# freesurferator: create anatomical ROIs for DWI, fMRI, PET... in subject space
## Overview
### Summary
*This gear takes either an existing FreeSurfer zipped run, or an anatomical NIfTI file
and performs all of the FreeSurfer cortical reconstruction process. Outputs are
provided in a zip file and include the entire output directory tree from `recon-all`.
Configuration options exist for setting the subject ID and for converting outputs to
NIfTI, and CSV. FreeSurfer is a software package for the analysis and visualization of
structural and functional neuroimaging data from cross-sectional or longitudinal
studies. It is developed by the Laboratory for Computational Neuroimaging at the
Athinoula A. Martinos Center for Biomedical Imaging. Please see
<https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense> for license
information. Additionally, this gear includes a bunch of atlases included (cerebellum,
HCP, Neuropythy...) and user created ROIs in surface (fsaverage) or volumetric (MNI)
space can be provided. Depending on the configuration options it will create ROIs in
subject space that can later be incorporated to other neuroimaging tools (PETsurfer,
RTP2 DWI pipeline...)*
### Cite
*For citation information, please visit: <https://www.biorxiv.org/content/10.1101/2022.03.17.484761v1>*
*License:* *Other*
### Classification
*Category:* *analysis*
*Gear Level:*
- [x] Project
- [ ] Subject
- [ ] Session
- [ ] Acquisition
- [x] Analysis
----
[[*TOC*]]
----
### Inputs
- *anat*
- __Name__: *anat*
- __Type__: *nifti*
- __Optional__: *false*
- __Classification__: **
- __Description__: *Anatomical NIfTI file.*
- __Notes__: **
- *pre_fs*
- __Name__: *pre_fs*
- __Type__: *zip*
- __Optional__: *true*
- __Classification__: **
- __Description__: *ZIP file with a full freesurfer run*
- __Notes__: **
- *control_points*
- __Name__: *control_points*
- __Type__: *dat*
- __Optional__: *true*
- __Classification__: **
- __Description__: *Text file with the control points created in Freeview. You can
find them in the tmp/ folder once you create them in Freeview. If there is a
control points file and it is correct, Freesurfer will use them. Be careful to
add the proper recon-all call. The file name needs to be named control.dat,
and you need to copy it from the tmp/ folder, if it is not the case, the gear
will fail.*
- __Notes__: **
- *mniroizip*
- __Name__: *mniroizip*
- __Type__: *zip*
- __Optional__: *true*
- __Classification__: **
- __Description__: *ZIP file with ROIs in MNI space. If this zip exists (no folder
names, all ROIs in base folder after unzipping), the ROIs will be converted to
individual subject space. The ROIs need to be in FSL's 1mm isotropic MNI
template space. Please check with `mri_info` (Freesurfer) or `mrinfo` (MRtrix)
or an equivalent software that your ROI and the template are exactly in the
same space. The ROIs should be composed of 0-s and 1-s. For downloading an
example an several ROIs, please check <https://osf.io/download/m6b7r>*
- __Notes__: **
- *annotfile*
- __Name__: *annotfile*
- __Type__: *zip*
- __Optional__: *true*
- __Classification__: **
- __Description__: *zip with annot files in fsaverage space. Create surface
ROIs in fsaverage and save them as annots and them zip them, the system will
create cortical volumetric independent ROIs in subject space*
- __Notes__: **
- *t1w_anatomical_2*
- __Name__: *t1w_anatomical_2*
- __Type__: *nifti*
- __Optional__: *true*
- __Classification__: **
- __Description__: *Additional anatomical NIfTI file*
- __Notes__: **
- *t1w_anatomical_3*
- __Name__: *t1w_anatomical_3*
- __Type__: *nifti*
- __Optional__: *true*
- __Classification__: **
- __Description__: *Additional anatomical NIfTI file*
- __Notes__: **
- *t1w_anatomical_4*
- __Name__: *t1w_anatomical_4*
- __Type__: *nifti*
- __Optional__: *true*
- __Classification__: **
- __Description__: *Additional anatomical NIfTI file*
- __Notes__: **
- *t1w_anatomical_5*
- __Name__: *t1w_anatomical_5*
- __Type__: *nifti*
