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Author

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1		-XWiki.michaels
	1	+XWiki.evanhancock

Content

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13	13	* [[Series of mini videos>>https://www.youtube.com/playlist?list=PLVtblERyzDeLcVv4BbW3BvmO8D-qVZxKf]]
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15	15	[[image:export_overview_new2.png]]
	16	+TVB on EBRAINS services.
16	16	)))
17	17	)))
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25	25	[[image:Screenshot 2020-10-19 at 09.14.45.png]]
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	28	+(% class="wikigeneratedid" %)
	29	+TVB on EBRAINS cloud infrastructure.
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	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	+
27	27	== TVB pipeline: Extract connectomes ==
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29	29	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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36	36	[[image:img1.png]]
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	45	+KnowledgeGraph search sidebar and exemplary dataset card with link to OpenNeuro repository.
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38	38	* Download the imaging data. The full dataset would be quite large: 54.06 GB. It contains several subjects, modalities, tasks and runs, most of which we don’t need to demo the workflow. We will therefore only download the minimal set of files that we need to form a valid BIDS data set and to perform the following steps. Using the Dataset File Tree on the right, download the files indicated in the following folder tree. The interface unfortunately only allows to download individual files, so you have to click each one of them and also you have to create the necessary folder structure (incl. the folders sub-01, anat, dwi, func) yourself. Note that the full data set contains multiple sessions identified by the keyword “ses-XX”, where “XX” indicates the session number. Here we use only data from ses-00 and therefore omit the folder and instead directly copy the folders “dwi”, “func”, and “anat” one level beneath “sub-01”. When you are done, your folder tree should look like this:
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40	40	[[image:tree.png]]
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	51	+Folder tree of the example data set.
	52	+
42	42	* We now have an MRI dataset in BIDS format. The next step is to compress the folder (e.g. as a .zip or .tar.gz file) so that we can upload it as a single file to the EBAINS Collaboratory and later to the supercomputer. In the next steps, we are going to use diffusion MRI tractography to reconstruct white matter fiber pathways and to estimate coupling weights between brain regions.
43	43	* Open the [[TVB Pipeline EBRAINS Collab>>https://wiki.ebrains.eu/bin/view/Collabs/tvb-pipeline/]].
44	44	* The pipeline is implemented in the form of a Jupyter notebook that shows how to upload data from local filesystem to EBRAINS drive; how to copy the data to the supercomputer; how to run the three docker containers that perform the processing; how to download results to local filesystem.
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48	48	[[image:sc.png]]
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	61	+Structural connectivity matrices. Left panel: weights, right panel: distances.
	62	+
50	50	== The Virtual Brain: Simulate brain activity ==
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52	52	The Virtual Brain is the main TVB software package. It is a neuroinformatics platform that provides an ecosystem of tools for simulating and analysing large-scale brain network dynamics based on biologically realistic connectivity. TVB can be operated via GUI and programmatic Python interface. On the EBRAINS Collaboratory Platform TVB Simulator usage is introduced through IPython Notebooks in the main TVB [[collab>>doc:Collabs.the-virtual-brain.WebHome||target="_blank"]]. Additionally, the TVB GUI can be directly accessed as [[a Web App>>https://thevirtualbrain.apps.hbp.eu/user/profile]]. Via the Web App users can configure simulations that are – depending on their complexity – either simulated directly on the web server or on a supercomputer, thereby making resource-consuming TVB functionality accessible to researchers that do not have access to supercomputers. Compiled standalone versions of the main software package can be downloaded from thevirtualbrain.org. In the following we take you through the main steps of brain network model simulation.
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119	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/]]
	134	+[[https:~~/~~/wiki.ebrains.eu/bin/view/Collabs/openminds-metadata/>>https://wiki.ebrains.eu/bin/view/Collabs/openminds-metadata/]]
122	122	)))
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