Changes for page User Story: TVB

Last modified by ldomide on 2024/05/20 08:51
From version 33.1
edited by michaels
on 2020/10/19 09:45
Change comment: There is no comment for this version
To version 30.1
edited by michaels
on 2020/10/19 09:42
Change comment: Uploaded new attachment "overview_figure_v6.pdf", version {1}

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20 20  (((
21 21  (% class="col-xs-12 col-sm-8" %)
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24 -== The Virtual Brain at EBRAINS ==
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26 -[[image:overview_figure_v6.pdf]][[image:Screenshot 2020-10-19 at 09.14.45.png]]
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28 28  == TVB pipeline: Extract connectomes ==
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30 30  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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