============================================================================== 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 $OUT --roygbiv \
    --ants $ANTS_SUBJECT/antsBrainSegmentation.nii.gz

Example 4: Generate only volume (no surface) labels and shapes::

mindboggle $FREESURFER_SUBJECT --out $OUT \
    --ants $ANTS_SUBJECT/antsBrainSegmentation.nii.gz \
    --no_surfaces

_Visualize output

To visualize Mindboggle output with roygbiv, start the Docker image (#1 above), then run roygbiv on an output directory::

roygbiv $OUT/$ID

and open a browser to localhost:5000.

Currently roygbiv only shows summarized data, but one of our goals is to work on by-vertex visualizations (for the latter, try Paraview <https://www.paraview.org/2>_).

_Appendix: processing

The following steps are performed by Mindboggle (with links to code on GitHub):

1. Create hybrid gray/white segmentation from FreeSurfer and ANTs output (combine_2labels_in_2volumes <https://github.com/nipy/mindboggle/blob/master/mindboggle/guts/segment.py#L1660>_).

2. Fill hybrid segmentation with FreeSurfer- or ANTs-registered labels.

3. Compute volume shape measures for each labeled region:

   • volume (volume_per_brain_region <https://github.com/nipy/mindboggle/blob/master/mindboggle/shapes/volume_shapes.py#L14>_)

4. Compute surface shape measures for every cortical mesh vertex:

   • surface area <https://github.com/nipy/mindboggle/blob/master/vtk_cpp_tools/PointAreaComputer.cpp>_
   • travel depth <https://github.com/nipy/mindboggle/blob/master/vtk_cpp_tools/TravelDepth.cpp>_
   • geodesic depth <https://github.com/nipy/mindboggle/blob/master/vtk_cpp_tools/geodesic_depth/GeodesicDepthMain.cpp>_
   • mean curvature <https://github.com/nipy/mindboggle/blob/master/vtk_cpp_tools/curvature/CurvatureMain.cpp>_
   • convexity (from FreeSurfer)
   • thickness (from FreeSurfer)

5. Extract cortical surface features:

   • folds <https://github.com/nipy/mindboggle/blob/master/mindboggle/features/folds.py>_
   • sulci <https://github.com/nipy/mindboggle/blob/master/mindboggle/features/sulci.py>_
   • fundi <https://github.com/nipy/mindboggle/blob/master/mindboggle/features/fundi.py>_

6. For each cortical surface label/sulcus, compute:

   • area <https://github.com/nipy/mindboggle/blob/master/vtk_cpp_tools/area/PointAreaMain.cpp>_
   • mean coordinates: means_per_label <https://github.com/nipy/mindboggle/blob/master/mindboggle/guts/compute.py#L512>_
   • mean coordinates in MNI152 space
   • Laplace-Beltrami spectrum <https://github.com/nipy/mindboggle/blob/master/mindboggle/shapes/laplace_beltrami.py>_
   • Zernike moments <https://github.com/nipy/mindboggle/blob/master/mindboggle/shapes/zernike/zernike.py>_

7. Compute statistics (stats_per_label in compute.py <https://github.com/nipy/mindboggle/blob/master/mindboggle/guts/compute.py#L716>_) for each shape measure in #4 for each label/feature:

   • median
   • median absolute deviation
   • mean
   • standard deviation
   • skew
   • kurtosis
   • lower quartile
   • upper quartile

_Appendix: output

Example output data can be found on Mindboggle's examples <https://osf.io/8cf5z>_ site on osf.io. By default, output files are saved in $HOME/mindboggled/SUBJECT, where $HOME is the home directory and SUBJECT is a name representing the person's brain that has been scanned. Volume files are in NIfTI <http://nifti.nimh.nih.gov>_ format, surface meshes in VTK <http://www.vtk.org/>_ format, and tables are comma-delimited. Each file contains integers that correspond to anatomical :doc:labels <labels> or features (0-24 for sulci). All output data are in the original subject's space. The following include outputs from most, but not all, optional arguments.

+----------------+----------------------------------------------------+--------------+ | Folder | Contents | Format | +----------------+----------------------------------------------------+--------------+ | labels/ | number-labeled surfaces and volumes | .vtk, .nii.gz| +----------------+----------------------------------------------------+--------------+ | features/ | surfaces with features: sulci, fundi | .vtk | +----------------+----------------------------------------------------+--------------+ | shapes/ | surfaces with shape measures (per vertex) | .vtk | +----------------+----------------------------------------------------+--------------+ | tables/ |tables of shape measures (per label/feature/vertex) | .csv | +----------------+----------------------------------------------------+--------------+

mindboggled / $SUBJECT /

**labels** /

    **freesurfer_wmparc_labels_in_hybrid_graywhite.nii.gz**:  *hybrid segmentation filled with FS labels*

    **ants_labels_in_hybrid_graywhite.nii.gz**:  *hybrid segmentation filled with ANTs + FS cerebellar labels*

    [left,right]_cortical_surface / **freesurfer_cortex_labels.vtk**: `DKT <http://mindboggle.info/data.html>`_ *cortical surface labels*

**features** / [left,right]_cortical_surface /

        **folds.vtk**:  *(unidentified) depth-based folds*

        **sulci.vtk**:  *sulci defined by* `DKT <http://mindboggle.info/data.html>`_ *label pairs in depth-based folds*

        **fundus_per_sulcus.vtk**:  *fundus curve per sulcus*  **-- UNDER EVALUATION --**

        **cortex_in_MNI152_space.vtk**:  *cortical surfaces aligned to an MNI152 template*

**shapes** / [left,right]_cortical_surface /

        **area.vtk**:  *per-vertex surface area*

        **mean_curvature.vtk**:  *per-vertex mean curvature*

        **geodesic_depth.vtk**:  *per-vertex geodesic depth*

        **travel_depth.vtk**:  *per-vertex travel depth*

        **freesurfer_curvature.vtk**:  *FS curvature files converted to VTK*

        **freesurfer_sulc.vtk**:  *FS sulc (convexity) files converted to VTK*

        **freesurfer_thickness.vtk**:  *FS thickness files converted to VTK*

**tables** /

    **volume_per_freesurfer_label.csv**:  *volume per FS label*

    **volumes_per_ants_label.csv**:  *volume per ANTs label*

    [left,right]_cortical_surface /

        **label_shapes.csv**:  *per-label surface shape statistics*

        **sulcus_shapes.csv**:  *per-sulcus surface shape statistics*

        **fundus_shapes.csv**:  *per-fundus surface shape statistics*  **-- UNDER EVALUATION --**

        **vertices.csv**:  *per-vertex surface shape statistics*