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@ARTICLE{vanderVlag:1024471,
      author       = {van der Vlag, Michiel and Kusch, Lionel and Destexhe, Alain
                      and Jirsa, Viktor and Diaz, Sandra and Goldman, Jennifer S.},
      title        = {{V}ast {P}arameter {S}pace {E}xploration of the {V}irtual
                      {B}rain: {A} {M}odular {F}ramework for {A}ccelerating the
                      {M}ulti-{S}cale {S}imulation of {H}uman {B}rain {D}ynamics},
      journal      = {Applied Sciences},
      volume       = {14},
      number       = {5},
      issn         = {2076-3417},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2024-02192},
      pages        = {2211},
      year         = {2024},
      abstract     = {Global 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.},
      cin          = {JSC},
      ddc          = {600},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / HBP SGA3 - Human
                      Brain Project Specific Grant Agreement 3 (945539) /
                      eBRAIN-Health - eBRAIN-Health - Actionable Multilevel Health
                      Data (101058516) / DFG project 491111487 -
                      Open-Access-Publikationskosten / 2022 - 2024 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487) / JL SMHB -
                      Joint Lab Supercomputing and Modeling for the Human Brain
                      (JL SMHB-2021-2027) / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS) / ICEI - Interactive Computing
                      E-Infrastructure for the Human Brain Project (800858)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)945539 /
                      G:(EU-Grant)101058516 / G:(GEPRIS)491111487 / G:(DE-Juel1)JL
                      SMHB-2021-2027 / G:(DE-Juel1)Helmholtz-SLNS /
                      G:(EU-Grant)800858},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001182541600001},
      doi          = {10.3390/app14052211},
      url          = {https://juser.fz-juelich.de/record/1024471},
}