Package: nctpy
nctpy: Network Control Theory for PythonOverview.. image::https://zenodo.org/badge/370716682.svg:target:https://zenodo.org/badge/latestdoi/370716682.. image::https://readthedocs.org/projects/nctpy/badge/?version=latest:target:https://nctpy.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status
.. image::https://img.shields.io/pypi/l/ansicolortags.svg:target:https://pypi.python.org/pypi/ansicolortags/Network Control Theory (NCT) is a branch of physical and engineering sciences that treats a network as a dynamical
system. Generally, the system is controlled throughcontrol signalsthat originate at a control node (or control nodes) and
move through the network. In the brain, NCT models each region’s activity as a time-dependent internal state that is
predicted from a combination of three factors: (i) its previous state, (ii) whole-brain structural connectivity,
and (iii) external inputs. NCT enables asking a broad range of questions of a networked system that are highly relevant
to network neuroscientists, such as: which regions are positioned such that they can efficiently distribute activity
throughout the brain to drive changes in brain states? Do different brain regions control system dynamics in different
ways? Given a set of control nodes, how can the system be driven to a specific target state, or switch between a pair of
states, by means of internal or external control input?nctpyis a Python toolbox that provides researchers with a set of tools to conduct some of the
common NCT analyses reported in the literature. Below, we list select publications that serve as a primer for
these tools and their use cases:Gu, S., Pasqualetti, F., Cieslak, M. et al. Controllability of structural brain networks.
Nature Communications (2015).https://doi.org/10.1038/ncomms9414Gu, S., Betzel R. F., Mattar, M. G. et al. Optimal trajectories of brain state transitions.
NeuroImage (2017).https://doi.org/10.1016/j.neuroimage.2017.01.003Karrer, T. M., Kim, J. Z., Stiso, J. et al. A practical guide to methodological considerations in the
controllability of structural brain networks.
Journal of Neural Engineering (2020).https://doi.org/10.1088/1741-2552/ab6e8bKim, J. Z., & Bassett, D. S. Linear dynamics & control of brain networks.
arXiv (2019).https://arxiv.org/abs/1902.03309.. _readme_requirements:RequirementsCurrently,nctpyworks with Python 3.9 and requires the following core dependencies:- numpy (tested on 1.23.4)
- scipy (tested on 1.9.3)
- tqdm (tested on 4.64.1)Theutilsmodule also requires:- statsmodels (tested on 0.13.2)Theplottingmodule also requires:- seaborn (tested on 0.12.0)
- nibabel (tested on 4.0.2)
- nilearn (tested on 0.9.2)There are some additional (optional) dependencies you can install (note, these are only used for i/o and plotting in the
Python notebooks located in thescriptsdirectory):- pandas (tested on 1.5.1)
- matplotlib (tested on 3.5.3)
- jupyterlab (tested on 3.4.4)
- sklearn (tested on 0.0.post1)If you want to install the environment that was used to run the analyses presented in the manuscript, use the
environment.yml file.Basic installationAssuming you have Python 3.9 installed, you can installnctpyby opening a terminal and running
the following:.. code-block:: bashgit clone https://github.com/BassettLab/nctpy.git
cd nctpy
pip install .QuestionsIf you have any questions, please contact Linden Parkes (https://twitter.com/LindenParkes), Jason Kim (https://twitter.com/jason_z_kim), or Jennifer Stiso (https://twitter.com/JenniferStiso).