Wiki source code of Developing workflows on the EBRAINS research infrastructure – an early career researchers’ perspective.

Last modified by marissadiazpier on 2024/06/18 16:30

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15.1	1	== Download the early version of this [[WhitePaper PDF.>>attach:YRW_whitepaper (1).pdf]] ==
9.1	2
16.1	3	==== ====
9.1	4
10.1	5
9.1	6	==== //This online document will be updated as new information is provided by the students.// ====
	7
13.1	8	== “Developing workflows on
	9	the Ebrains research infrastructure – an early career researchers’ perspective.” ==
5.1	10
13.1	11	Christian Mata, Christian Stephan-Otto, Nalan Karunanayake, Igori Comarovschii, Niccolò Mattiello, João Miguel Alves Ferreira,
5.1	12	Katia Djerroud, Nathaniel Adibuer, Aziz Ullah Khan, Claudia Bachmann, Alper Yegenoglu, Sandra Díaz, Marissa Díaz.
	13
	14	September 1, 2023
	15
	16
	17	(% style="text-align: center;" %)
	18	==== Abstract ====
	19
13.1	20	This white paper presents a collection of scientific workflows crafted by students under the guidance of the Scientific Liaison Unit (SLU) within the EBRAINS infrastructure. This document describes how they translated their research goals into structured workflows using a standardized process. The workflows showcased in this paper span varying levels of maturity, from abstract concepts to well-defined requirements within the scientific process. These early career researchers effectively articulated their requirements and harnessed EBRAINS tools to construct scientific workflows. The objective of this white paper is not only to highlight the practical application of SLU-developed tools and methodologies but also to serve as a valuable resource for the EBRAINS community, offering insights into the process of defining and executing scientific goals through modular and traceable workflows.
5.1	21
	22	(% style="text-align: center;" %)
16.1	23	==== ====
5.1	24
	25	(% style="text-align: center;" %)
	26	==== 1.Introduction ====
	27
	28	EBRAINS is the European infrastructure for brain research which consists of an ecosystem of software tools, hardware components and methods capable of interacting with each other. Neuroscience has become an extremely interdisciplinary field which requires tools, methods, procedures, standards and a common language to enable scientists to have a successful exchange of ideas and work together to address complex questions.
	29
	30	One of the most prominent characteristics of EBRAINS is its modularity and composability. EBRAINS enables users to describe their research journey in the form of workflows which cover different time spans, can be dynamic, recursive and easy to extend, share and reproduce. Tools and services are connected directly or indirectly in such a way that the output of one tool can serve as input to one or many other tools, allowing scientific work to be expressed as workflows. These workflows can be then executed manually or automatically, sequentially or in parallel and locally (in a workstation or local university cluster) or remotely (on dedicated cloud or supercomputing infrastructure) depending on the use case requirements. By doing this, EBRAINS promotes FAIR science and provides the neuroscience community a common space where research questions of different kinds can be addressed in a collaborative and secure manner.
	31
	32	In this white paper our intention is to illustrate a set of workflows that have been derived from different user groups. These are intended to serve as examples for the community and to help the EBRAINS users identify a process by which it is possible to define requirements to achieve a scientific goal and translate them into a chain of modular tasks which can be easily tracked, reproduced, analyzed and shared.
	33
	34	In this white paper we cover workflows at different levels of maturity. Some of them are in an abstract stage, where only a general goal or research idea has been identified and the first steps to create a working approach towards it have been taken. Other workflows are more mature and rely on well identified requirements within the scientific journey. These requirements can include: data querying and analysis, model generation, simulation, analysis of experimental and simulated data, visualization, machine learning, interaction with the neurorobotics or the medical informatics platforms, usage of large computing infrastructure, interactive simulations, emulation and simulation on neuromorphic hardware, storage, sharing, secure data transfer, etc.
	35
13.2	36	The results section first presents workflows from early career researchers who have limited or no experience with the EBRAINS ecosystem. The early career researchers were guided by the scientific liaison unit of the human brain project and the technical coordination team to describe their requirements and use EBRAINS tools to create both executive and scientific workflows. An executive workflow is the one which describes the interactions between scientists, groups, communities and other stakeholders. A scientific workflow describes the steps to be taken to collect observations, generate hypotheses, design, implement and execute experiments of different kinds, analyze, validate and visualize the results, asses the hypotheses, disseminate and share the results in an interactive manner.
5.1	37
	38	In a second part of the results we include the EBRAINS showcases, which represent the workflows with higher level of maturity. The EBRAINS showcases are examples of complex research questions which have been addressed using a combination of EBRAINS tools and services and which can be modified, extended or used as a template by end users.
	39
	40	* topics covered and associated workflows (aim to give the reader a shortcut to workflows that might be interesting to her/him)
	41	* (((
	42	workflows of different readiness levels:
	43
13.2	44	1. early-stage workflows: the implementation of the workflow has not started yet or is still in the first phases. Here, mainly early career researchers contributed. If you find a workflow particularly interesting, please do not hesitate to contact the authors.
5.1	45	1. middle-stage workflows: workflows that are currently in the implementation process. Here, authors can share already first experiences with the implementation
	46	1. late-stage workflows: workflows that have been almost completed. Here, we will also present some showcases of EBRAINS.
	47	)))
	48	* the general structure how the workflow are represented (include legend for the diagrams)
	49	* (((
	50	short list of tools and services with name, short description, link to material.[[image:legend_workflows.png\|\|alt="Figure1. Legend Workflows"]]
	51	)))
	52
	53	(% style="text-align:center" %)
	54	[[image:Screen Shot 2023-09-11 at 17.57.38.png]]
	55
	56	(% style="text-align:center" %)
	57	[[image:Screen Shot 2023-09-11 at 17.59.04.png\|\|alt="Table 1: Tool overview. Tools are classified according to their field of application: ’Data and knowledge’, ’Atlas and image processing’, ’Community’, ’Data analysis, visualisation and validation’, ’Medical data analysis’, ’Model building and simulation’, ’Brain Inspired Technologies’ and ’High performance computing’. For each tool a short description is given and a link to more information."]]
	58
	59	(% class="small" %)Table 1: Tool overview. Tools are classified according to their field of application: ’Data and knowledge’, ’Atlas and image processing’, ’Community’, ’Data analysis, visualisation and validation’, Medical data analysis’, ’Model building and simulation’, ’Brain Inspired Technologies’ and ’High performance computing’. For each tool a short description is given and a link to more information.