- __Optional__: *true*
- __Classification__: **
- __Description__: *Additional anatomical NIfTI file*
- __Notes__: **
- *freesurfer_license_file*
- __Name__: *freesurfer_license_file*
- __Type__: *file*
- __Optional__: *true*
- __Classification__: *{Based on "inputs.Input-File.base"}*
- __Description__: *FreeSurfer license file, provided during registration
with FreeSurfer. This file will be copied to the $FSHOME directory and used
during execution of the Gear.*
- __Notes__: **
- *t2w_anatomical*
- __Name__: *t2w_anatomical*
- __Type__: *nifti*
- __Optional__: *true*
- __Classification__: **
- __Description__: *T2w Anatomical NIfTI file*
- __Notes__: **
### Config
- *subject_id*
- __Name__: *subject_id*
- __Type__: *string*
- __Description__: *Desired subject ID. Any spaces in the subject_id will be replaced
with underscores and will be used to name the resulting FreeSurfer output
directory. NOTE: If using a previous Gear output as input the subject code
will be parsed from the input archive, however it should still be provided
here for good measure.*
- __Default__: *Sxxx*
- *reconall_options*
- __Name__: *reconall_options*
- __Type__: *string*
- __Description__: *Command line options to the `recon-all` algorithm. (Default=`-all
-qcache`. `-all` runs the entire pipeline and `-qcache` will resample data onto the
average subject (called fsaverage) and smooth it at various full-width/half-max
(FWHM) values, usually 0, 5, 10, 15, 20, and 25mm, which can speed later
processing.) Note that modification of these options may result in failure
if the options are not recognized. Note: if the optional file control_points
are included, then it will write it in the tmp/ folder and it will re-reun it
with the options set up in this field. For example, if control points are added
for intensity correction, then -autorecon2-cp and -autorecon3 will be expected
here instead of -all.*
- __Default__: *-all -qcache*
- *hippocampal_subfields*
- __Name__: *hippocampal_subfields*
- __Type__: *boolean*
- __Description__: *Generates an automated segmentation of the hippocampal subfields
based on a statistical atlas built primarily upon ultra-high resolution (~0.1 mm
isotropic) ex vivo MRI data. Choosing this option will write
`<subject_id>_HippocampalSubfields.csv` to the final results. See:
<https://surfer.nmr.mgh.harvard.edu/fswiki/HippocampalSubfields> for more info.
(Default=false)*
- __Default__: *false*
- *brainstem_structures*
- __Name__: *brainstem_structures*
- __Type__: *boolean*
- __Description__: *Generate automated segmentation of four different brainstem
structures from the input T1 scan: medulla oblongata, pons, midbrain and
superior cerebellar peduncle (SCP). We use a Bayesian segmentation algorithm
that relies on a probabilistic atlas of the brainstem (and neighboring brain
structures) built upon manual delineations of the structures on interest
in 49 scans (10 for the brainstem structures, 39 for the surrounding
structures). The delineation protocol for the brainstem was designed by
Dr. Adam Boxer and his team at the UCSF Memory and Aging Center, and is
described in the paper. Choosing this option will write
`<subject_id>_BrainstemStructures.csv` to the final results. See:
<https://surfer.nmr.mgh.harvard.edu/fswiki/BrainstemSubstructures> for more
info. (Default=false)*
- __Default__: *false*
- *thalamic_nuclei*
- __Name__: *thalamic_nuclei*
- __Type__: *boolean*
- __Description__: *Generate parcellation of the thalamus into 25 different nuclei,
using a probabilistic atlas built with histological data. The parcellation
is based on structural MRI, either the main T1 scan processed through
recon-all, or an additional scan of a different modality, which potentially
shows better contrast between the nuclei. Choosing this option will write
<subject_id>_thalamic-nuclei.lh.v10.T1.csv and
<subject_id>_thalamic-nuclei.rh.v10.T1.stats.csv to the final results. See:
<https://surfer.nmr.mgh.harvard.edu/fswiki/ThalamicNuclei> for more info.
