Changes for page User Story: TVB

Last modified by ldomide on 2024/05/20 08:51
From version 37.1
edited by evanhancock
on 2020/12/10 10:17
Change comment: There is no comment for this version
To version 43.1
edited by michaels
on 2021/07/12 16:53
Change comment: Uploaded new attachment "overview_figure_v10_whitebg.pdf", version {1}

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26 26  [[image:Screenshot 2020-10-19 at 09.14.45.png]]
27 27  
28 28  (% class="wikigeneratedid" %)
29 -TVB on EBRAINS cloud infrastructure. Brain simulation and neuroimaging workflows require personal medical data that is applicable to data protection regulation. To protect personal data the services rely on
30 -end-to-end encryption and access control. EBRAINS provides several core services: 'Drive' is a service for hosting and sharing files; 'Wiki' and 'Office' allow users to create workspaces and documents for collaborative research; 'Lab' hosts sandboxed JupyterLab instances for running live code; 'OpenShift' orchestrates different services and provides resources for interactive computing; 'HPC' are supercomputing backends for resource-intensive computations. Core services interact with the different
31 -deployments of TVB services via a RESTful API and Unicore for communication with supercomputers. TVB services are deployed in the form of a Web GUI, container images, Python notebooks, Python libraries and high-performance backend codes. The TVB Image Processing Pipeline produces structural and functional connectomes from MRI data and its outputs can be ingested by KnowledgeGraph and annotated with openMINDS metadata, which allows re-using the connectomes in other services. The connectors show
32 -interactions between different components (colours group connectors for different deployments). The six TVB services are independent modules that can be combined according to the requirements of the research question.
29 +TVB on EBRAINS cloud infrastructure.
33 33  
31 +(% class="wikigeneratedid" %)
32 +Brain simulation and neuroimaging workflows require personal medical data that is applicable to data protection regulation. To protect personal data the services rely on end-to-end encryption and access control. EBRAINS provides several core services: 'Drive' is a service for hosting and sharing files; 'Wiki' and 'Office' allow users to create workspaces and documents for collaborative research; 'Lab' hosts sandboxed JupyterLab instances for running live code; 'OpenShift' orchestrates different services and provides resources for interactive computing; 'HPC' are supercomputing backends for resource-intensive computations. Core services interact with the different deployments of TVB services via a RESTful API and Unicore for communication with supercomputers. TVB services are deployed in the form of a Web GUI, container images, Python notebooks, Python libraries and high-performance backend codes. The TVB Image Processing Pipeline produces structural and functional connectomes from MRI data and its outputs can be ingested by KnowledgeGraph and annotated with openMINDS metadata, which allows re-using the connectomes in other services. The connectors show interactions between different components (colours group connectors for different deployments). The six TVB services are independent modules that can be combined according to the requirements of the research question.
33 +
34 34  == TVB pipeline: Extract connectomes ==
35 35  
36 36  As a first step we browse through The Knowledge Graph (KG) in order to find a suitable dataset to construct a brain model. The dataset must contain diffusion-weighted MRI data, in order to extract a structural connectome, which will form the basis of a brain network model. Structural connectivity extracted from diffusion MRI is used to quantify how strongly brain regions interact in the brain model. Next, the data set must contain functional MRI (fMRI) data, because a common approach is to tune the parameters of the brain model such that the simulated fMRI functional connectivity fits with the empirical fMRI data. For fitting, we usually compute functional connectivity matrices from simulated and empirical data. Finally, we need anatomical T1-weighted MRI to extract cortical surfaces and to perform a parcellation of the brain into different regions.
overview_figure_v10_whitebg.pdf
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