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Mindboggle’s open source brain morphometry platform takes in preprocessed T1-weightedMRI data, and outputs volume, surface, and tabular data containing label, feature, and shapeinformation 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 reproducibilityof results. The software runs on Linux and is written in Python 3 and Python-wrapped C++ codecalled within a Nipype pipeline framework.We have tested the software most extensively with Python 3.5.1 on Ubuntu Linux 14.04.

Module Index and Index

Contents¶

Links¶

• GitHub and Circleci tests

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

Help¶

General questions about Mindboggle, or having some difficulties getting started?Please search for relevant mindboggle posts inNeuroStarsor post your own message with the tag “mindboggle”.

Found a bug, big or small? Pleasesubmit an issue on GitHub.

Installation¶

We recommend installing Mindboggle and its dependencies as a cross-platformDocker 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.)

1. Install and run Dockeron your (macOS, Linux, or Windows) host machine.

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

Note 1: This contains FreeSurfer, ANTs, and Mindboggle, so it is currentlyover 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.

3. Recommended: download sample data. To try out the mindboggle examplesbelow, download and unzip the directory of example input datamindboggle_input_example.zip (455 MB).For example MRI data to preprocess with FreeSurfer and ANTs software,download and unzipexample_mri_data.zip (29 MB).

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

Tutorial¶

To run the Mindboggle jupyter notebook tutorial, first install the MindboggleDocker 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):

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

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 processa 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:

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 usesMindboggle’s DKT-100 surface-based atlas to generate corresponding labelson 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:

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

3. FreeSurfer generates labeledcortical surfaces, and labeled cortical and noncortical volumes.Run recon-all on a T1-weighted IMAGE file (and optionally a T2-weightedimage), and set the output ID name as well as the $FREESURFER_OUT outputdirectory:

4. ANTs provides brain volume extraction,segmentation, and registration-based labeling. antsCorticalThickness.shgenerates transforms and segmentation files used by Mindboggle, and is runon the same IMAGE file and ID as above, with $ANTS_OUT output directory.TEMPLATE points to the OASIS-30_Atropos_template folderalready installed in the Docker container (“' splits the command for readability):

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

Or it can be run on themindboggle_input_examplepreprocessed data by setting:

Example Mindboggle commands:

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

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

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

Example 3:Take advantage of ANTs output as well (“' splits for readability):

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

Visualize output¶

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

and open a browser to localhost:5000.

Currently roygbiv only shows summarized data, but one of our goals is to workon by-vertex visualizations (for the latter, try Paraview).

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).

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

3. Compute volume shape measures for each labeled region:

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

   • convexity (from FreeSurfer)
   • thickness (from FreeSurfer)

5. Extract cortical surface features:

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

   • mean coordinates: means_per_label
   • mean coordinates in MNI152 space

7. Compute statistics (stats_per_label in compute.py) 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 foundon Mindboggle’s examples site on osf.io.By default, output files are saved in $HOME/mindboggled/SUBJECT, where $HOMEis the home directory and SUBJECT is a name representing the person’sbrain that has been scanned.Volume files are in NIfTI format,surface meshes in VTK format,and tables are comma-delimited.Each file contains integers that correspond to anatomical labelsor 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.

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: DKTcortical surface labels

features / [left,right]_cortical_surface /

folds.vtk: (unidentified) depth-based folds

sulci.vtk: sulci defined byDKTlabel 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

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freesurfer_thickness.vtk: FS thickness files converted to VTK

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tables /

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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