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Netneurotools: an accessible, reproducible, and practical toolkit for network neuroscience

Poster No:

1188

Submission Type:

Abstract Submission

Authors:

Zhen-Qi Liu¹, Vincent Bazinet¹, Justine Hansen¹, Filip Milisav¹, Andrea Luppi², Eric Ceballos¹, Asa Farahani¹, Golia Shafiei³, Ross Markello¹, Bratislav Misic¹

Institutions:

¹Montreal Neurological Institute, Montreal, Canada, ²University of Oxford, Cambridge, Cambridge, ³University of Pennsylvania, Philadelphia, USA

First Author:

Zhen-Qi Liu
Montreal Neurological Institute
Montreal, Canada

Co-Author(s):

Vincent Bazinet
Montreal Neurological Institute
Montreal, Canada

Justine Hansen
Montreal Neurological Institute
Montreal, Canada

Filip Milisav
Montreal Neurological Institute
Montreal, Canada

Andrea Luppi
University of Oxford
Cambridge, Cambridge

Eric Ceballos
Montreal Neurological Institute
Montreal, Canada

Asa Farahani
Montreal Neurological Institute
Montreal, Canada

Golia Shafiei, PhD
University of Pennsylvania
Philadelphia, USA

Ross Markello
Montreal Neurological Institute
Montreal, Canada

Bratislav Misic
Montreal Neurological Institute
Montreal, Canada

Introduction:

Modern neuroimaging research requires proficiency in a range of computing and analytical tools. However, existing neuroimaging analysis software packages are often highly specialized, with "locked-in" pipelines that leave significant gaps and barriers throughout, preventing flexible analytical processes. For a new trainee working at the intersection of network neuroscience and brain imaging, bootstrapping a project typically involves myriad adventures through messy raw datasets, in-house code snippets, custom terminal commands, and dangling visualization tools. This regularly results in a code base that is hard to maintain, giving rise to a range of methodological and reproducibility issues. To tackle this challenge, we introduce netneurotools, an open, community-driven Python toolbox aiming at standardizing common procedures in network neuroscience.

Methods:

Netneurotools is an open-access Python package available at https://github.com/netneurolab/netneurotools. It hosts a diverse collection of functions including data handling, network & statistical modelling, and visualization. It is designed with modularity in mind and conform with current Python packaging recommendations.

For data ingress and visualization, "datasets" contains fetchers for a large curated collection of standard templates, common atlases, and datasets releases from publications; "interface" deals with external data formats, tools, and modalities; "plotting" provides a range of brain plotting functions, featuring painless surface rendering. For network reconstruction and metric estimation, "networks" contains standardized procedures for consensus and surrogate networks; "metrics" has optimized implementations of many network-theoretic metrics; "modularity" helps with streamlining the community detection workflow. For general statistical inquiries, "stats" consists of functions for correlations, regressions, and permutation tests; "spatial" implements some spatial statistics procedures. Finally, we have "experimental" for cutting-edge functions can be incorporated as they appear in the literature.

Results:

Netneurotools is a 7-year-old open-source Python package developed and maintained by the Network Neuroscience Lab at the Montréal Neurological Institute. It is "battle tested" in >50 peer-reviewer publications and widely used by the neuroimaging community, accumulating >60 starts, >30 forks on GitHub. As a toolbox by the trainees and for the trainees, it aggregates best practices and implements standardized analysis procedures by students from the lab. By design, we include a diverse set of functions covering all stages of a research project. Starting with dataset fetching and network reconstruction, followed by graph metric estimation and statistical inferences, and concluded by brain plotting, all functions are organized into well-designed modules.

Since 2024, we have been working on a major renovation with brand-new functions and modernized package structure. We provided an updated set of dataset fetchers including data releases from lab publications. We added and optimized a significant amount of graph-theoretical and statistical metrics. We also added a spatial statistics module in light of recent researches on the geometry and spatial organization of the brain. One highlight in this renewal is a new brain surface plotting function with PyVista backend, greatly relieving dependency issues. Finally, we include a module that interfaces with external tools, wrapping convoluted command-line calls into simple functions. We are also integrating methods and datasets contributions from the community.

Conclusions:

The challenge of accessible and reproducible research practice comes a long way. Netneurotools brings not only tools of the trade, but also our efforts to promote the FAIR principle in research coding. We envision netneurotools as an organic, living set of utilities that reflect the day-to-day needs of new and experienced trainees in the field.

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural) ¹

fMRI Connectivity and Network Modeling

Methods Development ²

Neuroinformatics and Data Sharing:

Informatics Other

Keywords:

Other - network neuroscience; connectomics; open-source development

^1|2Indicates the priority used for review

·Figure 1

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Provide references using APA citation style.

Markello, R. D., Hansen, J. Y., Liu, Z. Q., Bazinet, V., Shafiei, G., Suárez, L. E., ... & Misic, B. (2022). Neuromaps: structural and functional interpretation of brain maps. Nature Methods, 19(11), 1472-1479.

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