If you want to match existing 4K data, you shouldn't create a new sphere, you should obtain the spheres that define the correspondence between the surfaces you want to use, and the atlas your data is currently on.
Also, note that wb_command -cifti-resample will allow you to skip the separate, resample, cifti-create sequence. This command is smart enough to notice when you are using label data, and do the right thing. For an extreme downsampling like this, it will likely be rather detrimental to the area definitions (if you have a summary measure you want to compare to the atlas, consider upsampling that summary measure instead), but the ADAP_BARY_AREA method should be somewhat better for downsampling (but requires a little extra work). If the outlines of the labels are what you want, then you can use border files instead - these aren't restricted to the vertices the way label files are, so the border file outlines will remain sharp when resampled. You can make border files from label files at their original resolution with wb_command -cifti-label-to-border and/or -label-to-border . Tim On Thu, Apr 12, 2018 at 7:37 AM, Georgios Michalareas <g...@ae.mpg.de> wrote: > Hi Benjamin, > > to resample from 165K and 32K to 4K you need to use wb_command from > connectome workbench software available on the connectome db website. > This is the actual documentation page of wb_command > > https://www.humanconnectome.org/software/workbench-command > > To downsample form 165K and 32K to a 4k representation you need a target > 4k sphere for each hemisphere (cortical surfaces are inflated to spheres > then downsampled and then deflated again). > To create such a sphere you can use something like the foillowing commands > > > downsampDir=/home/georgios.michalareas/workspace/ > projects/testHCPsMRIinMPIEA/wbdata_downsampled/ > > wb_command -surface-create-sphere 4000 ${downsampDir}Sphere.4k.R.surf.gii > wb_command -surface-flip-lr ${downsampDir}Sphere.4k.R.surf.gii > ${downsampDir}Sphere.4k.L.surf.gii > wb_command -set-structure ${downsampDir}Sphere.4k.R.surf.gii CORTEX_RIGHT > wb_command -set-structure ${downsampDir}Sphere.4k.L.surf.gii CORTEX_LEFT > > > Then once you have these spheres you can downsample a label file with > something like (commands on a linux terminal) > wb_command -cifti-separate EZE09.aparc.164k_fs_LR.dlabel.nii COLUMN > -label CORTEX_LEFT TMPOLD_LEFT_EZE09.aparc.164k.label.gii > wb_command -cifti-separate EZE09.aparc.164k_fs_LR.dlabel.nii COLUMN > -label CORTEX_RIGHT TMPOLD_RIGHT_EZE09.aparc.164k.label.gii > wb_command -label-resample TMPOLD_LEFT_EZE09.aparc.164k.label.gii > EZE09.L.sphere.164k_fs_LR.surf.gii Sphere.4k.L.surf.gii BARYCENTRIC > TMPNEW_LEFT_EZE09.aparc.164k.label.gii > wb_command -label-resample TMPOLD_RIGHT_EZE09.aparc.164k.label.gii > EZE09.R.sphere.164k_fs_LR.surf.gii Sphere.4k.R.surf.gii BARYCENTRIC > TMPNEW_RIGHT_EZE09.aparc.164k.label.gii > wb_command -cifti-create-label EZE09.aparc.a2009s.4k_fs_LR.dlabel.nii > -left-label TMPNEW_LEFT_EZE09.aparc.164k.label.gii -right-label > TMPNEW_RIGHT_EZE09.aparc.164k.label.gii > > # delete temporary files > rm -f TMPOLD_LEFT_EZE09.aparc.164k.label.gii > rm -f TMPOLD_RIGHT_EZE09.aparc.164k.label.gii > rm -f TMPNEW_LEFT_EZE09.aparc.164k.label.gii > rm -f TMPNEW_RIGHT_EZE09.aparc.164k.label.gii > > The parcellation files from Freesurfer atlases , including > Desikan-Killiany, i.e. EZE09.aparc.164k_fs_LR.dlabel.nii, as well as the > 164K spheres required for downsampling , i.e. > EZE09.L.sphere.164k_fs_LR.surf.gii, > should be in the latest processed structural mri data that you download > from connectome db. Look in the "MNINonLinear" folder. > > I hope these helped > Best > Giorgos > > > > > On 4/10/2018 4:25 PM, Benjamin Chiêm wrote: > > Dear Georgios, > > > > Thank you again for your very helpful answer. > > I have succeeded in parcellating my results using the Yeo et al. atlas, > with the *.dlabel.nii file provided with the parcellated HCP MEG data (107 > nodes), and with the wb_command –cifti-parcellate line code. > > > > However, I have troubles to find equivalent *.dlabel.nii files for other > parcellations scheme, like for the example the Desikan-Killiany atlas. > Could you help me? Also, should I just use the ft_resampledata Fieldtrip > function in order to go from the 165K or 32K to the 4K dlabel files, or am > I missing something? > > > > Regards, > > > > Benjamin > > > > *De :* Georgios Michalareas <g...