(Default=false)*
- __Default__: *false*
- *cerebellum*
- __Name__: *cerebellum*
- __Type__: *boolean*
- __Description__: *bring CerebellumParcellation-Bucker2011 (17Networks LooseMask)
into native space, and generate separate image files for each volume*
- __Default__: *false*
- *hcp*
- __Name__: *hcp*
- __Type__: *boolean*
- __Description__: *bring MNI_Glasser_HCP into native space, and generate separate
image files for each volume.*
- __Default__: *false*
- *mni_rois*
- __Name__: *mni_rois*
- __Type__: *boolean*
- __Description__: *Bring MNI ROIs such as MNI_JHU_tracts_ROIs, custom created and
other ROIs into native space. If no `mniroizip` input is passed and this is
set to True, it will use the default Mori ROIs, CC ROIs and some other (such
as eyes or the mOTS and pOTS VWFAs). Download the zip from the Freesurferator
repo or check the README for more information. If this is set to True and a
`mniroizip` input is passed, then both will be added and converted to
subject's space. If it is set to False and `mniroizip` is passed only those
MNI ROIs will be converted.*
- __Default__: *false*
- *aparc2009*
- __Name__: *aparc2009*
- __Type__: *boolean*
- __Description__: *separate the aparc.a2009 from freesurfer to individual images for
each segment*
- __Default__: *false*
- *rois_in_output*
- __Name__: *rois_in_output*
- __Type__: *boolean*
- __Description__: *Depending on the selection there can be hundreds of ROIs,
therefore they will be all zipped in the `fs.zip` archive in the `/ROIs`
folder. In some cases (few ROIs, debugging) it might be of interest having the
ROIs in the output. Default is false.*
- __Default__: *false*
- *neuropythy_analysis*
- __Name__: *neuropythy_analysis*
- __Type__: *boolean*
- __Description__: *Perform a neuropythy analysis. See:
<https://github.com/noahbenson/neuropythy> for more info. (Default=false)*
- __Default__: *false*
- *run_gtmseg*
- __Name__: *run_gtmseg*
- __Type__: *boolean*
- __Description__: *Run gtmseg, it is a step required for PETsurfer. By default, is
false.*
- __Default__: *false*
- *force_ants*
- __Name__: *force_ants*
- __Type__: *boolean*
- __Description__: *Ants will be automatically done depending on what segmentations
are asked. If this options is set to true, ANTs will be run regardless of
those options. Default=false*
- __Default__: *false*
- *freesurfer_license_key*
- __Name__: *freesurfer_license_key*
- __Type__: *string*
- __Description__: *Text from license file generated during FreeSurfer registration.
Entries should be space separated. Usually this should be added at the project
level and it will be read from there.*
- __Default__: **
### Outputs
#### Files
- *freesurferator_<subject_id>_<date>.zip*
- __Name__: *freesurferator_<subject_id>_<date>.zip*
- __Type__: *zip*
- __Optional__: *false*
- __Classification__: *zip*
- __Description__: *Freesurfer's complete output, zipped*
- __Notes__: **
- *fs.zip*
- __Name__: *fs.zip*
- __Type__: *zip*
- __Optional__: *false*
- __Classification__: *zip*
- __Description__: *All ROIs in subject space, that can be used in other analyses.
On top of that, it contains T1w file, the MNI template and other files.*
- __Notes__: **
- *T1.nii.gz*
- __Name__: *T1.nii.gz*
- __Type__: *nifti*
- __Optional__: *false*
- __Classification__: *nifti*
- __Description__: *T1w image, from Freesurfer's output*
- __Notes__: **
There are other files in the output, but most of them depend on the config options.
They should be auto-explicative.
#### Metadata
This gear does not generate metadata
### Pre-requisites
It has no prerequisites, more than having a nifti file
#### Prerequisite Gear Runs
A list of gears, in the order they need to be run:
1. __*dcm2niix*
- Level: *Acquisition*
#### Prerequisite Files
A list of any files (OTHER than those specified by the input) that the gear will need.
If possible, list as many specific files as you can:
None
#### Prerequisite Metadata
A description of any metadata that is needed for the gear to run.
