Package: vb-toolbox
VBIndexVogt-Bailey index toolbox in PythonInstallationIt is possible to simply copy the foldervb_tooboxto your project folder and
proceed from there. If you take this approach, be sure you have the following
packages installedmultiprocess
nibabel
numpy
scipy
pillow
psutilThe preferred way to install is through pip. It is as easy aspipinstallvb_toolboxIf your pip is properly configured, you can now use the programvb_toolfrom
your command line, and import any of the submodules invb_toolboxinto your python
interpreter.Usage ofvb_toolCLIIf the VBIndex toolbox was installed from PyPI viapip, the command line programvb_toolshould
be available in your terminal. You can test if the program is correctly
installed by typingvb_tool-hAlternatively, if you have downloaded the source code, you can install the program by
typingpipinstallpy_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 thevb_toolSearchlight analysis (hybrid)Full brain feature gradient analysisFeature 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 analysisThe per vertex VB-index analysis can be carried out with the following commandvb_tool--hybrid--surfaceinput_data/surface.surf.gii--datainput_data/data.nii--maskinput_data/cortical_mask.shape.gii--outputsearch_lightThe number of vertices in the surface mesh must match the number of entries in
the mask.The cortical mask must contain a logical array, withTruevalues in the
region on which the analysis will be carried out, andFalsein 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 analysisTo perform full brain feature gradient analysis and extract the associated VB index, the flag-fbor--full-brainmust be set instead of--hybrid. Otherwise, the flags are the same as for the searchlight analysis.vb_tool--surfaceinput_data/surface.surf.gii--datainput_data/data.func.gii--maskinput_data/cortical_mask.shape.gii--full-brain--outputfull_brain_gradientBe 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) analysisSometimes, 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--surfaceinput_data/surface.surf.gii--datainput_data/data.func.gii-cinput_data/clusters.shape.gii--outputclustered_analysisThe 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 willnotbe analyzed. In this regard, it has a
similar use to the cortical mask.General NotesNote on the data filevb_toolcan handle two separate cases. If there is a single structure in the
file,vb_toolwill 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 parallelismvb_tooluses a high level of parallelism. The number of threads spawned byvb_toolitself can be controlled using the-j/--jobsflag. 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 thevb_tool, the level of parallelism it can achieve
on its own depends on the specific analysis being carried out.Searchlight analysis: High level of parallelism. Will spawn as many jobs as
there are CPUsFull brain analysis: Low level of parallelism. Will only spawn one jobRegion 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 InformationBuildThe 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 foundhere.Be sure that setuptools, twine, and wheel are up-to-datedpython3-mpipinstall--user--upgradesetuptoolswheeltwineRun the build commandpython3setup.pysdistbdist_wheelUpload the package to pippython3-mtwineuploaddist/*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 theall-contributorsspecification. Contributions of any kind welcome!