============================================================================== Software

.. role:: red

Mindboggle's open source brain morphometry platform takes in preprocessed T1-weighted MRI data, and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. Mindboggle can be run on the command line as "mindboggle" and also exists as a cross-platform Docker container for convenience and reproducibility of results. The software runs on Linux and is written in Python 3 and Python-wrapped C++ code called within a Nipype <http://nipy.org/nipype>_ pipeline framework. We have tested the software most extensively with Python 3.5.1 on Ubuntu Linux 14.04.

:Release: |version| :Date: |today| :ref:modindex and :ref:genindex

---

Contents

• Links_
• Reference_
• Help_
• Installation_
• Tutorial_
• Run one command_
• Run separate commands_
• Visualize output_
• Appendix: processing_
• Appendix: output_

---

_Links

• FAQ <http://mindboggle.readthedocs.io/en/latest/faq.html>_
• GitHub <http://github.com/nipy/mindboggle>_ and Circleci tests <https://circleci.com/gh/nipy/mindboggle>_
• Contributors <http://mindboggle.info/people.html>_
• License <http://mindboggle.readthedocs.io/en/latest/license.html>_

---

_Reference

A Klein, SS Ghosh, FS Bao, J Giard, Y Hame, E Stavsky, N Lee, B Rossa, M Reuter, EC Neto, A Keshavan. 2017. Mindboggling morphometry of human brains. PLoS Computational Biology 13(3): e1005350. doi:10.1371/journal.pcbi.1005350 <https://doi.org/10.1371/journal.pcbi.1005350>_

---

_Help

General questions about Mindboggle, or having some difficulties getting started?
Please search for relevant mindboggle posts in NeuroStars <https://neurostars.org/tags/mindboggle/>_ or post your own message with the tag "mindboggle".

Found a bug, big or small? Please submit an issue <https://github.com/nipy/mindboggle/issues>_ on GitHub.

---

_Installation

We recommend installing Mindboggle and its dependencies as a cross-platform Docker container for greater convenience and reproducibility of results. All the examples below assume you are using this Docker container, with the path /home/jovyan/work/ pointing to your host machine. (Alternatively, one can create a Singularity image <http://mindboggle.readthedocs.io/en/latest/faq/singularity.html>_.)

1. Install and run Docker <https://docs.docker.com/engine/installation/>_ on your (macOS, Linux, or Windows) host machine.

2. Download the Mindboggle Docker container (copy/paste the following in a terminal window)::

   docker pull nipy/mindboggle

Note 1: This contains FreeSurfer, ANTs, and Mindboggle, so it is currently over 6GB.*

Note 2: You may need to increase memory allocated by Docker to at least 5GB. For example: By default, Docker for Mac is set to use 2 GB runtime memory <https://docs.docker.com/docker-for-mac/>_.

3. Recommended: download sample data. To try out the mindboggle examples below, download and unzip the directory of example input data mindboggle_input_example.zip <https://osf.io/3xfb8/?action=download&version=1>_ (455 MB). For example MRI data to preprocess with FreeSurfer and ANTs software, download and unzip example_mri_data.zip <https://osf.io/k3m94/?action=download&version=1>_ (29 MB).

4. Recommended: set environment variables for clarity in the commands below (modify accordingly, except for DOCK -- careful, this step is tricky!)::

   HOST=/Users/binarybottle # path on local host seen from Docker container to access/save data DOCK=/home/jovyan/work # path to HOST from Docker container (DO NOT CHANGE) IMAGE=$DOCK/example_mri_data/T1.nii.gz # brain image in $HOST to process ID=arno # ID for brain image OUT=$DOCK/mindboggle123_output # output path ('--out $OUT' below is optional)

---

_Tutorial

To run the Mindboggle jupyter notebook tutorial, first install the Mindboggle Docker container (above) and run the notebook in a web browser as follows (replacing $HOST with the absolute path where you want to access/save data)::

docker run --rm -ti -v $HOST:/home/jovyan/work -p 8888:8888 nipy/mindboggle jupyter notebook /opt/mindboggle/docs/mindboggle_tutorial.ipynb --ip=0.0.0.0 --allow-root

In the output on the command line you'll see something like::

[I 20:47:38.209 NotebookApp] The Jupyter Notebook is running at:
[I 20:47:38.210 NotebookApp] http://(057a72e00d63 or 127.0.0.1):8888/?token=62853787e0d6e180856eb22a51609b25e

You would then copy and paste the corresponding address into your web browser (in this case, http://127.0.0.1:8888/?token=62853787e0d6e180856eb22a51609b25e), and click on "mindboggle_tutorial.ipynb".

