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https://github.com/bids-apps/baracus

Predicts brain age, based on data from Freesurfer 5.3
https://github.com/bids-apps/baracus

bids bidsapp

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Predicts brain age, based on data from Freesurfer 5.3

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# BARACUS: Brain-Age Regression Analysis and Computation Utility Software



[![CircleCI](https://circleci.com/gh/BIDS-Apps/baracus.svg?style=shield&circle-token=:circle-token)](https://circleci.com/gh/BIDS-Apps/baracus)

[![DOI](https://zenodo.org/badge/93560323.svg)](https://zenodo.org/badge/latestdoi/93560323)



This [BIDS App](http://bids-apps.neuroimaging.io/) 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., 2017).



## Requirements

Your data has to be organized according to the

[BIDS standard](http://bids.neuroimaging.io) and each subject needs at

least one T1w image.

In a first step, BARACUS runs [FreeSurfer's](http://freesurfer.net)

`recon-all` command and saves the output in `{out_dir}/freesurfer/`

If the data has previously been analyzed with FreeSurfer version 5.3.0,

and BARACUS finds them in `--freesurfer_dir` this step is skipped.



**Important:** if you use previously processed FreeSurfer data



1. the data has to be preprocessed with

Freesurfer's 5.3.0 installation, not the 5.3.0-HCP installation;

2. FreeSurfer data needs to be BIDS-formatted, i.e. subject folders

should be named *sub-*, (e.g., sub-01, sub-02...)



**Also important:** if you are comparing groups regarding brain-age,

make sure that the groups are well matched (e.g. regarding ethnicity;

see [here](https://github.com/BIDS-Apps/baracus/issues/10)).



## Acknowledgements

If you use BARACUS in your work please cite:



1. Liem et al. (2017),

1. the [zenodo DOI](https://zenodo.org/badge/latestdoi/93560323)

of the BARACUS version you used, and

1. The [FreeSurfer tool](https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferMethodsCitation)



Liem et al. (2017). Predicting brain-age

from multimodal imaging data captures cognitive impairment.

Neuroimage, 148:179–188,

[doi: 10.1016/j.neuroimage.2016.11.005](http://www.sciencedirect.com/science/article/pii/S1053811916306103).

[\[preprint\]](http://www.biorxiv.org/content/early/2016/11/07/085506)



## Models

**Liem2016__OCI_norm**: Model trained on subjects that have no

objective cognitive impairment (OCI) (*OCI norm* in Liem et al., 2017).

Sample: N = 1166, 566f/600m, age: M = 59.1, SD = 15.2, 20-80y



**Liem2016__full_2samp_training**: Model trained on subjects that have no

objective cognitive impairment (OCI) (*full LIFE 2sample training* in Liem et al., 2017).

Sample: N = 2377, 1133f/1244m, age: M=58.4, SD=15.4, 18-83y;

containing data from the LIFE and NKI studies.



Models were trained as part of Liem et al. (2017) and the training code is available

[here](https://github.com/fliem/LeiCA_LIFE).



## Modes

It can be run in **BIDS mode** (recommended) 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.



## BIDS mode

### Example

These examples demonstrate how to run the `bids/baracus` docker container.

For a brief introduction how to run BIDS Apps see

[this site](http://bids-apps.neuroimaging.io/tutorial/).

In the examples `/project/bids_sourcedata` and

`/project/baracus` are directories on your hard drive, which are mapped

into the docker container directories `/data/in` and `/data/out`,

respectively, via the `-v` flag.



#### Participants



    docker run -ti --rm \

    -v /project/bids_sourcedata/:/data/in \

    -v /project/baracus:/data/out \

    bids/baracus /data/in /data/out participant \

    --license_key "XX"



#### Group



    docker run -ti --rm \

    -v /project/bids_sourcedata/:/data/in \

    -v /project/baracus:/data/out \

    bids/baracus /data/in /data/out group \

    --license_key "XX"



#### Participants with previously processed FreeSurfer data

If FreeSurfer data is already available, for example at

`/project/freesurfer/` running the follwing command will use the

previously processed data:



    docker run -ti --rm \

    -v /project/bids_sourcedata/:/data/in \

    -v /project/freesurfer/:/data/freesurfer \

    -v /project/baracus:/data/out \

    bids/baracus /data/in /data/out participant \

    --license_key "XX" --freesurfer_dir /data/freesurfer



### Usage



    usage: run_brain_age_bids.py [-h]

                                 [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]

                                 [--freesurfer_dir FREESURFER_DIR]

                                 [--models {Liem2016__OCI_norm,Liem2016__full_2samp_training} [{Liem2016__OCI_norm,Liem2016__full_2samp_training} ...]]

                                 --license_key LICENSE_KEY [--n_cpus N_CPUS] [-v]

                                 bids_dir out_dir {participant,group}



    BARACUS: Brain-Age Regression Analysis and Computation Utility Software. BIDS

    mode. You specify a BIDS-formatted freesurfer folder as input. All data is

    extracted automatiacally from that folder.



    positional arguments:

      bids_dir              The directory with the input dataset formatted

                            according to the BIDS standard.

      out_dir               Results are put into {out_dir}/baracus.

      {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- 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.

      --freesurfer_dir FREESURFER_DIR

                            Folder with FreeSurfer subjects formatted according to

                            BIDS standard. If subject's recon-all folder cannot be

                            found, recon-all will be run. If not specified

                            freesurfer data will be saved to {out_dir}/freesurfer

      --models {Liem2016__OCI_norm,Liem2016__full_2samp_training} [{Liem2016__OCI_norm,Liem2016__full_2samp_training} ...]

      --skip_missing        Flag to skip not segmented subjects

      --license_key LICENSE_KEY

                            FreeSurfer license key - letters and numbers after "*"

                            in the email you received after registration. To

                            register (for free) visit

                            https://surfer.nmr.mgh.harvard.edu/registration.html

      --n_cpus N_CPUS       Number of CPUs/cores available to use.

      -v, --version         show program's version number and exit



## FILE mode

### Example

    docker run -ti --rm \

    -v /project/data/:/data/in \

    -v /project/out:/data/out \

    --entrypoint=run_brain_age_files.py \

    bids/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

    usage: run_brain_age_files.py [-h] [--participant_label PARTICIPANT_LABEL]

                                  [--models {Liem2016__OCI_norm,Liem2016__full_2samp_training} [{Liem2016__OCI_norm,Liem2016__full_2samp_training} ...]]

                                  --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



    BARACUS: Brain-Age Regression Analysis and Computation Utility Software. 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

      --models {Liem2016__OCI_norm,Liem2016__full_2samp_training} [{Liem2016__OCI_norm,Liem2016__full_2samp_training} ...]

      --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