Changes for page User Story: TVB

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20 20  (((
21 21  (% class="col-xs-12 col-sm-8" %)
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23 +== The Virtual Brain at EBRAINS ==
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25 +[[image:Screenshot 2020-10-19 at 09.14.45.png]]
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23 23  == TVB pipeline: Extract connectomes ==
24 24  
25 25  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.
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105 105  
106 106  TVB EduPack provides didactic use cases for The Virtual Brain. Typically a use case consists of a jupyter notebook and a didactic video. EduPack use cases help the user to reproduce TVB based publications or to get started quickly with TVB. EduCases demonstrate for example how to use TVB via the Collaboratory of the Human Brain Project, how to run multi-scale co-simulations with other simulators such as NEST, how to process imaging data to construct personalized virtual brains of healthy individuals and patients. See the INCF Study Track [[here>>https://training.incf.org/studytrack/virtual-brain-simulation-platform]].
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112 +
113 +== openMINDS metadata ==
114 +
115 +EBRAINS uses the openMINDS schema to annotate neural data with metadata:
116 +
117 +[[https:~~/~~/github.com/HumanBrainProject/openMINDS>>https://github.com/HumanBrainProject/openMINDS]]
118 +
119 +We provide a Jupyter notebook where we demonstrate the process of how to fill up Python dictionaries with key-value pairs that specify metadata according to the openMINDS schema and how to then dump them into a set of JSON-LD files.
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121 +​​​​​​​[[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/openminds-metadata/>>https://wiki.ebrains.eu/bin/view/Collabs/openminds-metadata/]]
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