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. 2022 Apr 12;9:168. doi: 10.1038/s41597-022-01219-3

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — Schematic outline of the brainstem connectome generation and simulations in the neuroVIISAS framework. Data generation starts with hypothesis of possible connections using stereotaxic atlases and concepts of knowledge (ontologies). Then TT experiments are performed and connections are published. Original research publications were evaluated and connections described herein were collated and imported into the rat connectome database to build the connectivity of adjacency matrices. These are starting points for global and local as well as motif network analysis. neuroVIISAS provides tools to investigate pathways and community detection algorithms like weighted stochastic block matching (WSBM), Louvain modularity and spectral graph analysis among oth-ers. The weights of a pathway can be reduced to model demyelination disorders (lesion) which can be compared with control coactivation matrices derived from excitatory FitzHugh Nagumo network propagation models.