If possible, list as many specific metadata objects that are required:
None
## Usage
This gear is used directly or as a Gear Rule. It has only one required file, the T1w.
This is actually not always necessary. If a T1w and a previously run freesurfer zip
is passed, it will ignore the T1w and unzip the fs_zip and go from there.
### Description
*In the most basic form it just runs freesurfer. If a fs_zip is passed, not even that.
On top of those options, it is possible to select many other segmentations and atlases
to obtain innumerable ROIs. It is possible to pass a zip with MNI ROIs that the gear
will convert to subject space ROIs. It is possible to pass a zip with fsaverage
annotfiles, and the gear will generate cortical volumetric ROIs as well.*
### Workflow
There is no workflow, it runs recon_all or unzips de zipped freesurfer output,
and then the rest of the analyses are optional and it depends on what the
researcher wants.
Description of workflow
1. Upload file to container
1. Select file as input to gear
1. Geat places output in Analysis
### Logging
There are several logs, depending on what has been run. If recon_all is being run,
there will be a huge fresurfer log. Then, depending on the selected options,
there will be sections per every groups of ROIs that have been asked to calculate.
## FAQ
[FAQ.md](FAQ.md)
## Contributing
[For more information about how to get started contributing to that gear,
checkout [CONTRIBUTING.md](CONTRIBUTING.md).]
Raw data
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"requires_python": "<4.0,>=3.9",
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"keywords": "Flywheel, Gears",
"author": "Flywheel",
"author_email": "support@flywheel.io",
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"platform": null,
"description": "# freesurferator: create anatomical ROIs for DWI, fMRI, PET... in subject space\n\n## Overview\n\n### Summary\n\n*This gear takes either an existing FreeSurfer zipped run, or an anatomical NIfTI file\nand performs all of the FreeSurfer cortical reconstruction process. Outputs are\nprovided in a zip file and include the entire output directory tree from `recon-all`.\nConfiguration options exist for setting the subject ID and for converting outputs to\nNIfTI, and CSV. FreeSurfer is a software package for the analysis and visualization of\nstructural and functional neuroimaging data from cross-sectional or longitudinal\nstudies. It is developed by the Laboratory for Computational Neuroimaging at the\nAthinoula A. Martinos Center for Biomedical Imaging. Please see\n<https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense> for license\ninformation. Additionally, this gear includes a bunch of atlases included (cerebellum,\nHCP, Neuropythy...) and user created ROIs in surface (fsaverage) or volumetric (MNI)\nspace can be provided. Depending on the configuration options it will create ROIs in\nsubject space that can later be incorporated to other neuroimaging tools (PETsurfer,\nRTP2 DWI pipeline...)*\n\n### Cite\n\n*For citation information, please visit: <https://www.biorxiv.org/content/10.1101/2022.03.17.484761v1>*\n*License:* *Other*\n\n### Classification\n\n*Category:* *analysis*\n\n*Gear Level:*\n\n- [x] Project\n- [ ] Subject\n- [ ] Session\n- [ ] Acquisition\n- [x] Analysis\n\n----\n\n[[*TOC*]]\n\n----\n\n### Inputs\n\n- *anat*\n - __Name__: *anat*\n - __Type__: *nifti*\n - __Optional__: *false*\n - __Classification__: **\n - __Description__: *Anatomical NIfTI file.*\n - __Notes__: **\n\n- *pre_fs*\n - __Name__: *pre_fs*\n - __Type__: *zip*\n - __Optional__: *true*\n - __Classification__: **\n - __Description__: *ZIP file with a full freesurfer run*\n - __Notes__: **\n \n- *control_points*\n - __Name__: *control_points*\n - __Type__: *dat*\n - __Optional__: *true*\n - __Classification__: **\n - __Description__: *Text file with the control points created in Freeview. You can\n find them in the tmp/ folder once you create them in Freeview. If there is a\n control points file and it is correct, Freesurfer will use them. Be careful to\n add the proper recon-all call. The file name needs to be named control.dat,\n and you need to copy it from the tmp/ folder, if it is not the case, the gear\n will fail.