Network control theory for python (nctpy)

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure–function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes’ general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called ‘network control theory for python’. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

Project Leads

Linden Parkes, Jason Z. Kim

Faculty Leads

Theodore D. Satterthwaite, Dani S. Bassett

Collaborators

Jennifer Stiso, Julia K Brynildsen, Matthew Cieslak, Sydney Covitz, Raquel E Gur, Ruben C Gur, Fabio Pasqualetti, Russell T Shinohara, Dale Zhou

Project Start Date

January 2023

Current Project Status

Published in Nature Protocols (2024) as A network control theory pipeline for studying the dynamics of the structural connectome

Datasets

Philadelphia Neurodevelopmental Cohort (PNC), Allen Mouse Brain Connectivity Atlas

Github Repository

https://github.com/LindenParkesLab/nctpy

Path to Data on Filesystem

n/a

Publication DOI

https://doi.org/10.1038/s41596-024-01023-w

Conference Presentations

Poster presentation at the The Organization for Human Brain Mapping Annual Meeting, June 2024. A network control theory pipeline for studying the dynamics of the structural connectome.