Treffer: The Virtual Brain Ontology: A Digital Knowledge Framework for Reproducible Brain Network Modeling.

Title:
The Virtual Brain Ontology: A Digital Knowledge Framework for Reproducible Brain Network Modeling.
Authors:
Martin L; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany., Bülau K; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany., Pille M; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany., Schmitt R; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany., Hüttl C; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany., Meier J; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany., Taher H; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany., Perdikis D; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany., Schirner M; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany.; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany.; Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Einstein Center Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany., Stefanovski L; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany., Ritter P; Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin (Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin), Charitéplatz 1, 10117, Berlin, Germany.; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, Berlin, Germany.; Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117, Berlin, Germany.; Einstein Center Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany.
Source:
BioRxiv : the preprint server for biology [bioRxiv] 2025 Nov 20. Date of Electronic Publication: 2025 Nov 20.
Publication Type:
Journal Article; Preprint
Language:
English
Journal Info:
Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
References:
Sci Am. 2001 Jun;284(6):34-5. (PMID: 11396337)
J Neurosci. 2006 Jan 25;26(4):1314-28. (PMID: 16436619)
PLoS Comput Biol. 2020 Apr 23;16(4):e1007822. (PMID: 32324734)
Nat Genet. 2000 May;25(1):25-9. (PMID: 10802651)
Biol Cybern. 1995 Sep;73(4):357-66. (PMID: 7578475)
PLoS Comput Biol. 2023 Dec 27;19(12):e1011761. (PMID: 38150479)
Nature. 2023 Aug;620(7972):47-60. (PMID: 37532811)
Nat Neurosci. 2014 Nov;17(11):1510-7. (PMID: 25349916)
Neuroimage. 2022 May 1;251:118973. (PMID: 35131433)
IEEE Trans Biomed Eng. 2016 Oct;63(10):2021-35. (PMID: 27046845)
Nat Methods. 2022 Dec;19(12):1568-1571. (PMID: 36456786)
Exp Neurol. 2022 Aug;354:114111. (PMID: 35569510)
Prog Neurobiol. 2011 Dec;95(4):629-35. (PMID: 21930184)
J Comput Neurosci. 2017 Feb;42(1):1-10. (PMID: 27629590)
Ann Neurol. 2017 Jul;82(1):67-78. (PMID: 28586141)
PLoS Comput Biol. 2020 Nov 30;16(11):e1008386. (PMID: 33253147)
Neuron. 2015 Oct 7;88(1):207-19. (PMID: 26447582)
Neuroimage. 2018 Apr 15;170:271-282. (PMID: 28536045)
Nature. 2022 Sep;609(7926):222. (PMID: 36064801)
Neuroimage. 2013 Oct 15;80:220-33. (PMID: 23707579)
Bioinformatics. 2017 Nov 15;33(22):3679-3681. (PMID: 28651363)
J Comput Neurosci. 2018 Dec;45(3):163-172. (PMID: 30377880)
Nat Methods. 2025 Apr;22(4):641-645. (PMID: 40175561)
Elife. 2018 Jan 08;7:. (PMID: 29308767)
Brain Connect. 2013;3(2):121-45. (PMID: 23442172)
J Neurophysiol. 2011 Sep;106(3):1125-65. (PMID: 21653723)
Neuroimage. 2017 Apr 15;150:395-404. (PMID: 28163141)
J Neurosci. 2014 Jun 4;34(23):7886-98. (PMID: 24899711)
PLoS Comput Biol. 2020 Feb 24;16(2):e1007696. (PMID: 32092054)
Contributed Indexing:
Keywords: Brain Simulation; FAIR research; Knowledge Base; Knowledge Engineering; Ontology; Scientific Computing; Semantic Code Generation Framework
Entry Date(s):
Date Created: 20251203 Date Completed: 20251222 Latest Revision: 20251222
Update Code:
20251222
PubMed Central ID:
PMC12667764
DOI:
10.1101/2025.11.19.689211
PMID:
41332756
Database:
MEDLINE

Weitere Informationen

Computational models of brain network dynamics offer mechanistic insights into brain function and disease, and are utilized for hypothesis generation, data interpretation, and the creation of personalized digital brain twins. However, results remain difficult to reproduce and compare because equations, parameters, networks, and numerical settings are reported inconsistently across the literature, and shared code is often not fully documented, standardized, or executable. We introduce The Virtual Brain Ontology (TVB-O), a semantic knowledge base, minimal metadata standard, and Python toolbox that simplifies the description, execution, and sharing of network simulations. TVB-O offers 1) a common vocabulary and ontology for core concepts and axioms representing current domain knowledge for simulating brain network dynamics, 2) a minimal, human- and machine-readable metadata specification for the information needed to reproduce an experiment, 3) a curated database of published models, brain networks, and study configurations, and 4) software that generates executable code for various simulation platforms and programming languages, including The Virtual Brain, Jax, or Julia. FAIR metadata and provenance-aware reports can be exported from TVB-O's model specification. It hereby enables a flexible framework for adopting new models and enhances reproducibility, comparability, and portability across simulators, while making assumptions explicit and linking models to biomedical knowledge and observation pathways. By reducing technical barriers and standardizing workflows, TVB-O broadens access to computational neuroscience and establishes a foundation for transparent, shareable "digital brain twins" that integrate with clinical pipelines and large-scale data resources.