---

_Run one command

The Mindboggle Docker container can be run as a single command to process a T1-weighted MR brain image through FreeSurfer, ANTs, and Mindboggle. Skip to the next section if you wish to run recon-all, antsCorticalThickness.sh, and mindboggle differently::

docker run --rm -ti -v $HOST:$DOCK nipy/mindboggle mindboggle123 $IMAGE --id $ID

Outputs are stored in $DOCK/mindboggle123_output/ by default, but you can set a different output path with --out $OUT.

---

_Run separate commands

If finer control is needed over the software in the Docker container, the following instructions outline how to run each command separately. Mindboggle currently takes output from FreeSurfer and optionally from ANTs. FreeSurfer version 6 or higher is recommended because by default it uses Mindboggle’s DKT-100 surface-based atlas to generate corresponding labels on the cortical surfaces and in the cortical and non-cortical volumes (v5.3 generates these surface labels by default; older versions require "-gcs DKTatlas40.gcs" to generate these surface labels).

1. Enter the Docker container's bash shell to run recon-all, antsCorticalThickness.sh, and mindboggle commands::

   docker run --rm -ti -v $HOST:$DOCK -p 5000:5000 nipy/mindboggle

2. Recommended: reset environment variables as above within the Docker container::

   DOCK=/home/jovyan/work # path to HOST from Docker container IMAGE=$DOCK/example_mri_data/T1.nii.gz # input image on HOST ID=arno # ID for brain image

3. FreeSurfer <http://surfer.nmr.mgh.harvard.edu>_ generates labeled cortical surfaces, and labeled cortical and noncortical volumes. Run recon-all on a T1-weighted IMAGE file (and optionally a T2-weighted image), and set the output ID name as well as the $FREESURFER_OUT output directory::

   FREESURFER_OUT=$DOCK/freesurfer_subjects

   recon-all -all -i $IMAGE -s $ID -sd $FREESURFER_OUT

4. ANTs <http://stnava.github.io/ANTs/>_ provides brain volume extraction, segmentation, and registration-based labeling. antsCorticalThickness.sh generates transforms and segmentation files used by Mindboggle, and is run on the same IMAGE file and ID as above, with $ANTS_OUT output directory. TEMPLATE points to the OASIS-30_Atropos_template <https://osf.io/rh9km/>_ folder already installed in the Docker container (backslashes split the command for readability)::

   ANTS_OUT=$DOCK/ants_subjects TEMPLATE=/opt/data/OASIS-30_Atropos_template

   antsCorticalThickness.sh -d 3 -a $IMAGE -o $ANTS_OUT/$ID/ants
   -e $TEMPLATE/T_template0.nii.gz
   -t $TEMPLATE/T_template0_BrainCerebellum.nii.gz
   -m $TEMPLATE/T_template0_BrainCerebellumProbabilityMask.nii.gz
   -f $TEMPLATE/T_template0_BrainCerebellumExtractionMask.nii.gz
   -p $TEMPLATE/Priors2/priors%d.nii.gz
   -u 0

5. Mindboggle can be run on data preprocessed by recon-all and antsCorticalThickness.sh as above by setting::

   FREESURFER_SUBJECT=$FREESURFER_OUT/$ID ANTS_SUBJECT=$ANTS_OUT/$ID OUT=$DOCK/mindboggled # output folder

Or it can be run on the mindboggle_input_example <https://osf.io/3xfb8/?action=download&version=1>_ preprocessed data by setting::

EXAMPLE=$DOCK/mindboggle_input_example
FREESURFER_SUBJECT=$EXAMPLE/freesurfer/subjects/arno
ANTS_SUBJECT=$EXAMPLE/ants/subjects/arno
OUT=$DOCK/mindboggled  # output folder

Example Mindboggle commands:

To learn about Mindboggle's command options, type this in a terminal window::

mindboggle -h

Example 1: Run Mindboggle on data processed by FreeSurfer but not ANTs::

mindboggle $FREESURFER_SUBJECT --out $OUT

Example 2: Same as Example 1 with output to visualize surface data with roygbiv::

mindboggle $FREESURFER_SUBJECT --out $OUT --roygbiv

Example 3: Take advantage of ANTs output as well ("\" splits for readability)::

mindboggle $FREESURFER_SUBJECT --out