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29	29	* In order to use EBRAINS Collab software, it is necessary to download the notebook, create a new Collab, and upload the notebook there. The process is described on the main Collab page and in the notebook.
30	30	* For brain simulation the most important result of tractography is the structural connectome (SC), which consists of the coupling strengths matrix and the distances matrix. The former quantifies the strength of interaction between each pair of brain regions and the latter contains the average length of the respective fiber bundle. The exported SC can be directly imported to TVB: as one of the last steps of the pipeline, the SC was stored along with other data that can be read by TVB in the file "TVB_output.zip". Within that ZIP archive is the file “sub-<participant_label>_Connectome.zip”, which can be used to set up a brain network model in the other TVB workflows.
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32	32	== The Virtual Brain: simulate brain activity ==
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34	34	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 HBP Collaboratory Platform TVB Simulator usage is introduced through IPython Notebooks. Additionally, the TVB GUI can be directly accessed as a Web App ([[https:~~/~~/thevirtualbrain.apps.hbp.eu/user/profile>>url: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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60	60	* How to run a co-simulation from a notebook: [[https:~~/~~/collab.humanbrainproject.eu/#/collab/58136/nav/531966>>url:https://collab.humanbrainproject.eu/#/collab/58136/nav/531966]]
61	61	* Run a notebook from storage: [[https:~~/~~/collab.humanbrainproject.eu/#/collab/58136/nav/482634>>url:https://collab.humanbrainproject.eu/#/collab/58136/nav/482634]]
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63	63	== Fast_TVB: fast and parallel simulation ==
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65	65	Fast_TVB is thousands of times faster than Python TVB as it uses several optimization techniques and is implemented in the hardware-near language C. In addition, it is able to simulate in parallel, i.e., users can specify a number of threads that will simultaneously perform the processing and occupy multiple processors, as often done on supercomputers.
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76	76	* Follow the instructions in the Collab notebook or at [[https:~~/~~/hub.docker.com/r/thevirtualbrain/fast_tvb>>url:https://hub.docker.com/r/thevirtualbrain/fast_tvb]] to set up a brain model, simulate it and collect the results.
77	77	* 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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79	79	== TVB-HPC: high-performance computing ==
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81	81	In this project a toolbox has been created that supports the efficiently port TVB neural mass models between different computing architectures. This addresses the need that most models simulated in TVB are written in Python, and most of them have not yet been optimized for parallel execution or deployment on high-performance computing architectures. At the heart of this project is the development of a domain-specific language (DSL) that lets us define TVB models in a structured language that allows automatic code generation. Based on the model description computing code for different environments or hardware is automatically generated.
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103	103	The dataset contains BOLD time series averaged over 68 regions of interest according to the Desikan-Kilianny atlas, and a structural connectivity matrix displaying the fibers connecting each pair of these regions of interest, derived from the DWI data. The locations of the areas, the centers, the fiber lengths and densities are also included. The computational models are implemented at each of these regions of interested, connected according to the white matter fibers. The empirical functional connectivity matrix (the Pearson correlation among pairs of BOLD time series from each ROI) is used to fit the model.
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105		-Having learned how to create and simulate first brain models in the initial chapters, how to optimally implement them in the middle chapters, and how to implement disease mechanisms in the last two chapters, researchers may now combine these workflows and extend them to study other healthy or pathological brain processes.
	108	+Having learned how to create and simulate first brain models in the initial chapters, how to optimally implement them in the middle chapters, and how to implement disease mechanisms in the last two chapters, researchers may now combine these workflows and extend them to study other healthy or pathological brain processes.
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108		-== INCF training space ==
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110		-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.
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112		-[[https:~~/~~/training.incf.org/course/virtual-brain-education-pack-tvb-edupack>>url:https://training.incf.org/course/virtual-brain-education-pack-tvb-edupack]]
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115	115	)))
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