	60
	61
	62	(% style="text-align: center;" %)
	63	==== 2 Methods ====
	64
	65	It can be challenging for new EBRAINS users to determine how EBRAINS products can assist them in realizing their scientific projects. In order to help in this process, the Scientific Liaison Unit of EBRAINS (SLU) has developed a standard description of workflows, which allows them to map the scientific project to software and compute resources and plan out its implementation.
	66
13.2	67	Aside from this support, the HBP also organizes outreach activities targeted at different scientific communities. In this regard, a special focus is placed on early career researchers who wish to learn how to use EBRAINS.
5.1	68
13.2	69	In order to learn how to use EBRAINS, it is helpful to understand how other scientific projects have been translated into EBRAINS workflows. Also in this regard, HBP seeks to support EBRAINS users. Following this idea, we (the authors of this paper) investigated the possibility of writing a white paper that serves as a database of different workflows and making them available to other researchers. In order to collect such use cases, we decided to combine the outreach efforts of the education program and the student ambassadors of the HBP with SLU formalization and technical coordination (TC). A students’ workshop was chosen as the best means of collecting use-cases.
5.1	70
	71	With the students’ workshop we wanted to accomplish the following goals:
	72
8.1	73
16.1	74	* Collecting different neuroscientific projects suitable for creating workflows in EBRAINS.
8.1	75	* Presenting the different EBRAINS tools and computing resources to the students.
	76	* Teaching students how to create their own EBRAINS workflows.
	77	* Supporting students in creating their first workflows.
	78
5.1	79	Accordingly we structured the workflow as follows:
	80
	81	In the first part we introduced the different EBRAINS tools and services.
	82
	83	In the second part, we explained the concept of a workflow and how it applies to EBRAINS workflows. In this context, we introduced a workflow generation template in the form of a mural board (see Fig. 2). By using this template, all necessary information on the science side could be collected and translated into a workflow in a graph-like format. Based on this information, this paper’s workflow descriptions were created.
	84
	85	In the third part we asked students to present their scientific project idea. For this part we asked students to give a presentation prior to the workshop. There was no requirement for the project to have already produced any kind of results or to have used EBRAINS tools. There was only one thing that was important: presenting the scientific idea.
	86
8.1	87	How to create EBRAINS workflows from these scientific projects was discussed after the presentations. Here, the mural board were of great assistance and helped the students and tutors to streamline. In this last part of the workshop, we reserved extra time for students to ask questions, seek assistance, and/or present and discuss their first Mural boards.
5.1	88
	89	The workshop idea proved to be successful. During the 1,5 days of the workshop, we had 9 participants, of which 5 agreed to share their workflows in this paper.
	90
13.2	91	This work was made possible by the following groups sharing their workflows, which were subsequently integrated into this work after a long post-processing period, which included many emails between early career researchers and organizers.
5.1	92
	93	* Research Center Sant Joan de Déu in Spain, Christian Mata and Christian Stephan-Otto (work- flow 2)
	94	* Nalan Kraunanayake, a PhD in Biomedical Engineering and Prof. Dr. Stanislav S. Makhanov both working at Thammasat University in Thailand (workflow 3)
	95	* Igori Comarovschii, Mickkel Vinding and Prof. Daniel Lundqvist from the Karolinska Institute as well as Pascal Helson for the KTH Royal Institute of Technology in Sweden (workflow 4)
	96
	97	• PhD student Niccolò Mattiello of Gerardo Biella’s group at the University of Pavia (workflow 5)
	98
8.1	99	• Katia Djerround, a master student at the Algerian University of Science and Technology Houari Boumediene, Nathaniel Adibuer, a research assistant at the University of Ghana, and Aziz Ullah Khan, a professional engineer.
5.1	100
	101
8.1	102	(% style="text-align:center" %)
	103	[[image:Marissa Diaz_2023-03-02_14-43-52.png]]
	104
	105	(% class="small" %)Figure 2: Mural board presented to the students during the workshop with an invite to fill it out. Each field on the board had a short caption and a short explanation of what to fill in: Team: the team name, the members and and one sentence per each group member to introduce themselves.
	106
	107
	108	Background: Explaining the main trends of the state of the art to address the research question the reasearchers are working on; Problem: Decomposing the main challenge into subproblems; Vision: Where does the research group want to go? What are dreams of the researchers for future researchers working in your field?; Impact: What difference will the researchers make? Who else will benefit from the designed workflow?; Solution elements: Brainstorm the elements which would be key to solve your problem; Challenges:What is missing to achieve the researcher’s vision and can they address this using EBRAINS tools?; Workflow: Associating solution elements to EBRAINS components, and ordering them in a workflow; the construction of diagrams make use of the symbols described in 1.
	109
5.1	110	In the following chapter, the scientific projects and the corresponding workflows are explained. An overview of the tools used in these workshops is given in the table 1. Tools are categorized here according to their fields of application. A short description of each tool is provided, along with a link to a more detailed explanation.
	111
	112	(% style="text-align: center;" %)
	113	==== 3. Results ====
	114
	115	The cases, which we will present in more detail later in this section, are very widely spread in terms of content, data types they ingest, tools they are using, maturity level and background of the corresponding scientists.
	116
	117	One of the use-cases, for example, focuses on establishing a methodology to facilitate the creation of 6
	118
8.1	119	brain atlases for a particular target group. Other studies investigate phenomena like contour grouping, oscillation in Parkinson’s Disease, cell and network properties of the entorhinal cortex, and the use of imaging signals, specifically the EEG, to improve brain-computer interfaces.
5.1	120
	121	In order to help the reader to quickly identify the most relevant use cases, we will briefly sketch each use case in a couple of sentences. This will include a quick overview of the use case content, the data it treats, EBRAINS tools used, maturity level achieved so far and information on the research group.
	122
16.1	123	subsection 3.1 The researcher João Miguel Alves Ferreira,of Medicine of the University of Coimbra, is innovating with new therapies to enhance our understanding of the effects of environmental changes in the function of our brains and create new therapies for depression and anxiety disorders.
	124
8.1	125	subsection 3.2 The researchers at Research Center Sant Joan de Déu in Spain, Christian Mata and Christian Stephan-Otto, desire to create anatomical brain templates of specific human subgroups from images in the BIDS format. Among the tools they are using are the Knowledge Graph, Quick NII, and the Brain Atlas of EBRAINS.
