BrainPrint

Our BrainPrint tools provide shape descriptors of neuroanatomical structures and require a FreeSurfer or FastSurfer segmentation as a pre-processing step. BrainPrint is based on “ShapeDNA” a spectral shape descriptor.

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What BrainPrint can do for you

PyPI version

BrainPrint

This is the brainprint python package, a derivative of the original BrainPrint-legacy scripts, with the primary goal to provide a Python-only version, to integrate the LaPy package, and to remove dependencies on third-party software (shapeDNA-* binaries, gmsh, meshfix). As a result, some functionality of the original BrainPrint-legacy scripts is no longer maintained (currently no support of tetrahedral meshes and no support of cortical parcellations or label files).

Installation

Use the following code to install the latest release of LaPy into your local Python package directory:

python3 -m pip install brainprint

This will also install the necessary dependencies, e.g. the LaPy package. You may need to add your local Python package directory to your $PATH in order to run the scripts.

Usage

Command Line Interface (CLI)

Once installed, the package provides a brainprint executable which can be run from the command line.

The brainprint CLI enables per-subject computation of the individual brainprint descriptors. Its usage and options are summarized below; detailed info is available by calling the script without any arguments from the command line.

brainprint --sdir <directory> --sid <SubjectID>  [--num <num>] [--evec] [--skipcortex] [--norm <surface|volume|geometry|none> ] [--reweight] [--asymmetry] [--outdir <directory>] [--help] [--more-help]

Options:
  --help           Show this help message and exit
  --more-help      Show extensive help message and exit

Required options:
  --sid <SubjectID>
                   Subject ID (FreeSurfer-processed directory inside the
                   subjects directory)
  --sdir <directory>
                   FreeSurfer subjects directory

Processing directives:
  --num <num>      Number of eigenvalues/vectors to compute (default: 50)
  --evec           Switch on eigenvector computation (default: off)
  --skipcortex     Skip cortical surfaces (default: off)
  --norm <surface|volume|geometry|none>
                   Switch on eigenvalue normalization; will be either surface,
                   volume, or determined by the geometry of the object. Use
                   "none" or leave out entirely to skip normalization.
  --reweight       Switch on eigenvalue reweighting (default: off)
  --asymmetry      Perform left-right asymmetry calculation (default: off)
  --cholmod        Switch on use of (faster) Cholesky decomposition instead
                   of (slower) LU decomposition (default: off). May require 
                   manual install of scikit-sparse package. 

Output parameters:
  --outdir=OUTDIR  Output directory (default: <sdir>/<sid>/brainprint)
  --keep-temp      Whether to keep the temporary files directory or not
                   by default False

Python Package

brainprint can also be run within a pure Python environment, i.e. installed and imported as a Python package. E.g.:

>>> from brainprint import Brainprint

>>> subjects_dir = "/path/to/freesurfer/subjects_dir/"
>>> subject_id = "42"

>>> bp = Brainprint(subjects_dir=subjects_dir, asymmetry=True, keep_eigenvectors=True)
>>> results = bp.run(subject_id=subject_id)
>>> results
{"eigenvalues": PosixPath("/path/to/freesurfer/subjects_dir/subject_id/brainprint/subject_id.brainprint.csv"), "eigenvectors": PosixPath("/path/to/freesurfer/subjects_dir/subject_id/brainprint/eigenvectors"), "distances": PosixPath("/path/to/freesurfer/subjects_dir/subject_id/brainprint/subject_id.brainprint.asymmetry.csv")}

Output

The script will create an output directory that contains a CSV table with values (in that order) for the area, volume, and first n eigenvalues per each FreeSurfer structure. An additional output file will be created if the asymmetry calculation is performed and/or for the eigenvectors (CLI --evecs flag or keep_eigenvectors on class initialization).

Changes

There are some changes in functionality in comparison to the original BrainPrint scripts:

  • currently no support for tetrahedral meshes
  • currently no support for analyses of cortical parcellation or label files
  • no more Python 2.x compatibility

API Documentation

The API Documentation can be found at https://deep-mi.org/BrainPrint .

References

If you use this software for a publication please cite:

[1] BrainPrint: a discriminative characterization of brain morphology. Wachinger C, Golland P, Kremen W, Fischl B, Reuter M. Neuroimage. 2015;109:232-48. http://dx.doi.org/10.1016/j.neuroimage.2015.01.032 http://www.ncbi.nlm.nih.gov/pubmed/25613439

[2] Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids. Reuter M, Wolter F-E, Peinecke N Computer-Aided Design. 2006;38:342-366. http://dx.doi.org/10.1016/j.cad.2005.10.011

Keywords
Programming language
  • Python 100%
License
  • MIT
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Packages
pypi.org

Participating organisations

German Center for Neurodegenerative Diseases
Harvard Medical School

Mentions

Contributors

MR
Martin Reuter
Project Manager
German Center for Neurodegenerative Diseases
KD
Kersten Diers
German Center for Neurodegenerative Diseases

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LaPy

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LaPy is an open-source Python package for differential geometry on triangle and tetrahedra meshes. It includes an FEM solver to estimate the Laplace, Poisson or Heat equations, also the computations of gradients, divergence, mean-curvature flow, conformal mappings, geodesics, ShapeDNA and more.

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