https://github.com/makism/dyconnmap

A dynamic connectome mapping module in python.
https://github.com/makism/dyconnmap

clustering connectivity connectome dynamic eeg fc-states fmri functional-connectivity graph-analysis graph-distances graphs meg neuroimaging neuroscience python time-varying

Last synced: about 21 hours ago
JSON representation

A dynamic connectome mapping module in python.

• Host: GitHub
• URL: https://github.com/makism/dyconnmap
• Owner: makism
• License: bsd-3-clause
• Created: 2017-07-28T11:37:17.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-07-24T04:49:03.000Z (over 1 year ago)
• Last Synced: 2025-01-23T02:36:59.436Z (1 day ago)
• Topics: clustering, connectivity, connectome, dynamic, eeg, fc-states, fmri, functional-connectivity, graph-analysis, graph-distances, graphs, meg, neuroimaging, neuroscience, python, time-varying
• Language: Python
• Homepage:
• Size: 105 MB
• Stars: 104
• Watchers: 9
• Forks: 31
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        


### dyconnmap

A neuroimaging module for dynamic connectome mapping.

 

 

  build & code

  


   

[![Build Status](https://travis-ci.org/makism/dyconnmap.svg?branch=master)](https://travis-ci.org/makism/dyconnmap) [![Coverage Status](https://coveralls.io/repos/github/makism/dyconnmap/badge.svg?branch=master)](https://coveralls.io/github/makism/dyconnmap?branch=master) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/10f434822c3a4bdb89dd0bf43f524970)](https://www.codacy.com/gh/makism/dyconnmap/dashboard?utm_source=github.com&utm_medium=referral&utm_content=makism/dyconnmap&utm_campaign=Badge_Grade)

   

 

 

  package

  


 

 [![PyPI version](https://badge.fury.io/py/dyconnmap.svg)](https://badge.fury.io/py/dyconnmap) 

 [![Anaconda-Server Badge](https://anaconda.org/makism/dyconnmap/badges/version.svg)](https://anaconda.org/makism/dyconnmap)

 ![Whenenver a new tag is pushed; a wheel distribution is uploaded on the Test PyPI index.](https://github.com/makism/dyconnmap/workflows/publish-test-pypi/badge.svg)

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 [![Downloads](https://pepy.tech/badge/dyconnmap)](https://pepy.tech/project/dyconnmap)

 

 

 

 release

 


![python-versions](https://img.shields.io/pypi/pyversions/dyconnmap)

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[![Documentation Status](https://readthedocs.org/projects/dyconnmap/badge/?version=latest)](https://dyconnmap.readthedocs.io/?badge=latest)

 

 

 tutorials

 


  

   [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/makism/dyconnmap/master?filepath=tutorials)

 social & updates

 


  

   [![RG](https://img.shields.io/badge/RG%20Project-Python%20tools%20for%20Brain%20Network%20Analysis-%2300ccbb)](https://www.researchgate.net/project/Python-tools-for-Brain-Network-Analysis)

  

_dyconnmap_ (abbreviated from “dynamic connectome mapping”), a neuroimaging python module specifically designed for estimating the dynamic connectivity and analyzing complex brain networks; from neurophysiological data such as electroencephalogram (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) recordings. It includes numerous submodules to work with, such as chronnectomics and graph-theoretical algorithms, (symbolic) time series and statistical methods.

This is an ongoing effort to develop the module further and extend it by adding more algorithms related to graph analysis and statistical approaches. Considering the increasing acceptance and usage of python in analyzing neuroimaging data, we firmly believe that the module will be a great addition in every practitioner's toolbox engaged in brain connectivity analysis.

Built on [NumPy](http://www.numpy.org/), [SciPy](http://www.scipy.org/), [matplotlib](http://matplotlib.org/) and [networkx](https://networkx.github.io/).

#### Workflow outline

![workflow](docs/v2_pipeline.png)

#### Publications

* [ Marimpis, A. D., Dimitriadis, S. I., & Goebel, R. (2021). Dyconnmap: Dynamic connectome mapping—A neuroimaging python module. Human Brain Mapping, 1– 31. https://doi.org/10.1002/hbm.25589](https://onlinelibrary.wiley.com/doi/10.1002/hbm.25589) [![DOI](https://img.shields.io/badge/DOI-10.1002/hbm.25589-blue.svg)](https://doi.org/10.1002/hbm.25589)

* [poster presented @ 13th International Conference for Cognitive Neuroscience in Amsterdam \(ICON2017\)](https://f1000research.com/posters/6-1638) [![DOI](https://img.shields.io/badge/DOI-10.7490%2Ff1000research.1114652.1-blue.svg)](https://dx.doi.org/10.7490/f1000research.1114652.1) [under the previous name "dyfunconn"]

#### Resources

* [Installation](https://github.com/makism/dyconnmap/blob/master/INSTALL.md)

* [API Documentation](http://dyconnmap.readthedocs.io/?badge=latest)

* [Tutorials](https://github.com/makism/dyconnmap/tree/master/tutorials)

* [Examples](https://github.com/makism/dyconnmap/tree/master/examples)

#### Citation

If you use _dyconnmap_ or _dyfunconn_ in a published work, please consider citing.

    

        1.

         Marimpis, A. D., Dimitriadis, S. I., & Goebel, R. (2021). Dyconnmap: Dynamic connectome mapping—A neuroimaging python module. Human Brain Mapping, 42( 15), 4909– 4939. https://doi.org/10.1002/hbm.25589

    

    

        2.

        Marimpis, A. D., & Dimitriadis, S. I. (2017). dyfunncon: dynamic functional connectivity–a neuroimaging Python module. F1000Research, 6. https://doi.org/10.7490/f1000research.1114652.1

    

#### Sponsors

 

  Nov 2017 - Apr 2021

  Brain Innovation B.V.

 

 

  Sept 2017

  BRAINTRAIN (Taking imaging into the therapeutic domain: Self-regulation of brain systems for mental disorders) research project (FP7-HEALTH).