@ae.mpg.de> <g...@ae.mpg.de> > *Envoyé :* lundi 26 mars 2018 19:20 > *À :* Benjamin Chiêm <benjamin.ch...@uclouvain.be> > <benjamin.ch...@uclouvain.be>; hcp-users@humanconnectome.org > *Objet :* Re: [HCP-Users] MEG processing pipelines > > > > Hi Benjamin, > > Sorry for late reply but I was out of office. > > Regarding single trial source analysis I have put together some code that > shows you how to project all motor trials for LH into source space in a > matrix with dimensions: > > Nsources * Ntimespoints * Ntrials > > beware this matrix for the specific subject is 11GB. > > Please find the code attached in file > > testsourcepertrial1.m > > I have put some comments i hope they help. > > > > Regarding parcellating the data, you have to use the workbench command > tool to donwload all parcelations from thw 165 K representation to the 4K > one used for the MEG source level analysis. > > I ll have a look and come back to you on this. > > > > Till then I hope the code I am sending you helps > > Best > > Giorgos > > > > On 3/16/2018 3:38 PM, Benjamin Chiêm wrote: > > Dear HCP experts, > > > > In the context of my PhD thesis (using HCP data), I had some questions > about MEG processing pipelines. > > In my research, I somehow need the MEG data during motor task, in the > source space (i.e. after beamforming), for *each trial* and each > individual *separately*. It seems that the MEG pipelines average data > over trials for each individual, and that they reduce the temporal > resolution of the data when going from 'tmegpreproc' files to 'srcavglcmv’. > > > > So my two questions are: > > > > - Is it possible, from the 'tmegpreproc' file for each individual, to > perform sources reconstruction with Linearly Constrained Minimum Variance > beamformer for each trial independently, and to keep the original temporal > resolution (about 500Hz)? What are the steps to follow? I tried to play > with options like « cfg.rawtrial = yes » or « cfg.keeptrials = yes » in > ft_timelockanalysis and ft_sourceanalysis of the hcp_srcavglcmv_contrasts.m > file, but to be honest I am not sure of what I am doing… > > > > - After projection on the sources space, how can I parcellate the data, > using for example the Desikan-Killiany atlas? > > > > I hope I am clear enough in my explanations. Thank you again for the > amazing work you’re doing! > > > > Regards, > > > > Benjamin Chiêm > > > > _______________________________________________ > HCP-Users mailing list > HCP-Users@humanconnectome.org > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > > > > > <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> > > Virus-free. www.avg.com > <http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient> > > > > -- > > ------------------------------------------------ > > Dr. Georgios Michalareas > > Neuroscience Department > > Max Planck Institute for Empirical Aesthetics > > > > email: g...@aesthetics.mpg.de > > phone: +49 69 8300479-325 > > ------------------------------------------------ > > > -- > ------------------------------------------------ > Dr. Georgios Michalareas > Neuroscience Department > Max Planck Institute for Empirical Aesthetics > > email: g...@aesthetics.mpg.de > phone: +49 69 8300479-325 > ------------------------------------------------ > > _______________________________________________ > HCP-Users mailing list > HCP-Users@humanconnectome.org > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