*\n - __Notes__: **\n\n- *mniroizip*\n - __Name__: *mniroizip*\n - __Type__: *zip*\n - __Optional__: *true*\n - __Classification__: **\n - __Description__: *ZIP file with ROIs in MNI space. If this zip exists (no folder\n names, all ROIs in base folder after unzipping), the ROIs will be converted to\n individual subject space. The ROIs need to be in FSL's 1mm isotropic MNI\n template space. Please check with `mri_info` (Freesurfer) or `mrinfo` (MRtrix)\n or an equivalent software that your ROI and the template are exactly in the\n same space. The ROIs should be composed of 0-s and 1-s. For downloading an\n example an several ROIs, please check <https://osf.io/download/m6b7r>*\n - __Notes__: **\n \n- *annotfile*\n - __Name__: *annotfile*\n - __Type__: *zip*\n - __Optional__: *true*\n - __Classification__: **\n - __Description__: *zip with annot files in fsaverage space. Create surface\n ROIs in fsaverage and save them as annots and them zip them, the system will\n create cortical volumetric independent ROIs in subject space*\n - __Notes__: **\n \n- *t1w_anatomical_2*\n - __Name__: *t1w_anatomical_2*\n - __Type__: *nifti*\n - __Optional__: *true*\n - __Classification__: **\n - __Description__: *Additional anatomical NIfTI file*\n - __Notes__: **\n \n- *t1w_anatomical_3*\n - __Name__: *t1w_anatomical_3*\n - __Type__: *nifti*\n - __Optional__: *true*\n - __Classification__: **\n - __Description__: *Additional anatomical NIfTI file*\n - __Notes__: **\n \n- *t1w_anatomical_4*\n - __Name__: *t1w_anatomical_4*\n - __Type__: *nifti*\n - __Optional__: *true*\n - __Classification__: **\n - __Description__: *Additional anatomical NIfTI file*\n - __Notes__: **\n \n- *t1w_anatomical_5*\n - __Name__: *t1w_anatomical_5*\n - __Type__: *nifti*\n - __Optional__: *true*\n - __Classification__: **\n - __Description__: *Additional anatomical NIfTI file*\n - __Notes__: **\n \n- *freesurfer_license_file*\n - __Name__: *freesurfer_license_file*\n - __Type__: *file*\n - __Optional__: *true*\n - __Classification__: *{Based on \"inputs.Input-File.base\"}*\n - __Description__: *FreeSurfer license file, provided during registration\n with FreeSurfer. This file will be copied to the $FSHOME directory and used\n during execution of the Gear.*\n - __Notes__: **\n \n- *t2w_anatomical*\n - __Name__: *t2w_anatomical*\n - __Type__: *nifti*\n - __Optional__: *true*\n - __Classification__: **\n - __Description__: *T2w Anatomical NIfTI file*\n - __Notes__: ** \n\n### Config\n\n- *subject_id*\n - __Name__: *subject_id*\n - __Type__: *string*\n - __Description__: *Desired subject ID. Any spaces in the subject_id will be replaced\n with underscores and will be used to name the resulting FreeSurfer output\n directory. NOTE: If using a previous Gear output as input the subject code\n will be parsed from the input archive, however it should still be provided\n here for good measure.*\n - __Default__: *Sxxx*\n\n- *reconall_options*\n - __Name__: *reconall_options*\n - __Type__: *string*\n - __Description__: *Command line options to the `recon-all` algorithm. (Default=`-all\n -qcache`. `-all` runs the entire pipeline and `-qcache` will resample data onto the\n average subject (called fsaverage) and smooth it at various full-width/half-max\n (FWHM) values, usually 0, 5, 10, 15, 20, and 25mm, which can speed later\n processing.) Note that modification of these options may result in failure\n if the options are not recognized. Note: if the optional file control_points\n are included, then it will write it in the tmp/ folder and it will re-reun it\n with the options set up in this field. For example, if control points are added\n for intensity correction, then -autorecon2-cp and -autorecon3 will be expected\n here instead of -all.