This app implements surface reconstruction using Freesurfer. It reconstructs the surface for each subject individually and then creates a study specific template. In case there are multiple sessions the Freesurfer longitudinal pipeline is used (creating subject specific templates) unless instructed to combine data across sessions.
The current Freesurfer version is based on: freesurfer-Linux-centos6_x86_64-stable-pub-v6.0.0.tar.gz
The output of the pipeline consist of the SUBJECTS_DIR created during the analysis.
https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSupport
https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferMethodsCitation
This App has the following command line arguments:
$ docker run -ti --rm bids/freesurfer --help
usage: run.py [-h]
[--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
[--session_label SESSION_LABEL [SESSION_LABEL ...]]
[--n_cpus N_CPUS]
[--stages {autorecon1,autorecon2,autorecon2-cp,autorecon2-wm,autorecon-pial,autorecon3,autorecon-all,all}
[{autorecon1,autorecon2,autorecon2-cp,autorecon2-wm,autorecon-pial,autorecon3,autorecon-all,all} ...]]
[--steps {cross-sectional,template,longitudinal}
[{cross-sectional,template,longitudinal} ...]]
[--template_name TEMPLATE_NAME] --license_file LICENSE_FILE
[--acquisition_label ACQUISITION_LABEL]
[--refine_pial_acquisition_label REFINE_PIAL_ACQUISITION_LABEL]
[--multiple_sessions {longitudinal,multiday}]
[--refine_pial {T2,FLAIR,None,T1only}]
[--hires_mode {auto,enable,disable}]
[--parcellations {aparc,aparc.a2009s} [{aparc,aparc.a2009s} ...]]
[--measurements {area,volume,thickness,thicknessstd,meancurv,gauscurv,foldind,curvind}
[{area,volume,thickness,thicknessstd,meancurv,gauscurv,foldind,curvind} ...]]
[-v] [--bids_validator_config BIDS_VALIDATOR_CONFIG]
[--skip_bids_validator] [--3T {true,false}]
bids_dir output_dir {participant,group1,group2}
FreeSurfer recon-all + custom template generation.
positional arguments:
bids_dir The directory with the input dataset formatted
according to the BIDS standard.
output_dir The directory where the output files should be stored.
If you are running group level analysis this folder
should be prepopulated with the results of
theparticipant level analysis.
{participant,group1,group2}
Level of the analysis that will be performed. Multiple
participant level analyses can be run independently
(in parallel) using the same output_dir. "group1"
creates study specific group template. "group2"
exports group stats tables for cortical parcellation,
subcortical segmentation a table with euler numbers.
optional arguments:
-h, --help show this help message and exit
--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
The label of the participant that should be analyzed.
The label corresponds to sub-<participant_label> from
the BIDS spec (so it does not include "sub-"). If this
parameter is not provided all subjects should be
analyzed. Multiple participants can be specified with
a space separated list.
--session_label SESSION_LABEL [SESSION_LABEL ...]
The label of the session that should be analyzed. The
label corresponds to ses-<session_label> from the BIDS
spec (so it does not include "ses-"). If this
parameter is not provided all sessions should be
analyzed. Multiple sessions can be specified with a
space separated list.
--n_cpus N_CPUS Number of CPUs/cores available to use.
--stages {autorecon1,autorecon2,autorecon2-cp,autorecon2-wm,autorecon-pial,autorecon3,autorecon-all,all}
[{autorecon1,autorecon2,autorecon2-cp,autorecon2-wm,autorecon-pial,autorecon3,autorecon-all,all} ...]
Autorecon stages to run.
--steps {cross-sectional,template,longitudinal} [{cross-sectional,template,longitudinal} ...]
Longitudinal pipeline steps to run.
--template_name TEMPLATE_NAME
Name for the custom group level template generated for
this dataset
--license_file LICENSE_FILE
Path to FreeSurfer license key file. To obtain it you
need to register (for free) at
https://surfer.nmr.mgh.harvard.edu/registration.html
--acquisition_label ACQUISITION_LABEL
If the dataset contains multiple T1 weighted images
from different acquisitions which one should be used?
Corresponds to "acq-<acquisition_label>"
--refine_pial_acquisition_label REFINE_PIAL_ACQUISITION_LABEL
If the dataset contains multiple T2 or FLAIR weighted
images from different acquisitions which one should be
used? Corresponds to "acq-<acquisition_label>"
--multiple_sessions {longitudinal,multiday}
For datasets with multiday sessions where you do not
want to use the longitudinal pipeline, i.e., sessions
were back-to-back, set this to multiday, otherwise
sessions with T1w data will be considered independent
sessions for longitudinal analysis.
--refine_pial {T2,FLAIR,None,T1only}
If the dataset contains 3D T2 or T2 FLAIR weighted
images (~1x1x1), these can be used to refine the pial
surface. If you want to ignore these, specify None or
T1only to base surfaces on the T1 alone.
--hires_mode {auto,enable,disable}
Submilimiter (high resolution) processing. 'auto' -
use only if <1.0mm data detected, 'enable' - force on,
'disable' - force off
--parcellations {aparc,aparc.a2009s} [{aparc,aparc.a2009s} ...]
Group2 option: cortical parcellation(s) to extract
stats from.
--measurements {area,volume,thickness,thicknessstd,meancurv,gauscurv,foldind,curvind}
[{area,volume,thickness,thicknessstd,meancurv,gauscurv,foldind,curvind} ...]
Group2 option: cortical measurements to extract stats
for.
-v, --version show program's version number and exit
--bids_validator_config BIDS_VALIDATOR_CONFIG
JSON file specifying configuration of bids-validator:
See https://github.com/INCF/bids-validator for more
info
--skip_bids_validator
skips bids validation
--3T {true,false} enables the two 3T specific options that recon-all
supports: nu intensity correction params, and the
special schwartz atlas
To run it in participant level mode (for one participant):
docker run -ti --rm \
-v /Users/filo/data/ds005:/bids_dataset:ro \
-v /Users/filo/outputs:/outputs \
-v /Users/filo/freesurfer_license.txt:/license.txt \
bids/freesurfer \
/bids_dataset /outputs participant --participant_label 01 \
--license_file "/license.txt"
After doing this for all subjects (potentially in parallel) the group level analyses can be run.
To create a study specific template run:
docker run -ti --rm \
-v /Users/filo/data/ds005:/bids_dataset:ro \
-v /Users/filo/outputs:/outputs \
-v /Users/filo/freesurfer_license.txt:/license.txt \
bids/freesurfer \
/bids_dataset /outputs group1 \
--license_file "/license.txt"
To export tables with aggregated measurements within regions of cortical parcellation and subcortical segementation, and a table with euler numbers (a quality metric, see Rosen et. al, 2017) run:
docker run -ti --rm \
-v /Users/filo/data/ds005:/bids_dataset:ro \
-v /Users/filo/outputs:/outputs \
-v /Users/filo/freesurfer_license.txt:/license.txt \
bids/freesurfer \
/bids_dataset /outputs group2 \
--license_file "/license.txt"
Also see the --parcellations and --measurements arguments.
This step writes ouput into <output_dir>/00_group2_stats_tables/. E.g.:
lh.aparc.thickness.tsvcontains cortical thickness values for the left hemisphere extracted via the aparac parcellation.aseg.tsvcontains subcortical information from the aseg segmentation.euler.tsvcontains the euler numbers