Changes for page User Story: TVB

Last modified by ldomide on 2024/05/20 08:51
From version 21.1
edited by michaels
on 2020/06/26 12:03
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To version 24.1
edited by evanhancock
on 2020/07/22 13:57
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Author
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1 -XWiki.michaels
1 +XWiki.evanhancock
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2 2  (((
3 3  (% class="container" %)
4 4  (((
5 -= From MRI to personalized brain simulation =
5 += From MRI to personalized brain simulation using TVB-EBRAINS integrated workflows =
6 6  
7 7  Here we explain step by step how to use The Virtual Brain (TVB) tools for end-to-end personalized brain simulation. We start by finding shared MRI data using KnowledgeGraph, create a brain model from extracted connectomes using TVB pipeline, and simulate neural activity using TVB brain network model simulators. We will use Jupyter notebooks on EBRAINS Collab platforms for frontend operations and supercomputers in the backend for intensive number crunching.
8 8  
9 -Overview video: [[https:~~/~~/youtu.be/_G_DjRb1ams>>https://youtu.be/_G_DjRb1ams||rel=" noopener noreferrer" target="_blank"]]
9 +Watch these videos to learn more:
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11 +* [[TVB on EBRAINS (short version)>>https://www.youtube.com/watch?v=EETRdGskiWQ&t=2s]]
12 +* [[TVB on EBRAINS (long version)>>https://www.youtube.com/watch?v=VYhR9RNRIpA&t=2s]]
13 +* [[Series of mini videos>>https://www.youtube.com/playlist?list=PLVtblERyzDeLcVv4BbW3BvmO8D-qVZxKf]]
14 +
11 11  [[image:export_overview_new2.png]]
12 12  )))
13 13  )))
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18 18  (((
19 19  == TVB pipeline: Extract connectomes ==
20 20  
21 -As a first step we browse through KnowledgeGraph 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.
25 +As a first step we browse through The Knowledge Graph 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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23 23  
24 24  * Open KG in browser: [[https:~~/~~/kg.ebrains.eu/search/>>url:https://kg.ebrains.eu/search/||rel="noopener noreferrer" target="_blank"]]
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Description
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1 -From MRI to personalized brain simulation. Here we explain step by step how to use The Virtual Brain (TVB) tools for end-to-end personalized brain simulation.
1 +TVB User Story: From MRI to personalized brain simulation using TVB-EBRAINS integrated workflows. Here we explain step by step how to use The Virtual Brain (TVB) tools for end-to-end personalized brain simulation.