*\n - __Default__: *-all -qcache*\n\n- *hippocampal_subfields*\n - __Name__: *hippocampal_subfields*\n - __Type__: *boolean*\n - __Description__: *Generates an automated segmentation of the hippocampal subfields\n based on a statistical atlas built primarily upon ultra-high resolution (~0.1 mm\n isotropic) ex vivo MRI data. Choosing this option will write\n `<subject_id>_HippocampalSubfields.csv` to the final results. See:\n <https://surfer.nmr.mgh.harvard.edu/fswiki/HippocampalSubfields> for more info.\n (Default=false)*\n - __Default__: *false*\n\n- *brainstem_structures*\n - __Name__: *brainstem_structures*\n - __Type__: *boolean*\n - __Description__: *Generate automated segmentation of four different brainstem\n structures from the input T1 scan: medulla oblongata, pons, midbrain and\n superior cerebellar peduncle (SCP). We use a Bayesian segmentation algorithm\n that relies on a probabilistic atlas of the brainstem (and neighboring brain\n structures) built upon manual delineations of the structures on interest\n in 49 scans (10 for the brainstem structures, 39 for the surrounding\n structures). The delineation protocol for the brainstem was designed by\n Dr. Adam Boxer and his team at the UCSF Memory and Aging Center, and is\n described in the paper. Choosing this option will write\n `<subject_id>_BrainstemStructures.csv` to the final results. See:\n <https://surfer.nmr.mgh.harvard.edu/fswiki/BrainstemSubstructures> for more\n info. (Default=false)*\n - __Default__: *false*\n\n- *thalamic_nuclei*\n - __Name__: *thalamic_nuclei*\n - __Type__: *boolean*\n - __Description__: *Generate parcellation of the thalamus into 25 different nuclei,\n using a probabilistic atlas built with histological data. The parcellation\n is based on structural MRI, either the main T1 scan processed through\n recon-all, or an additional scan of a different modality, which potentially\n shows better contrast between the nuclei. Choosing this option will write\n <subject_id>_thalamic-nuclei.lh.v10.T1.csv and\n <subject_id>_thalamic-nuclei.rh.v10.T1.stats.csv to the final results. See:\n <https://surfer.nmr.mgh.harvard.edu/fswiki/ThalamicNuclei> for more info.\n (Default=false)*\n - __Default__: *false*\n\n- *cerebellum*\n - __Name__: *cerebellum*\n - __Type__: *boolean*\n - __Description__: *bring CerebellumParcellation-Bucker2011 (17Networks LooseMask)\n into native space, and generate separate image files for each volume*\n - __Default__: *false*\n\n- *hcp*\n - __Name__: *hcp*\n - __Type__: *boolean*\n - __Description__: *bring MNI_Glasser_HCP into native space, and generate separate\n image files for each volume.*\n - __Default__: *false*\n\n- *mni_rois*\n - __Name__: *mni_rois*\n - __Type__: *boolean*\n - __Description__: *Bring MNI ROIs such as MNI_JHU_tracts_ROIs, custom created and\n other ROIs into native space. If no `mniroizip` input is passed and this is\n set to True, it will use the default Mori ROIs, CC ROIs and some other (such\n as eyes or the mOTS and pOTS VWFAs). Download the zip from the Freesurferator\n repo or check the README for more information. If this is set to True and a\n `mniroizip` input is passed, then both will be added and converted to\n subject's space. If it is set to False and `mniroizip` is passed only those\n MNI ROIs will be converted.*\n - __Default__: *false*\n\n- *aparc2009*\n - __Name__: *aparc2009*\n - __Type__: *boolean*\n - __Description__: *separate the aparc.a2009 from freesurfer to individual images for\n each segment*\n - __Default__: *false*\n\n- *rois_in_output*\n - __Name__: *rois_in_output*\n - __Type__: *boolean*\n - __Description__: *Depending on the selection there can be hundreds of ROIs,\n therefore they will be all zipped in the `fs.zip` archive in the `/ROIs`\n folder. In some cases (few ROIs, debugging) it might be of interest having the\n ROIs in the output. Default is false.