5.1	126
8.1	127	subsection 3.3 In the third workflow, Nalan Kraunanayake, a PhD in Biomedical Engineering and Prof. Dr. Stanislav S. Makhanov both working at Thammasat University in Thailand, present a project in which contour grouping is investigated. Using robot simulations of visual neuro function, the group explores how local and global stimulus properties affect contour grouping. Here, EBRAINS tools and services such as the NRP, NEST and L2L support this research.
5.1	128
	129	subsection 3.4 Igori Comarovschii, Mickkel Vinding and Prof. Daniel Lundqvist from the Karolinska Institute as well as Pascal Helson for the KTH Royal Institute of Technology in Sweden present a work- flow that investigates beta oscillations in Parkinson’s Disease and the possibility of DBS treatment. To this end, a multiscale model is constructed based on MEG data collected under different experimental conditions. In particular the effect of DBS should be studied in this model. Thus, the overall goal is to create a workflow that leads from raw MEG data to a prediction of how MEG activity is influenced by DBS. This is accomplished with the aid of BIDS manager, Knowledge Graph, TVB, TVB imaging pipeline, NEST, and L2L. [The maturity level of this project is probably already advanced but this is something we have to ask them]
	130
8.1	131	subsection 3.5 PhD student Niccolò Mattiello of Gerardo Biella’s group at the University of Pavia is investigating the different functional interactions of heterogeneous neurons in the entorhinal and perirhinal cortex. With this aim in mind he first focuses on single neuron model properties, collecting and analysing the data with the help of Neo, Elephant, Elephant Vis, BluePyEfe and Neuro Tech Mesh and, in a subsequent step investigates how the different experimental results influence the neuron’s activity in a simulated environment using Arbor, Neuron, BluePyEfe, BluePyOpt, L2L, Validation Framework and Hippo Unit. In a second step a larger cortical network is considered. Here data analysis can be carried out via Nutil and VisuAlign, Ilastik and Neuro Tech Mesh. For the creation of a model of a large network and its simulation, Brain Scafold Builder, Arbor, Core Neuron ViSimple, Neuroscheme, BluePyOpt, L2L, and Network Unit are considered.
5.1	132
	133	Worflow 6:Katia Djerround, a master student at the Algerian University of Science and Technology Houari Boumediene, Nathaniel Adibuer, a research assistant at the University of Ghana, and Aziz Ullah Khan, a professional engineer from X, aim to develop a systematic approach for utilizing EEG data in Brain Computer Interfaces (BCI). A GDPR compliant data storage location combined with a standard data processing procedure should be made possible using the Health data cloud and tools like Frites and Neo. As a second step, the potential of MEG data for steering a robot is examined using the NRP. Finally, the loop is closed by exploring the potential of EEG signals to interact with a robotic application via a TVB simulation embedded in a NRP environment. Currently, this project is in the planning stage.
	134
8.1	135
5.1	136	(% style="text-align: center;" %)
	137	3.1 Workflow 1
	138
13.2	139	3.1.1 Team
5.1	140
13.2	141	• João Miguel Alves Ferreira - molecular and translational neuroscience, PhD.
	142
	143	3.1.2 Background
	144
	145	There have been several projects in the past relating the effect of cultural activities in the wellbeing of patients suffering from a variety of disorders including anxiety and depression.
	146
	147	3.1.3 Problem
	148
	149	Many patients who suffer from depression, anxiety, etc. are prescribed drugs to treat the symptoms of these disorders.
	150
	151	However, it is possible that a large part of these patients do not really have neurological disorders and
	152	Could get real improvement in their overall wellbeing by changing their environment and lifestye.
	153
	154	3.1.4 Vision
	155
	156	Develop and test a new treatment program focused on inserting patients with different disorders such as depression and anxiety into a culturally rich and nurturing environment in order to eliminate/reduce the need for drugs.
	157
	158	Create a modus operandi which can be transformed into a course which can be shared and taught to other museums / cultural centers all around the world on how to help people with therapeutic protocols to enhance the quality of life.
	159
	160	3.1.5 Impact
	161
	162	This innovative perspective on the treatment of these disorders could:
	163	• Reduce the dependency of patents on drugs.
	164	• Have a better and more sustainable effect on the relief of these disorders,
	165	• Provide a more economic way to deal with mental disorders.
	166	• Enhance our understanding of the effects of environmental changes in the function of our brains.
	167
	168	3.1.6 Solution elements
	169
	170	Instead of prescribing drugs, get the patient involved into a well being program which embeds her/him into a new culturally rich environment, being it a museum or a botanical garden,
	171	Personalize it and prove that has a real effect.
	172	In order to formulate a scientific background for the effects of such innovative protocol, there should be a link to neurobiology, brain network plasticity, etc.
	173	• Measure some levels of anxiety hormones before and after the program
	174	• Measure other kinds of hormones like serotonine and oxitosin during and after the program.
	175	• Do some statistics on the data and see if the course of treatment is helping the people overcome the disorder.
	176	• We can also do psychological batteries of tests.
	177	• Create a collaborative network with neuroscientists to research the potential links of these environmental changes in network function at different scales.
	178
	179	• Explore the possibilty of conducting an imaging study (e.g MRI) and see if the program alters the brain activity at arge scale (and link to altered hormone levels)
	180
	181	• Explore the possibility of creating models which link mental states to neuroscience models of brain activity.
	182
	183	3.1.7 Challenges
	184
	185	• Figure out how to connect the mental disorders withthe neurobiology underneath.
13.3	186	• Find good measures of success for the effects of the new treatment that can be reproduced in a reliable and robust way.
13.2	187	• Develop a strong community to support the implementation of such approaches into everyday treatment plans.
	188
	189	3.1.8 Workflow
	190
15.1	191	[[image:image-20240618162546-1.png]]
13.2	192
5.1	193	(% style="text-align: center;" %)
	194	3.2 Workflow 2
	195
	196	3.2.1 Team
	197
	198	• Christian Mata - computer vision, PhD
	199	• Christian Stephan-Otto - neuroimaging, PhD
	200
	201	3.2.2 Background
	202
	203	We want to design and manage a biobank of images for EBRAINS to generate anatomical brain templates representing a specific subpopulation.
	204
	205	3.2.3 Problem
	206
	207	There is a lack of standardized brain anatomical templates segregated by ages, gender, disease, etc. to quantify the deviation of different groups of patients in each brain region.
	208
	209	3.2.4 Vision
	210
	211	We live in a time in which data is generated at an enormous speed. Researchers see the potential contributions to human knowledge that could be attained from analysing such large, reliable databases. Our approach is to provide researchers and society in general with a tool to better characterize the anatomy of the human brain in all its diversity, ensuring more precise and robust neuroscientific findings.
