4.7 Article

Brain simulation as a cloud service: The Virtual Brain on EBRAINS

期刊

NEUROIMAGE
卷 251, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2022.118973

关键词

Brain modelling; Cloud; Connectome; Neuroimaging; Network model; High performance computing; Reproducibility; Data protection

资金

  1. Swiss National Supercomputing center CSCS
  2. Gauss Centre for Supercomputing e.V.
  3. H2020 Research and Innovation Action grants Human Brain Project [SGA2 785907, SGA3 945539, ICEI 800858, 826421, ERC 683049]
  4. Berlin Institute of Health & Foundation Charite
  5. Johanna Quandt Excellence Initiative
  6. European Union [800858]
  7. German Research Foundation [SFB 1436, 425899996, SFB 1315, 327654276, SFB 936, 178316478, SFB-TRR 295, 424778381, RI 2073/6-1, RI 2073/10-2, RI 2073/9-1]

向作者/读者索取更多资源

The Virtual Brain (TVB) is an open-source service on the cloud research platform EBRAINS, providing software for constructing, simulating and analyzing brain network models. It offers features such as MRI processing pipelines and automatic conversion of model equations, facilitating online collaboration and data discovery.
The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation.

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