*\n - __Default__: *false*\n\n- *neuropythy_analysis*\n - __Name__: *neuropythy_analysis*\n - __Type__: *boolean*\n - __Description__: *Perform a neuropythy analysis. See:\n <https://github.com/noahbenson/neuropythy> for more info. (Default=false)*\n - __Default__: *false*\n\n- *run_gtmseg*\n - __Name__: *run_gtmseg*\n - __Type__: *boolean*\n - __Description__: *Run gtmseg, it is a step required for PETsurfer. By default, is\n false.*\n - __Default__: *false*\n\n- *force_ants*\n - __Name__: *force_ants*\n - __Type__: *boolean*\n - __Description__: *Ants will be automatically done depending on what segmentations\n are asked. If this options is set to true, ANTs will be run regardless of\n those options. Default=false*\n - __Default__: *false*\n\n- *freesurfer_license_key*\n - __Name__: *freesurfer_license_key*\n - __Type__: *string*\n - __Description__: *Text from license file generated during FreeSurfer registration.\n Entries should be space separated. Usually this should be added at the project\n level and it will be read from there.*\n - __Default__: **\n\n### Outputs\n\n#### Files\n\n- *freesurferator_<subject_id>_<date>.zip*\n - __Name__: *freesurferator_<subject_id>_<date>.zip*\n - __Type__: *zip*\n - __Optional__: *false*\n - __Classification__: *zip*\n - __Description__: *Freesurfer's complete output, zipped*\n - __Notes__: **\n\n- *fs.zip*\n - __Name__: *fs.zip*\n - __Type__: *zip*\n - __Optional__: *false*\n - __Classification__: *zip*\n - __Description__: *All ROIs in subject space, that can be used in other analyses.\n On top of that, it contains T1w file, the MNI template and other files.*\n - __Notes__: **\n\n- *T1.nii.gz*\n - __Name__: *T1.nii.gz*\n - __Type__: *nifti*\n - __Optional__: *false*\n - __Classification__: *nifti*\n - __Description__: *T1w image, from Freesurfer's output*\n - __Notes__: **\n\nThere are other files in the output, but most of them depend on the config options.\nThey should be auto-explicative.\n\n#### Metadata\n\nThis gear does not generate metadata\n\n### Pre-requisites\n\nIt has no prerequisites, more than having a nifti file\n\n#### Prerequisite Gear Runs\n\nA list of gears, in the order they need to be run:\n\n1. __*dcm2niix*\n - Level: *Acquisition*\n\n#### Prerequisite Files\n\nA list of any files (OTHER than those specified by the input) that the gear will need.\nIf possible, list as many specific files as you can:\n\nNone\n\n#### Prerequisite Metadata\n\nA description of any metadata that is needed for the gear to run.\nIf possible, list as many specific metadata objects that are required:\n\nNone\n\n## Usage\n\nThis gear is used directly or as a Gear Rule. It has only one required file, the T1w.\nThis is actually not always necessary. If a T1w and a previously run freesurfer zip\nis passed, it will ignore the T1w and unzip the fs_zip and go from there.\n\n### Description\n\n*In the most basic form it just runs freesurfer. If a fs_zip is passed, not even that.\nOn top of those options, it is possible to select many other segmentations and atlases\nto obtain innumerable ROIs. It is possible to pass a zip with MNI ROIs that the gear\nwill convert to subject space ROIs. It is possible to pass a zip with fsaverage\nannotfiles, and the gear will generate cortical volumetric ROIs as well.*\n\n### Workflow\n\nThere is no workflow, it runs recon_all or unzips de zipped freesurfer output,\nand then the rest of the analyses are optional and it depends on what the\nresearcher wants.\n\nDescription of workflow\n\n1. Upload file to container\n1. Select file as input to gear\n1. Geat places output in Analysis\n\n### Logging\n\nThere are several logs, depending on what has been run. If recon_all is being run,\nthere will be a huge fresurfer log. Then, depending on the selected options,\nthere will be sections per every groups of ROIs that have been asked to calculate.\n\n## FAQ\n\n[FAQ.md](FAQ.md)\n\n## Contributing\n\n[For more information about how to get started contributing to that gear,\ncheckout [CONTRIBUTING.md](CONTRIBUTING.md).]\n",
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