	212
	213	3.2.5 Impact
	214
	215	3.2.6 Solution elements
	216
	217	One the one hand, we want to create a pipeline that allows to create a biobank of images available to all EBRAINS users. On the other hand, the pipeline should be able to generate anatomical templates representative of the subpopulation the user can choose from the biobank.
	218
	219	For example, the user can ask for a template for young women aged from 22 to 24, in which all the segmentations of the Multiscale Atlas of the human brain could be projected.
	220
	221	As mentioned before, being able to create a repository of cases validated by experts and creating a specific database to be able to access specific cases is a very eﬀicient process. One of the main solutions is to be able to collect massive data in a practical and simple way for research projects or post-analysis.
	222
	223	3.2.7 Challenges
	224
13.2	225	EBRAINS’ tools offer many solutions and the main challenge is to correctly choose a specific application. The tool that could be useful for this purpose is the Multiscale Atlas of the Human Brain.
5.1	226
	227	For this reason, being able to create a repository of cases validated by experts and to create a specific database to be able to access specific cases represents one of the main objectives.
	228
	229	3.2.8 Workflow
	230
	231	A possible workflow is depicted in Figure 3 and outlined in the following:
	232
	233	1. (((
	234	Use the ’BIDS manager’ to organize own images, upload those images to the common repository
	235
	236	and label them according to the subject’s characteristics (age/sex/pathology/. . .).
	237	)))
	238	1. The user can request a brain template representative of certain profiles (e.g. Rett syndrome patients, all female, between 10 and 12 years old). For this, the ANTs software could be used to create such template, after preparing the images using the alignment and resizing capabilities of the QuickNII tool (https:~/~/ebrains.eu/service/quint). Then, some of the results of the Human Brain Atlas such as segmentations may be transferred to the new template, which in turn is now accessible for other researchers.
	239
	240	Figure 3: Workflow of the BIDS databank.
	241
	242	3. Every new template could be part of a live-paper, which would describe the characteristics of the population and the methodology for creating it.
	243
	244	(% style="text-align: center;" %)
	245	3.3 Workflow 3
	246
	247	3.3.1 Team
	248
	249	• Nalan Karunanayake
	250
	251	• Prof. Dr. Stanislav S. Makhanov
	252
	253	Institute: Biomedical Engineering Research Unit, Sirindhorn International Institute of Technology, Thammasat University, Thailand.
	254
	255	3.3.2 Background
	256
	257	We investigate how Contour Grouping can be performed using the neurorobotics platform (NRP) tools and associated modules. Furthermore, we develop a biologically plausible model inspired by the human visual system using machine learning, robotics (virtual) and physics-based simulations.
	258
	259	3.3.3 Problem
	260
	261	The perception of a shape depends on the organisation of its boundaries from local orientation edges into global continuous contours (contour grouping) [45, 46]. Computer scientists generally assume that grouping is based on local Gestalt principles such as proximity and good continuation [47, 48]. However, there are also reports that the global property of contour closure and surface features are involved in this process [49, 50, 51]. This raises the question: Is contour grouping completely insensitive to the global properties of the stimulus and dependent only on local features, or is it a mixture of interrelated local and global features? The human visual system is an integration of many subprocesses such as attention, eye movement, and prior knowledge. To study the relationship between shape perception and contour grouping, the neurorobotics platform (NRP) is very well suited as it has access to many expert vision modules [52] that can be integrated and tested under different virtual experiment conditions. With the NRP, we can integrate the virtual robot with various visual neuro functions [53] powered by NEST [54] to study brain activities to group contours and understand sensory processing through motor execution.
	262
	263	3.3.4 Vision
	264
15.1	265	We want to build a biologically explainable model that can explain the human vision of shape perception. How do we perceive shapes, and on what factors is our perception of shapes based? The true mental model for understanding the environment, color, depth, shapes, etc. Is learning an essential factor in human visual shape perception? Additionally, we aim to integrate robotics into the artificial vision models to verify the reliability and expand the feature set and strength of the frameworks.
5.1	266
	267	3.3.5 Impact
	268
	269	Our approach facilitates implementation, and through collaboration, it is possible to share knowledge among peer researchers. That would make the job easier and more fun. Industrial research institutions can also benefit from this approach. Collaboration, pre-implemented modules, and reproducibility make the EBRAINS workflow very appealing and helpful. The integration of robotics in vision research and expansion of the research space in artificial vision models
	270
	271	3.3.6 Solution elements
	272
	273	1. Develop a computational (biologically explainable) experimental vision model using NRP to investigate contour grouping.
	274	1. Use the NEST brain model with other modules (Python modules like PyTorch ) and a virtual environment with iCub.
	275	1. Use NRPs modules such as the Retina module, the Salience module, and the Laminart model.
	276	1. Benefit from the synchronization, scalability and reproducibility of NRPs to make the study flow straightforward.
	277	1. Use Gazebo (or Netlogo) with NRP to construct physics-based simulation modules to study brain activities (neural dynamics) of contour grouping and understand sensory processing through motor execution.
	278
	279	3.3.7 Challenges
	280
	281	We want to integrate various modules from already implemented experiments and easily add or remove modules on the platform. With the inclusion of a virtual robot unit (iCub), the classic 2D visual research will be extended to 3D to include depth features into the study. Additionally, we would like to integrate the Gazebo simulation platform or the NetLogo multi-agent framework with NEST to investigate the dynamics of the neuronal activities of the contour grouping.
	282
	283	3.3.8 Workflow
	284
	285	After successfully implementing the proposed model in NEST on the NRP, we consider running the simulations on the neuromorphic hardware platform (SpiNNaker) using spiking neural networks to make the model more biologically plausible and implement it at the hardware level integrating neuro- morphic sensors and actuators.
