You would be running sICA+FIX after ciftify. I believe this would work, though if the data are high temporal and spatial resolution fMRI data, you could not use the HCP’s training file and would have to investigate other training files.
Matt. From: Zhi Li <lizhi.ps...@gmail.com<mailto:lizhi.ps...@gmail.com>> Date: Wednesday, November 28, 2018 at 7:38 PM To: Matt Glasser <glass...@wustl.edu<mailto:glass...@wustl.edu>> Cc: Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>>, "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>" <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: Re: [HCP-Users] HCP parcellation template Thank you very much Matt. Actually I am exploring the relative optimal preprocessing method for our task-based fMRI data which were collected with traditional protocols (without T2w image and fieldmap). We want to get the time series of task-based image from the HCP 360 rois (best based on the individual parcellation). You mentioned in another reply that a legacy mode of the minimal preprocessing pipelines is in work. May I know what is the difference between the ongoing pipelines and the ciftify? Another question is could I use the ciftify to do FIX ICA as the HCP pipelines? Thanks and best wishes, Zhi On Wed, 28 Nov 2018 at 19:45, Glasser, Matthew <glass...@wustl.edu<mailto:glass...@wustl.edu>> wrote: I believe FSFAST is a surface-capable analysis stream, so reanalysis might not be necessary vs figuring out how to translate between FSFAST and HCP-Style CIFTI stuff. I am sure that is possible but have not used FSFAST myself. 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 2:36 PM To: "lizhi.ps...@gmail.com<mailto:lizhi.ps...@gmail.com>" <lizhi.ps...@gmail.com<mailto:lizhi.ps...@gmail.com>> Cc: hcp-users <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>> Subject: Re: [HCP-Users] HCP parcellation template You can display the resampled metric on the surfaces from the group-average HCP "subject" to ensure that things are where they are supposed to be (note, resampling both the data and the surfaces of a subject is recommended, but will always look reasonable even if the wrong spheres were used - using the group average surfaces as a reference ensures that your data aligns with other data that is known to be resampled correctly). The HCP MMP v1.0 parcellation doesn't include subcortical data, so it that is the parcellation you want to use, you can ignore subcortical data for now. You can use wb_command -cifti-create-dense-from-template to make a cifti version of your data (you can use the parcellation .dlabel.nii file as the template). If you want to also look at the subcortical data in cifti format, then we would recommend reanalyzing your data using the HCP pipelines instead of FSFAST. If you haven't acquired the scans that the HCP pipelines require (high-res T2, fieldmaps), there is ciftify: https://github.com/edickie/ciftify This can probably be followed by the task analysis scripts in the HCP pipelines. Tim On Wed, Nov 28, 2018 at 11:20 AM Zhi Li <lizhi.ps...@gmail.com<mailto:lizhi.ps...@gmail.com>> wrote: Hi Tim, Thank you very much for your kind suggestion. After the preprocessing of FSFAST I got the left and right surface-based data in 'nii.gz' format. Following the instructions I converted them to .gii format with mris_convert first, then to func.gii with the command "wb_command -metric-resample <metric-in> <current-sphere> <new-sphere> ADAP_BARY_AREA <metric-out> -area-metrics <current-area> <new-area>". I am new to the HCP pipeline, may I ask what I missed and how could I further convert the "func.gii" to the "cifti format"? And how to check if this resample is right? The cifti file should also include the subcortical regions, right? Do I need to combine the left and right surface and subcortcial volume into one cifti file? Sorry for the very basic questions. Thank you very much. Best wishes, Zhi Li On Tue, 27 Nov 2018 at 16:09, Timothy Coalson <tsc...@mst.edu<mailto:tsc...@mst.edu>> wrote: The HCP MMP v1.0 parcellation is defined on MSMAll-registered surfaces. I am not familiar with FSFAST, but if it gives you surface-based data, you should be able to resample the data or parcellation so that they are on the same mesh, following these instructions: https://wiki.humanconnectome.org/display/PublicData/HCP+Users+FAQ#HCPUsersFAQ-9.HowdoImapdatabetweenFreeSurferandHCP? If you resample/convert your timeseries data to cifti .dtseries.nii in our standard grayordinates, then the wb_command -cifti-parcellate command can extract average timeseries of all the ROIs in our parcellation (or any parcellation represented in our standard grayordinates). Note that we do not recommend using any group-average volume methods for human cortical data, because volume-based registration doesn't deal particularly well with intersubject differences in cortical folding (or in functional location compared to folds): https://www.ncbi.nlm.nih.gov/pubmed/29925602 Tim On Mon, Nov 26, 2018 at 5:28 PM Zhi Li <lizhi.ps...@gmail.com<mailto:lizhi.ps...@gmail.com>> wrote: Hello HCP experts, I would like to extract time series from the FSFAST-preprocessed fMRI data (acquired with traditional scanning protocol) with the HCP average template, may I know where I could find it (annotation format) ? I read from another post that I could use mri_aparc2aseg to map it to the anatomical volume. Do you have any other suggestions on extracting time series with the HCP rois? Thank you very much. Best wishes, Zhi Li _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto:HCP-Users@humanconnectome.org> http://lists.humanconnectome.org/mailman/listinfo/hcp-users _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org<mailto: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. 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