vb-toolbox 2.1.2

Library and command-line tool to calculate the Vogt-Bailey index of a dataset

VBIndex

Vogt-Bailey index toolbox in Python

Installation

It is possible to simply copy the folder vb_toobox to your project folder and proceed from there. If you take this approach, be sure you have the following packages installed

multiprocess
nibabel
numpy
scipy
pillow
psutil

The preferred way to install is through pip. It is as easy as

pip install vb_toolbox

If your pip is properly configured, you can now use the program vb_tool from your command line, and import any of the submodules in vb_toolbox into your python interpreter.

Usage of vb_tool CLI

If the VBIndex toolbox was installed from PyPI via pip, the command line program vb_tool should be available in your terminal. You can test if the program is correctly installed by typing

vb_tool -h

Alternatively, if you have downloaded the source code, you can install the program by typing

pip install py_vb_toolbox/

In your terminal, if you see the following output, the program has been properly installed.

usage: vb_tool [-h] [-j N] [-n norm] [-fb] [-hy] [-m file] [-c file]
               [-t tolerance] [-mi max iterations] [-debug] -s file -d file -o
               file

Calculate the Vogt-Bailey index of a dataset. For more information, refer to
https://github.com/VBIndex/py_vb_toolbox.

optional arguments:
  -h, --help            Show this help message and exit.
  -j N, --jobs N        Maximum number of jobs to be used. If absent, one job
                        per CPU will be spawned.
  -n norm, --norm norm  Laplacian normalization to be employed. Possibilities are
                        "geig", "unnorm", "rw" and "sym". Defaults to geig for
                        the full brain and ROI analyses, and to unnorm
                        otherwise.
  -fb, --full-brain     Calculate full brain feature gradient analysis.
  -hy, --hybrid         Calculate searchlight VB index with hybrid approach.
  -m file, --mask file  File containing the labels to identify the cortex,
                        rather than the medial brain structures. This flag
                        must be set for the searchlight and full brain
                        analyses.
  -c file, --clusters file
                        File specifying the surface clusters. The cluster with
                        index 0 is expected to denote the medial brain
                        structures and will be ignored.
  -t tolerance, --tol tolerance
                        Residual tolerance (stopping criterion) for LOBPCG.
                        Default value = sqrt(10e-18)*n, where n is the number
                        of nodes per graph. Note that the LOBPCG algorithm is only
                        utilised for full-brain analysis.
  -mi max iterations, --maxiter max iterations
                        Maximum number of iterations for LOBPCG. Defaults to
                        50.
  -debug, --debug       Save additional files for debugging.

required named arguments:
  -s file, --surface file
                        File containing the surface mesh.
  -d file, --data file  File containing the data over the surface (or volume
                        if hybrid).
  -o file, --output file
                        Base name for the output files.

authors:

The VB Index Team (See Contributors Section in the main README)

references:

Bajada, C. J., Campos, L. Q. C., Caspers, S., Muscat, R., Parker, G. J., Ralph, M. A. L., ... & Trujillo-Barreto, N. J. (2020). A tutorial and tool for exploring feature similarity gradients with MRI data. NeuroImage, 221, 117140.
Ciantar, K. G., Farrugia, C., Scerri, K., Xu, T., & Bajada, C. J. (2020). Geometric effects of volume-to-surface mapping of fMRI data. bioRxiv.

copyright:

This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.

This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with
this program. If not, see <https://www.gnu.org/licenses>.

There are three main uses for the vb_tool

1. Searchlight analysis (hybrid)
2. Full brain feature gradient analysis
3. Feature gradient analysis in a specified set of regions of interest (ROI analysis)

We currently recommend using the hybrid searchlight analysis as it is the most tested approach.

Searchlight analysis

The per vertex VB-index analysis can be carried out with the following command

vb_tool --hybrid --surface input_data/surface.surf.gii  --data input_data/data.nii --mask input_data/cortical_mask.shape.gii --output search_light

The number of vertices in the surface mesh must match the number of entries in the mask.

The cortical mask must contain a logical array, with True values in the region on which the analysis will be carried out, and False in the regions to be left out. This is most commonly used to mask out midbrain structures which would otherwise influence the analysis of the cortical regions.

Whole brain analysis

To perform full brain feature gradient analysis and extract the associated VB index, the flag -fb or --full-brain must be set instead of --hybrid. Otherwise, the flags are the same as for the searchlight analysis.

vb_tool --surface input_data/surface.surf.gii  --data input_data/data.func.gii --mask input_data/cortical_mask.shape.gii --full-brain --output full_brain_gradient

Be warned, however, that this analysis can take long and require a large amount of RAM. For data sets with 32k vertices, upwards of 30GB of RAM were used.

Regions of Interest (ROI) analysis

Sometimes, one is interested only in a small set of ROIs. In this case, the feature gradient map and the associated VB index value for each ROI will be extracted. The way of calling the program is as follows:

vb_tool --surface input_data/surface.surf.gii  --data input_data/data.func.gii  -c input_data/clusters.shape.gii --output clustered_analysis

The cluster file works similarly to the cortical mask employed for the searchlight and full brain methods. However, its structure is slightly different. Instead of an array of logical values, the file must contain an array of integers, where each integer corresponds to a different cluster. The 0th cluster is special, and denotes an area which will not be analyzed. In this regard, it has a similar use to the cortical mask.

General Notes

Note on the data file

vb_tool can handle two separate cases. If there is a single structure in the file, vb_tool will read it as a matrix in which each row relates to a specific vertex. If there are two or more structures, it will read them as a series of column vectors in which each entry relates to a vertex. It will then coalesce them into a single matrix, and run the analysis of all quantities concurrently.

Notes on parallelism

vb_tool uses a high level of parallelism. The number of threads spawned by vb_tool itself can be controlled using the -j/--jobs flag. By default, the software will try to use all the CPUs in your computer at the same time to perform the analysis. Depending on the BLAS installation on your computer, this might not be the fastest approach, but will rarely be the slowest. If you are unsure, keep the default number of jobs.

Due to the job structure of the vb_tool, the level of parallelism it can achieve on its own depends on the specific analysis being carried out.

1. Searchlight analysis: High level of parallelism. Will spawn as many jobs as there are CPUs
2. Full brain analysis: Low level of parallelism. Will only spawn one job
3. Region of Interest (ROI) analysis: Medium level of parallelism. Will spawn as many jobs as there are ROIs, or number of CPUS, whichever is the lowest.

Especially for the whole brain analysis, having a well-optimized BLAS installation will greatly accelerate the process, and allow for further parallelism. Both MKL and OpenBLAS have been shown to support fast analysis. If you are using the Anaconda distribution, you will have a good BLAS pre-configured.

Developer Information

Build

The following information is only useful for individuals who are actively contributing to the program.

We use setuptool and wheel to build the distribution code. The process is described next. More information can be found here.

1. Be sure that setuptools, twine, and wheel are up-to-dated

python3 -m pip install --user --upgrade setuptools wheel twine

2. Run the build command

python3 setup.py sdist bdist_wheel

3. Upload the package to pip

python3 -m twine upload dist/*

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Keith George Ciantar 💻	NicoleEic 💻	claudebajada 🐛 🤔 📆 💻	Lucas Campos 💻 🐛 🤔 🚧	paola-g 💻	ChristineFarrugia 💻	jschewts 💻
Kenneth Scerri 📆

This project follows the all-contributors specification. Contributions of any kind welcome!