	286
	287	(% style="text-align: center;" %)
	288	3.4. Workflow 4
	289
	290	3.4.1 Team
	291
	292	Figure 4: zebo workflow
	293
	294	* IgoriComarovschii,AﬀiliatedtoresearchDepartmentofClinicalNeuroscience,igori.comarovschii@ki.se
	295	* MikkelVinding,AﬀiliatedtoresearchDepartmentofClinicalNeuroscience,NatMEG,mikkel.vinding@ki.se
	296	* Pascal Helson, NeuroLogic group KTH Royal Institute of Technology, Stockholm, pashel@kth.se
	297	* (((
	298	Daniel Lundqvist, Senior research specialist Associate Professor, Head of Neuro division, and coordinator at CIR - Centre for Imaging Research, daniel.lundqvist@ki.se
	299
	300	3.4.2 Background
	301
	302	To understand cortical beta burst dynamics alteration it is relevant to consider the beta oscillatory network including the cortex, basal ganglia, and subthalamic nucleus. Indeed beta burst dynamics such as individual bursts’ amplitude and duration as well as burst patterning are altered in Parkinson’s disease (PD) yet normalized by chronic subthalamic deep brain stimulation (cDBS). Modulation and normalization in turn lead to improved motor function at the behavioral level. (Pauls)
	303
	304	To facilitate a deep understanding of how these neuromodulatory methods and pharmacological treat- ments can shift the brain dynamics from a diseased brain to a healthy state resting-state MEG could be used. MEG beta burst dynamics has the potential not only as the biomarker of PD but also as the biomarker for treatment and disease follow-up.
	305
	306	Virtual deep brain stimulation (DBS) using The Virtual Brain multiscale co-simulation has the potential for DBS optimization and forecasting of individualized DBS treatment. Multiscale simulation of empirical data-derived cortical beta burst dynamics altered by Parkinson’s disease but normalized by DBS could shed light on mechanisms of PD. The validation of such a model could be the normalization of beta burst activity following virtual DBS.
	307
	308	3.4.3 Problem
	309
	310	We explore multiscale PD signatures via brain network reconstruction from empirical data for per- sonalized virtual brain models for adaptive DBS therapy. However, not all categories of the data discussed have the permit to be shared. Furthermore, not all categories of MEG data are accompanied by structural MRI.
	311
	312	3.4.4 Vision
	313
	314	Human Brain Project visions and values (https:~/~/community.ebrains.eu/_ideas/-MuqsAgs1AnL0PK2RVJF/ about)
	315
	316	Link introduces the 3D web application (https:~/~/yngvifrey.github.io/Stockholm/) highlighting HBP values and visions of integration of brain-related data to create digital twins, neuromorphic computing, spiking-based algorithms, services for sensitive data, implementation of data safety by design. Given values and vision provide a basis for brain simulation ecosystem need for multiscale simulation of data derived neuromarkers.
	317	)))
	318
	319	Community development and collaboration through the EBRAINS Co-create AI and Digital Brain Health.
	320
	321	Support of Sustainable Development Goals
	322
	323	3.4.5 Impact
	324
	325	Our approach supports open science by using the EBRAINS FAIR MEG PD Database: ”The Swedish National Facility for Magnetoencephalography (NatMEG) Parkinson’s Disease Dataset” on EBRAINS. We explore the multiscale mechanism of neuromarkers from clinical data with simulation. Finally, we develop clinical analytical tools and improve of therapeutic interventions for Parkinson’s disease.
	326
	327	3.4.6 Solution elements
	328
	329	We describe two solutions, the empirical data and the multi-scale simulation. Empirical data
	330	The Swedish National Facility for Magnetoencephalography at Karolinska Institute, Stockholm sam- pled the largest existing MEG database on PD. Part of this database-“The Swedish National Facility for Magnetoencephalography Parkinson’s Disease Dataset” is currently under EBRAINS curation. Moreover, together with our collaborators from Aalto University and Helsinki University Hospital, Helsinki, Finland we are updating and merging databases with the DBS PD study.
	331
	332	Thus we have empirical resting-state MEG data describing brain dynamics consisting of:
	333
	334	* Chronic subthalamic DBS therapy (cDBS) OFF, Medication OFF (12 hours after the last dopaminergic medication)
	335	* cDBS OFF, Medication ON (normal medication)
	336	* cDBS ON,Medication OFF (12 hours after the last dopaminergic medication)
	337	* cDBS ON, Medication ON.(normal medication)
	338	* (((
	339	Healthy controls (age-matched)
	340
	341	Additionally, passive movements task-related brain dynamics will be included:
	342
	343	The Passive movements study aimed to measure cortical activity related to the processing of propri- oceptive signals affected by PD in comparison to healthy control. The passive movements task was performed with the help of pneumatic artificial muscles (PAM) to induce proprioceptive feedback by brief passive movements (200 ms) of the index finger every 3.5-4.0 seconds (Vinding et.al. 2019).
	344
	345	Experimental workflows to obtain the data are described in the following articles: • Resting-state MEG [55, 56, 57]
	346	• Task-related MEG [58]
	347
	348	In addition to MEG structural MRI will be included. It should be noted that MRI data exists only in some categories of patients. For example, DBS-related resting-state MEG(for patients and control) is not accompanied by MRI data. In this case, we have only sensor-level data.
	349
	350	We do not have tractography to construct an individualized connectome as a structural base for the functional oscillatory network. What could be the solution in this case if we want to construct TVB models? Could we use atlas for example human connctome mm1?
	351
	352	Multiscale simulation
	353
	354	Individualized brain simulations could be performed by The Virtual Brain (TVB). Data could be integrated using Individual cortical geometry (surface-based simulation), and individual tractography (employing DTI) to estimate structural connectivity where every node in the TVB network represents a brain region and its dynamics are simulated with a mean-field neural mass model (e.g. WilsonCowan, Jansen and Rit). As an option focal stimulation could be introduced. Following numerical integration as an output Local Field Potentials or EEG, MEG, sEEG via forward solution. further, the model could be fitted to the empirical data.
	355	)))
	356
	357	The recent approach extended TVB to multiscale adding fine-scale simulation spiking neurons in the basal ganglia-thalamic-cortical network. The use case is virtual DBS. The validation of such a model could be the normalization of beta burst activity following virtual DBS.[59]
	358
	359	Beta bursts could be simulated by next-generation neural mass models Wilson-Cowan type. As well as sustained oscillations the model may show bursting when pink noise (1/f) is added. Using excitatory- inhibitory pair and setting the parameter values close to the edge between stationary and oscillatory behavior noise can perturb the system into the oscillatory state and we can observe high amplitude activity bursts [60]
	360
	361	A recent approach implements inferring excitation/inhibition balance from resting-state MEG PD data ON and OFF medication and healthy controls using 1/f (pink noise) dynamics of aperiodic activity with Welch’s and FOOOF methods. 44 sources were labeled using the human connectome project mm1 atlas and estimated brain-wide distribution of 1/f exponent characteristic with a mean over time. Perspectives are the usage of 1/f exponent dynamics as a biomarker and a combination of data analysis and modeling as E-I balance is important for neuronal network models.(Helson, 2022) (ongoing work)
	362
	363	Multiscale co-simulation using TVB-NEST/Anarchy could provide knowledge on how electrical stimulus can perturb synchrony of spiking neuronal populations and shift basal ganglia in an asynchronous state and help to fine-tune DBS protocols.
