BARACUS: Brain-Age Regression Analysis and Computation Utility Software
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This package predicts brain age, based on data from Freesurfer 5.3. It combines data from cortical thickness, cortical surface area, and subcortical information (see Liem et al., 2016).
Liem et al. (2016). Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage, 148:179–188, doi: 10.1016/j.neuroimage.2016.11.005. [preprint]
Liem2016__OCI_norm: Model trained on subjects that have no objective cognitive impairment (OCI) (OCI norm in Liem et al., 2016). Sample: N = 1166, 566f/600m, age: M = 59.1, SD = 15.2, 20-80y
It can be run in BIDS mode and in in FILE mode.
In BIDS mode the input is a BIDS formatted Freesurfer folder.
In FILE mode the input is provided as surface and aseg files. Surface files need to be sampled to fsaverage4 space, aseg files extracted via asegstats2table.
Participants
docker run -ti --rm \
-v /project/freesurfer/:/data/in \
-v /project/out:/data/out \
fliem/baracus /data/in /data/out participant
Group
docker run -ti --rm \
-v /project/freesurfer/:/data/in \
-v /project/out:/data/out \
fliem/baracus /data/in /data/out group
usage: run_brain_age_bids.py [-h]
[--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
[--model {Liem2016__OCI_norm}]
freesurfer_dir out_dir {participant,group}
BAuto: automatic prediction of Brain Age. BIDS mode. You specify a BIDS-
formatted freesurfer folder as input. All data is extracted automatiacally
from that folder.
positional arguments:
freesurfer_dir Folder with freesurfer subjects formatted according to
BIDS standard.
out_dir Results are put here.
{participant,group} Level of the analysis that will be performed.
"participant": predicts single subject brain age,
"group": collects single subject predictions.
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.
--model {Liem2016__OCI_norm}
docker run -ti --rm \
-v /project/data/:/data/in \
-v /project/out:/data/out \
--entrypoint=run_brain_age_files.py \
fliem/baracus /data/out \
--lh_thickness_file /data/in/s01/lh.thickness.mgh \
--rh_thickness_file /data/in/s01/rh.thickness.mgh \
--lh_area_file /data/in/s01/lh.area.mgh \
--rh_area_file /data/in/s01/rh.area.mgh \
--aseg_file /data/in/s01/aseg.txt
usage: run_brain_age_files.py [-h] --mode {bids,files}
[--participant_label PARTICIPANT_LABEL]
[--model {Liem2016__OCI_norm}]
--lh_thickness_file LH_THICKNESS_FILE
--rh_thickness_file RH_THICKNESS_FILE
--lh_area_file LH_AREA_FILE --rh_area_file
RH_AREA_FILE --aseg_file ASEG_FILE
out_dir
BAuto: automatic prediction of Brain Age. Files mode. You specify lh/rh
thickness/area + aseg file (with --lh_thickness_file...). Surface files need
to be sampled to fsaverage4 space, aseg files extracted via asegstats2table.
Only one subject can be specified at a time.
positional arguments:
out_dir Results are put here.
optional arguments:
-h, --help show this help message and exit
--participant_label PARTICIPANT_LABEL
will be written into output files and can be omitted
--model {Liem2016__OCI_norm}
--lh_thickness_file LH_THICKNESS_FILE
--rh_thickness_file RH_THICKNESS_FILE
--lh_area_file LH_AREA_FILE
--rh_area_file RH_AREA_FILE
--aseg_file ASEG_FILE
Content type
Image
Digest
Size
2.6 GB
Last updated
over 9 years ago
docker pull fliem/baracus