A Python Hands-on Tutorial on Network and Topological Neuroscience

Eduarda Gervini Zampieri Centeno, Giulia Moreni, Chris Vriend, Linda Douw, Fernando Antônio N. brega Santos

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Network neuroscience investigates brain functioning through the prism of connectivity, and graph theory has been the main framework to understand brain networks. Recently, an alternative framework has gained attention: topological data analysis. It provides a set of metrics that go beyond pairwise connections and offer improved robustness against noise. Here, our goal is to provide an easy-to-grasp theoretical and computational tutorial to explore neuroimaging data using these frameworks, facilitating their accessibility, data visualisation, and comprehension for newcomers to the field. We provide a concise (and by no means complete) theoretical overview of the two frameworks and a computational guide on the computation of both well-established and newer metrics using a publicly available resting-state functional magnetic resonance imaging dataset. Moreover, we have developed a pipeline for three-dimensional (3-D) visualisation of high order interactions in brain networks.
Original languageEnglish
Title of host publicationGeometric Science of Information - 5th International Conference, GSI 2021, Proceedings
EditorsFrank Nielsen, Frédéric Barbaresco
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
Volume12829 LNCS
ISBN (Print)9783030802080
Publication statusPublished - 2021
Event5th International Conference on Geometric Science of Information, GSI 2021 - Paris, France
Duration: 21 Jul 202123 Jul 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12829 LNCS


Conference5th International Conference on Geometric Science of Information, GSI 2021


  • Data visualisation
  • Network neuroscience
  • Topological data analysis

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