	364
	365	3.4.7 Challenges
	366
	367	* BIDS Manager provided by EBRAINS workflows do not support MEG and only on Windows.
	368	* BIDS is not yet interoperable with openMINDS.(ongoing work)
	369	* There is no standardized electronic data capture that could help with data annotation for BIDS and curation.(ongoing work)
	370	* (((
	371	Finding appropriate tools for model fitting
	372
	373	3.4.8 Workflow
	374	)))
	375
	376	* Organization of raw data and metadata into BIDS MEG.
	377	* Integration of metadata according to openMINDS to connect it to EBRAINS Knowledge Graph
	378	* Integrate data and metadata to be compatible with The Virtual Brain with the help of The Virtual Brain Ontology.
	379	* Construction and fitting the multiscale model using for example TVB-NEST and Simulation of resting state beta burst dynamic and task-related beta rebound affected by PD
	380	* Model validation For literature see: [55, 60]
	381
	382	(% style="text-align: center;" %)
	383	3.5. Workflow 5
	384
	385	3.5.1 Team
	386
	387	The team I’m working with at the moment is the Gerardo Biella’s team at the University of Pavia. Among the team’s research projects one of them is studying the properties and the modulation of neurons in the perirhinal cortex. But since in this period they are mainly focussed in other projects and they don’t have funds for a PhD, in the following months I’ll find another lab where I’ll be able to carry on this project.
	388
	389
	390	Figure 5: PD-MEG-Data-Modeling workflow
	391
	392	The techniques adopted in the laboratory are based on electrophysiological approaches, in particular, patch-clamp recordings performed in mouse brain slices. This is the main and most common technique in order to collect electrophysiological information about the single neurons that constitute the network of the cortical area of our interest. In order to collect also some anatomical data we perform confocal microscopy acquisition of the electrophysiologically recorded cells.
	393
	394	3.5.2 Background
	395
13.2	396	The main research question I’m working on is to understand the neural mechanisms underlying the functions carried out by the enthorinal cortex (e.g. spatial navigation, time coding of events) and perirhinal cortex (e.g. multisensory integration, information gating towards hippocampus during memory formation). What I want to understand is how the heterogeneous populations of neurons (mainly from an electrophysiological perspective, but it could be also extended to a molecular point of view) located in those areas orchestrate in order to create structures that can perform the previously mentioned functions. In other words, how each of those neurons with its properties and its connections takes part to the process. In literature are reported a wealth of experimental data describing the electrophysiological behavior of the different neural populations, their modulation by different neurotransmitters and their anatomical connections. These experimental data are collected thanks to electrophysiological recordings as patch-clamp recordings performed at a single neuron resolution in mouse/rat brain slices or extracellular recordings performed both in vivo and in brain slices. Other experiments commonly conducted are imaging experiments as confocal microscopy acquisitions and calcium imaging microscopy performed both in vivo and in brain slices. At last, some experiments involved in assessing the contribution of a certain neurotransmitter or a specific neural population are performed with behavioral tests. But what is missing is a clear vision on how all those aspects at a single neuron level contribute to the dynamics at a network level. What could help to achieve this wide perspective of the neural dynamics is a neural network that as a scaffold gathers all the experimental evidences at a single neuron level. That could be a key point to make a step forward in understanding how the brain performs those functions and at same point this kind of network could be a useful resource both for the design of new experiments and the development of new paradigms for bio-inspired machine learning and neuromorphic architectures. At the moment different projects aimed in developing neural networks are going on but none of them involves the previously mentioned cortical structures and that’s the reason why I’m interested in design and carry on this workflow. Similar projects carried on by the Human Brain Project can support the feasibility and the potentials of the project I’m proposing, in particular, I’m referring to all the projects focused to model the cerebellum (as the ones conducted by the Egido D’Angelo’s Lab at the University of Pavia). That projects proved to be very useful to make some step forward in better understanding the cerebellar functions and also develop new neurorobotic prototypes.
5.1	397
	398	3.5.3 Problem
	399
	400	The projects require to face three main issues:
	401
	402	1. Gathering anatomical information concerning the distribution of the different neural population and their wiring
	403	1. Obtaining computational models of each neuronal type that can be enough reliable to integrate the different processing properties of all its compartments, but at the same time enough light weighted to be implemented in a neural network with a conspicuous number of neurons. As a following step combine them to create a neural network model
	404	1. Gathering additional experimental data for the network model validation
	405
	406	The first point will require the following daily activities:
	407
	408	* Gathering the already published anatomical/histological data from articles and atlases
	409	* (((
	410	Performing additional sets of experiments and analysis to refine the data we already possess. (And subsequently sharing them with the community)
	411
	412	The second point will require the following daily activities:
	413	)))
	414
	415	* Gathering the already published electrophysiological data from articles and databases
	416	* Perform additional electrophysiological experiments and analysis to refine the data we already possess
	417	* Update the multicompartmental single neuron models already at our disposal with the experimental data we obtained. As final step it will be necessary to create a simplified version of this multicompartmental models
	418	* (((
	419	Create a neural network that integrates all the anatomical data and the updated single neuron models of the previous points
	420
	421	The third point will require the following daily activities:
	422
13.2	423	• Collect and analyze experimental data (electrophysioloogical, behavioral, imaging data) that can be used to validate the network previously built
5.1	424
	425	3.5.4 Vision
	426
13.2	427	What I would like to achieve with the future projects I’ll be working on is to develop a model of perirhinal and entorhinal cortex that could let us visualize at a detailed cellular level the neural dynamics that are going on in those area both at rest and during specific tasks. To a far extent what I would like to achieve is to have a clear vision on how these networks of single ”computational” units brings to the emergence of the complex behaviors we experience in our daily lives in order to achieve a complete reductionist description of those phenomena
5.1	428
	429	3.5.5 Impact
	430
	431	The main contribution of this project would be the advancement in understanding the mechanisms underlying processes as memory formation/ multisensory integration (and so the representation of the surrounding world)/ spatial navigation and time perception. But along with this theoretical achievement there would be also interesting practical contributions. The network models developed during this project could be used in other fields, for example in neurorobotics or in the development of new information processing paradigms that could be applied in machine learning algorithms or neuromorphic computing architectures. This kind of predictions can be considered feasible achievements since some neurorobotic projects already carried on by the Human Brain Project could benefit from this new type of network. In fact, some of these projects are developing robots able to perform multisensory integration tasks but at the moment they are relying on network based on abstract architectures. A network more similar to the real neural network of the cortical area involved in that task might lead to an optimization of the task performed by the robots
	432	)))
	433
	434	3.5.6 Solution elements
	435
	436	Some solutions to the challenges and problems we’ll have to face could be:
	437
	438	Networking, data sharing and collaboration: To achieve the objective of a clear vision of the network dynamics of the previously mentioned cortical areas, the effort of different groups will be required. The contribution of experimental and computational groups is needed and to make it possible networking and collaboration are key points of the project. For this reason, tools as Notebooks, Live Papers and Knowledge Graphs will provide a useful help for data and knowledge sharing. The EBRAINS community as long as it will be active and it will expand/interact with other groups and communities can be a great resource for sharing and develop new collaborations.
