Changes for page User Story: TVB

Last modified by ldomide on 2024/05/20 08:51
From version 28.1
edited by michaels
on 2020/08/03 11:22
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To version 34.1
edited by michaels
on 2020/10/19 09:45
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20 20  (((
21 21  (% class="col-xs-12 col-sm-8" %)
22 22  (((
23 +== The Virtual Brain at EBRAINS ==
24 +
25 +[[image:Screenshot 2020-10-19 at 09.14.45.png]]
26 +
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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76 76  
77 77  The EBRAINS Collaboratory “[[TVB C ~~-~~- High-speed parallel brain network models>>https://wiki.ebrains.eu/bin/view/Collabs/tvb-c-high-speed-parallel-brain-network-]]” explains how to use the container on supercomputer backends with a Jupyter notebook as frontend:
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79 -* Open the “[[TVB C ~~-~~- High-speed parallel brain network models>>https://wiki.ebrains.eu/bin/view/Collabs/tvb-c-high-speed-parallel-brain-network]]” Collab
83 +* Open the “[[TVB C ~~-~~- High-speed parallel brain network models>>https://wiki.ebrains.eu/bin/view/Collabs/tvb-c-high-speed-parallel-brain-network-]]” Collab
80 80  * Follow the instructions in the Collab notebook or [[here>>https://hub.docker.com/r/thevirtualbrain/fast_tvb]] to set up a brain model, simulate it and collect the results.
81 81  * Simulations are more efficient when only a single thread is created, but faster for multiple threads. Play around with the num_threads parameter and compare the execution speeds for different settings. If execution speed is the primary goal a higher number of threads is advised, if efficiency during parameter space exploration is the goal, then it is advised to use multiple single-threaded instances of the program.
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