii -cifti subject1.dconn.nii -cift subject2.dconn.nii . . .-cifti subjectN.dconn.nii But this results in a dimension mismatch since the sub cortical portions of the individual subjects are different. Is there a way to only grab the surface data from each of the individual dconns so I do not get the dimension error. I tried using wb_command -cifti-restrict-dense-map subjectN.dconn.nii 'ROW/COLUMN' subjectN_output.dconn.nii -left-roi $left -right-roi $right where the left and right rois are: subjectN.L.aparc.32k_fs_LR.label.gii subjectN.R.aparc.32k_fs_LR.label.gii but since I am newer to the wb_commands, I am not sure what each of the inputs should be or if this is the correct command. This also resulted in an error. WARNING: Metric File: subjectN.L.aparc.32k_fs_LR.label.gii contains data array with NIFTI_INTENT_LABEL !!! WARNING: Metric File: subjectN.R.aparc.32k_fs_LR.label.gii contains data array with NIFTI_INTENT_LABEL !!! As well as an error when I tried to use the file saying something about fields not being the same size. Or is there a way to generate the surface dconns via command line without the subcortical information so I don't get the dimension mismatch error when I try to make an average? Any help is greatly appreciated. And specific example file names are also very much appreciated. Thanks! ________________________________ 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. 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