	439
	440	Analysis optimization: something that could help not only to speed up the advancement in this specific project but experimental research in general would be the optimization of the analysis tools. Based on my personal experience the analysis of electrophysiological data still involves time-consuming steps that might be optimized. In order to achieve this goal I think it might be useful relying more in open access tools as the ones offered in the EBRAINS platform. Thanks to those tools and the opportunity to collaborate with the EBRAINS group and the other members of the community it would be possible to develop better performing tools.
	441
13.2	442	Data similarity/Quantitative approximation: Using data from already better described areas might be a solution to cope with the lack of some experimental data during the early stages of the project and preventing the risk of delays in the development of computational models. For example, after appropriate evaluations and metanalysis some electrophysiological data describing hippocampal features shared with the neurons in the area of my interest might be used as preliminary data for the single neuron model development. In this way both the computational flow and the experimental flow can advance at the same time. Once the quantitative data are collected by the experimental flow they will the replace the ones that were used in the early stages.
5.1	443
	444	Iterative workflow and intermediate deliverables achievement: Since the project can’t be carried out as a single huge project, in order to be sustainable an iterative step workflow design might be the key to achieve the final result. So instead of collecting all the experimental results and input them in the workflow all at once, a good solution might be input the experimental data concerning some specific feature and at the end of the process release an updated version of the neural network implementing some additional feature in comparison to the previous one and then repeat the process. For example, in the first cycle I would like to implement the ”computational” operations performed by the dendrites and the spatial distribution of the different neural populations. So I’ll achieve a first updated network and I could assess its contribution in improving performances in neurorobotics and ML algorithms and in better describing the activity in those cortical areas. Then in a second cycle I could repeat the process in order to implement in the network additional information concerning the synaptic connectivity of the network. On a third cycle I could implement additional data concerning the modulation of certain neurons by a certain neurotransmitter.
	445
	446	Model optimization: Since using detailed multicompartmental models as building blocks for the creation of a neural network leads to a model too heavy to be simulated in a reasonable amount of time, the development of an intermediate/simplified multicompartmental model can be a useful step in the workflow. For example, replacing detailed dendrites made of various compartment with a single node/compartment that summarize the signal computation exerted by dendrites. Another optimization that might be done is a code optimization, for example writing the code in a manner that could exploit more the computational performances of GPUs (e.g. by using NVIDIA GPU optimized libraries or the GeNN library for GPU enhanced Neural Networks) HPC and Neuromorhpic resourses: Another solution to the computational complexity and weight issues is the opportunity to use resources as Super Computers and Neuromorphic platforms to perform simulations of high number of neurons in a reasonable amount of time.
	447
	448	3.5.7 Challenges
	449
	450	The challenges that will be faced during this phase will be:
	451
13.2	452	* Lack of some specific experimental data. As I mentioned in the background section there is already a large amount of data describing the behaviors and connections of the populations in my regions of interest, but there are still some aspects that require additional quantitative data in order to develop a model that reliably implements those features
5.1	453	* This aspect brings two additional challenges that must be faced, time consuming processing during data analysis and the risk of some bottle-necks in the process. The necessity to collect additional experimental data might cause a delay in the progression of the computational models development
13.2	454	* Developing a neural network made of detailed neurons require a considerable amount of computational resources.
5.1	455	* (((
	456	Time and Money. Not as technical as the previous challenges but as important as the other are two issues that every project has to face: the time required to advance across all the steps and achieve the final goal, and the the necessity of fundings to keep the workflow going on
	457
	458	3.5.8 Workflow
	459
	460	EBRAINS Tools that could be interesting for you:
	461
	462	Data and Models: https:~/~/neuromorpho.org/ provides cytological data of the entorhinal cortex Model Catalog: https:~/~/model-catalog.brainsimulation.eu/docs/
	463	)))
	464
	465	Data analysis:-)NeuroFeatureExtract (https:~/~/ebrains-cls-interactive.github.io/) (Electrophysiological data)
	466
	467	* INutil and VisuAlign (QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain - HBP Wiki (ebrains.eu) (Histological data)
	468	* Ilastik (ilastik - Animal Tracking) (Behavioural data) Model creation and Simulation SNUDDA (https:~/~/github.com/Hjorthmedh/Snudda) BRAINS Scaffold Builder (https:~/~/github.com/dbbs- lab/bsb) Single Cell In SilicoExperiment Tool (BlueNaaS) (https:~/~/blue-naas-bsp-epfl.apps.hbp.eu/#/)
	469	* Hippounit (GitHub - KaliLab/hippounit) (optimization of the model parameters)
	470	* BluepyOpt (GitHub - BlueBrain/BluePyOpt: Blue Brain Python Optimisation Library) (opti- mization of the model parameters)
	471
	472	(% style="text-align: center;" %)
	473	3.6. Workflow 6
	474
	475	3.6.1 Team
	476
	477	* Katia Djerround, Bachelor in Biochemistry and Master in Neurobiology.
13.2	478	* Nathaniel Adibuer, research and teaching assistant at the University of Ghana, Biomedical Engineering department
5.1	479	* (((
	480	Aziz Ullah Khan: professional Engineer, Master’s degree in Electrical Engineering
	481
	482	3.6.2 Background
	483
13.2	484	In the recent years, study of the BCI (Brain Computer Interface) technology based on EEG (Electroencephalography) has become one of the important parts in the biomedicine engineering. With this technique, people can control equipment or do very basic communication through their brains instead of language or physical actions [61] Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes [62]. They are translated in real-time into commands that operate a computer display or other device.
