001024471 001__ 1024471
001024471 005__ 20250204113821.0
001024471 0247_ $$2doi$$a10.3390/app14052211
001024471 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-02192
001024471 0247_ $$2WOS$$aWOS:001182541600001
001024471 037__ $$aFZJ-2024-02192
001024471 082__ $$a600
001024471 1001_ $$0P:(DE-Juel1)179447$$avan der Vlag, Michiel$$b0$$eCorresponding author
001024471 245__ $$aVast Parameter Space Exploration of the Virtual Brain: A Modular Framework for Accelerating the Multi-Scale Simulation of Human Brain Dynamics
001024471 260__ $$aBasel$$bMDPI$$c2024
001024471 3367_ $$2DRIVER$$aarticle
001024471 3367_ $$2DataCite$$aOutput Types/Journal article
001024471 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1714549719_11807
001024471 3367_ $$2BibTeX$$aARTICLE
001024471 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001024471 3367_ $$00$$2EndNote$$aJournal Article
001024471 520__ $$aGlobal neural dynamics emerge from multi-scale brain structures, with nodes dynamically communicating to form transient ensembles that may represent neural information. Neural activity can be measured empirically at scales spanning proteins and subcellular domains to neuronal assemblies or whole-brain networks connected through tracts, but it has remained challenging to bridge knowledge between empirically tractable scales. Multi-scale models of brain function have begun to directly link the emergence of global brain dynamics in conscious and unconscious brain states with microscopic changes at the level of cells. In particular, adaptive exponential integrate-and-fire (AdEx) mean-field models representing statistical properties of local populations of neurons have been connected following human tractography data to represent multi-scale neural phenomena in simulations using The Virtual Brain (TVB). While mean-field models can be run on personal computers for short simulations, or in parallel on high-performance computing (HPC) architectures for longer simulations and parameter scans, the computational burden remains red heavy and vast areas of the parameter space remain unexplored. In this work, we report that our HPC framework, a modular set of methods used here to implement the TVB-AdEx model for the graphics processing unit (GPU) and analyze emergent dynamics, notably accelerates simulations and substantially reduces computational resource requirements. The framework preserves the stability and robustness of the TVB-AdEx model, thus facilitating a finer-resolution exploration of vast parameter spaces as well as longer simulations that were previously near impossible to perform. Comparing our GPU implementations of the TVB-AdEx framework with previous implementations using central processing units (CPUs), we first show correspondence of the resulting simulated time-series data from GPU and CPU instantiations. Next, the similarity of parameter combinations, giving rise to patterns of functional connectivity, between brain regions is demonstrated. By varying global coupling together with spike-frequency adaptation, we next replicate previous results indicating inter-dependence of these parameters in inducing transitions between dynamics associated with conscious and unconscious brain states. Upon further exploring parameter space, we report a nonlinear interplay between the spike-frequency adaptation and subthreshold adaptation, as well as previously unappreciated interactions between the global coupling, adaptation, and propagation velocity of action potentials along the human connectome. Given that simulation and analysis toolkits are made public as open-source packages, this framework serves as a template onto which other models can be easily scripted. Further, personalized data-sets can be used for for the creation of red virtual brain twins toward facilitating more precise approaches to the study of epilepsy, sleep, anesthesia, and disorders of consciousness. These results thus represent potentially impactful, publicly available methods for simulating and analyzing human brain states.
001024471 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001024471 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x1
001024471 536__ $$0G:(EU-Grant)101058516$$aeBRAIN-Health - eBRAIN-Health - Actionable Multilevel Health Data (101058516)$$c101058516$$fHORIZON-INFRA-2021-TECH-01$$x2
001024471 536__ $$0G:(GEPRIS)491111487$$aDFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x3
001024471 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x4
001024471 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x5
001024471 536__ $$0G:(EU-Grant)800858$$aICEI - Interactive Computing E-Infrastructure for the Human Brain Project (800858)$$c800858$$fH2020-SGA-INFRA-FETFLAG-HBP$$x6
001024471 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001024471 7001_ $$00000-0003-3848-914X$$aKusch, Lionel$$b1
001024471 7001_ $$00000-0001-7405-0455$$aDestexhe, Alain$$b2
001024471 7001_ $$0P:(DE-HGF)0$$aJirsa, Viktor$$b3
001024471 7001_ $$0P:(DE-Juel1)165859$$aDiaz, Sandra$$b4
001024471 7001_ $$00000-0002-5880-3098$$aGoldman, Jennifer S.$$b5
001024471 773__ $$0PERI:(DE-600)2704225-X$$a10.3390/app14052211$$gVol. 14, no. 5, p. 2211 -$$n5$$p2211$$tApplied Sciences$$v14$$x2076-3417$$y2024
001024471 8564_ $$uhttps://juser.fz-juelich.de/record/1024471/files/FZJ-2024-02192.pdf$$yOpenAccess
001024471 8564_ $$uhttps://juser.fz-juelich.de/record/1024471/files/FZJ-2024-02192.gif?subformat=icon$$xicon$$yOpenAccess
001024471 8564_ $$uhttps://juser.fz-juelich.de/record/1024471/files/FZJ-2024-02192.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
001024471 8564_ $$uhttps://juser.fz-juelich.de/record/1024471/files/FZJ-2024-02192.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
001024471 8564_ $$uhttps://juser.fz-juelich.de/record/1024471/files/FZJ-2024-02192.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
001024471 8767_ $$d2024-04-03$$eAPC$$jZahlung erfolgt
001024471 909CO $$ooai:juser.fz-juelich.de:1024471$$pdnbdelivery$$popenCost$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
001024471 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179447$$aForschungszentrum Jülich$$b0$$kFZJ
001024471 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165859$$aForschungszentrum Jülich$$b4$$kFZJ
001024471 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001024471 9141_ $$y2024
001024471 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001024471 915pc $$0PC:(DE-HGF)0003$$2APC$$aDOAJ Journal
001024471 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-10-26
001024471 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001024471 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-10-26
001024471 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2023-10-26
001024471 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001024471 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2023-10-26
001024471 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bAPPL SCI-BASEL : 2022$$d2024-12-27
001024471 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-27
001024471 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-27
001024471 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2024-04-10T15:30:53Z
001024471 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2024-04-10T15:30:53Z
001024471 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2024-04-10T15:30:53Z
001024471 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-27
001024471 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2024-12-27
001024471 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2024-12-27
001024471 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-27
001024471 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2024-12-27
001024471 920__ $$lyes
001024471 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001024471 980__ $$ajournal
001024471 980__ $$aVDB
001024471 980__ $$aUNRESTRICTED
001024471 980__ $$aI:(DE-Juel1)JSC-20090406
001024471 980__ $$aAPC
001024471 9801_ $$aAPC
001024471 9801_ $$aFullTexts