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dfc

An implementation of several well-known dynamic Functional Connectivity assessment methods.

https://github.com/neurodatascience/dfc

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 6 DOI reference(s) in README
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    Academic publication links
    Links to: zenodo.org
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    Academic email domains
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    Scientific vocabulary similarity
    Low similarity (8.9%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

An implementation of several well-known dynamic Functional Connectivity assessment methods.

Basic Info
  • Host: GitHub
  • Owner: neurodatascience
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 45.1 MB
Statistics
  • Stars: 27
  • Watchers: 2
  • Forks: 9
  • Open Issues: 8
  • Releases: 6
Created over 4 years ago · Last pushed 7 months ago
Metadata Files
Readme License Citation

README.rst 

.. image:: docs/PydFC_logo_dark_round.png
    :alt: pydfc Logo
    :align: left
    :width: 250px
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.10161176.svg
    :target: https://zenodo.org/doi/10.5281/zenodo.10161176
.. image:: https://img.shields.io/pypi/v/pydfc.svg
    :target: https://pypi.org/project/pydfc/
    :alt: Pypi Package

pydfc
=====

An implementation of several well-known dynamic Functional Connectivity (dFC) assessment methods.

Simply install ``pydfc`` using the following steps:
  * ``conda create --name pydfc_env python=3.11``
  * ``conda activate pydfc_env``
  * ``pip install pydfc``

The ``dFC_methods_demo.ipynb`` illustrates how to load data and apply each of the dFC methods implemented in the ``pydfc`` toolbox individually.
The ``multi_analysis_demo.ipynb`` illustrates how to use the ``pydfc`` toolbox to apply multiple dFC methods at the same time on a dataset and compare their results.

For more details about the implemented methods and the comparison analysis see `our paper `_.

  * Mohammad Torabi, Georgios D Mitsis, Jean-Baptiste Poline, On the variability of dynamic functional connectivity assessment methods, GigaScience, Volume 13, 2024, giae009, https://doi.org/10.1093/gigascience/giae009.

Owner

  • Name: NeuroDataScience
  • Login: neurodatascience
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0

title: "pydfc"

version: 1.0.2

abstract:
  "An implementation of several well-known dynamic Functional Connectivity assessment methods."

message: "If you use this software, please cite it as below."

repository-code: "https://github.com/neurodatascience/dFC.git"


contact:
  - affiliation: "McGill University, Québec, Canada"
    email: mohammad.torabi@mail.mcgill.ca
    family-names: Torabi
    given-names: Mohammad

authors:
  - family-names: "Torabi"
    given-names: "Mohammad"
    orcid: "https://orcid.org/0000-0002-4429-8481"
    affiliation: Biological and Biomedical Engineering program, McGill University, Québec, Canada"

license: MIT

keywords:
  - dynamic functional connectivity
  - analytical flexibility
  - neuroimaging
  - reproducibility

GitHub Events

Total
  • Issues event: 3
  • Watch event: 13
  • Delete event: 1
  • Issue comment event: 21
  • Push event: 8
  • Pull request review event: 7
  • Pull request review comment event: 7
  • Pull request event: 12
  • Fork event: 1
  • Create event: 3
Last Year
  • Issues event: 3
  • Watch event: 13
  • Delete event: 1
  • Issue comment event: 21
  • Push event: 8
  • Pull request review event: 7
  • Pull request review comment event: 7
  • Pull request event: 12
  • Fork event: 1
  • Create event: 3

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 6
  • Total pull requests: 19
  • Average time to close issues: N/A
  • Average time to close pull requests: 10 days
  • Total issue authors: 5
  • Total pull request authors: 6
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.47
  • Merged pull requests: 17
  • Bot issues: 0
  • Bot pull requests: 3
Past Year
  • Issues: 3
  • Pull requests: 7
  • Average time to close issues: N/A
  • Average time to close pull requests: 4 days
  • Issue authors: 3
  • Pull request authors: 4
  • Average comments per issue: 0.0
  • Average comments per pull request: 1.14
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 1
View more stats
Top Authors
Issue Authors
  • Remi-Gau (2)
  • mibur1 (1)
  • zhangsy1915 (1)
  • SebastianVolkmer (1)
  • m-miedema (1)
Pull Request Authors
  • mtorabi59 (9)
  • dependabot[bot] (4)
  • Remi-Gau (3)
  • mibur1 (1)
  • m-miedema (1)
  • effigies (1)
Top Labels
Issue Labels
Pull Request Labels
dependencies (4) github_actions (1)

Dependencies

.github/workflows/validate_cff.yml actions
  • actions/checkout v4 composite
  • citation-file-format/cffconvert-github-action 2.0.0 composite
.github/workflows/run_precommit.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
  • pre-commit/action v3.0.1 composite
.github/workflows/test.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
  • pypa/gh-action-pypi-publish release/v1 composite
.github/workflows/update_precommit_hooks.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
  • peter-evans/create-pull-request v6 composite
pyproject.toml pypi
  • h5py *
  • hmmlearn *
  • ksvd *
  • matplotlib *
  • networkx *
  • nilearn >=0.10.2,!=0.10.3
  • pyclustering *
  • pycwt *
  • seaborn *
  • statsmodels *