5.1	485	)))
	486	* (((
	487	Figure 6: Enthorial cortex simulation model
	488	)))
	489
	490	EEG is not the only non-invasive technique to study the brain fMRI, CT, PET, MEG and FNIRS are commonly used. As for the brain stimulation techniques, we can site the rTMS, CES, tDCS, tRNS and the RINCE. [63]
	491
	492	3.6.3 Problem
	493
13.2	494	• Data collection: finding a place in which the sensitive data can be stored in a systematic way • Creating a data processing pipeline that fullfills the following criteria:
5.1	495
	496	– It can be applied to raw data of different experimental conditions and data formats – Allows to include various analysis techniques (such as AI) in a modular way
	497	– Allows to compare the results of different imaging modalities
	498
	499	• Creating a setup that allows to test, if the obtained knowledge (e.g. a trained AI) can improve BCI. Here probably a simulated brain-machine setting would be the first step to go
	500
	501	3.6.4 Vision
	502
	503	We want to contribute to the further development of BCIs by facilitating the research on understanding in how far internal representations can be accessed by brain imaging techniques. For that goal we want to develop an open-source library, in which data of different experiences are sorted according to different categories (such as experiment type, imaging modality, research outcome, further option of
	504
	505	18
	506
	507	how to process the data). Of course, also by making use of the library for our own research, we hope that we will identify mechanisms that will not help only BCIs but also brain-to-brain communication and of course also help to understand the brain better.
	508
	509	3.6.5 Impact
	510
	511	The differences which HBP will make within our project are:
	512
	513	* First you permitted us to create a network where every participant is adding up precious information
	514	* Secondly using the EBRAINS compound which will help us in working on developing the project.
	515	* (((
	516	Surely we shouldn’t forget about the unparalleled opportunity to learn and expand our perspectives as scientists.
	517
	518	Who else will benefit from the designed workflow? All scientists which are interested in:
	519	)))
	520
	521	* Understanding the hidden layers and functioning of the brain
	522	* Controlling engines ( robots/devices) by ”the power of the force” = brainwaves
	523	* Programming
	524	* Analyzing data
	525	* Biologist/ doctors which would like to work on regeneration
	526	* Teachers and students, can use the data for modelling.
	527
	528	3.6.6 Solution elements
	529
	530	* Creating a team with multidisciplinary background
	531	* Collaborating with universities, hospitals and laboratories which use these imaging devices for their higher accuracy
	532	* Identifying suitable tools for data processing, analysis and simulation
	533	* Developing procedures to handle different data formats
	534	* (((
	535	Using/creating a program which can regulate and validate the data obtained as the library will be open source
	536
	537	3.6.7 Challenges
	538
	539	The challenges that we encountered are as follow:
	540	)))
	541
	542	1. An infrastructure to store and process all the data which we will collect from the experiments and the databases from all over the Europe (if possible world) which mean also capable of handling the different countries data regulation. Here the Human health data cloud (https:~/~/www.healthdatacloud.eu/) or future infrastructures would help
	543	1. A consortium of experts that can validate the data (if possible this process should be automated. Here the EBRAINS Community space can help to find such experts.
	544	1. Appropriate data analysis tools. Here the following tools could help: Frites, Neo, TVB
	545
	546	3.6.8 Workflow
	547
	548	The workflow is divided into different steps in order to achieve the following goals:
	549
	550	~1. Creating a data base of imaging data either obtained in a BCI task or in tasks that required motor output (starting first with EEG data); Attached to this data base are data analysis tools that allow to analyse the data in a straight forward way.
	551
	552	19
	553
	554	1. Developing a virtual robotic environment that allow to test in how far this data can be used to steer a robot
	555	1. Substituting the data used in step 2) through simulations. Investigating in how far data properties can be reproduced via simulation. And, investigating which features of the data are crucial for steering the robot
	556	1. (((
	557	Expanding 1-3) towards new data modalities. Hereby the simulation framework in step 3) can help understanding in how far the different image modalities are needed to fine tune BCI tasks or are redundant and do not add further knowledge.
	558
	559	Figure 7: BCI–EEG–framwork workflow
	560	)))
	561
	562	4 Discussion
	563
	564	* summary: in this white paper we wanted to provide an overview of example workflow of different levels of activities which leverage the EBRAINS infrastructure to do innovative reasearch (of course, some more sentences of what we did, workflows covering areas a,b,c
	565	* short paragraph about the workflows
	566	* focussing on communalites of the workflows (data manipulation, integration of modelling cycles, producing some meaning output that can be shared with community, and other meta patterns
13.2	567	* impact of this work: highlighting the importance of early career researchers on the codesign of the research infrastructures, we would like to invite more early career researchers to integrate this methodologies of stating their scientific journeys in which they hopefully make use of EBRAINS tools and shape it, also highlight the template, having a methodology in a clear way is a path towards a common language between disciplines and also to open science and to have more standardized solution that are reproducible and more robust
	568	* education related to brain-sciences would benefit from integrating these type of concept and best practices, making visible to the students to the platforms available in science, students can integrate them in their science
	569	* PIs should encourage the usage of platforms, in order to really make the best out of the European Commission to build this infrastructure the research groups should encourage the usage of these methods/tools/standards and even become part of the developer community which also helps for the long-term stability of the infrastructure itself as a community effort
5.1	570
13.2	571	• short comings: in these use-cases we have only covered a subset of the capabilities of EBRAINS and the community is invited to explore/ extent these use-cases with different tools and services inside and outside of the EBRAINS platform; there are other tools, services and platforms designed to research of the brain (e.g open brain, Patraig Gleeson) we have not made an explicit comparison with the possibilities the platform offers, however these platforms can also host tools and services from EBRAINS; most of use-cases are in the initial state, ideally the science will be implemented later; not all use-cases covered in EBRAINS
5.1	572
13.2	573	z outlook at some point it would be interesting to have the possibility to derive the connection between the tools, not only in a diagram but with automatic links provided by the infrastructure; share the experience of the use-cases and use this knowledge to improve the infrastructure; tracking system would be nice also to guarantee a more intense collaboration
5.1	574
	575	(% style="text-align: center;" %)
	576	==== 5 References ====
	577
	578	Future work
	579
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	596	1. (((
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	598
	599	KaliLab/hippounit.
	600	)))
	601	1. (((
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	603
	604	2022]. url: https:~/~/hbp-validation-client.readthedocs.io/en/master/.
	605	)))
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	607
	608	21
	609
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	611	1. (((
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	613
	614	~/~/neuroscheme-documentation.readthedocs.io/en/latest/.
	615	)))
	616	1. (((
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	618
	619	~/~/neurotessmesh-documentation.readthedocs.io/en/latest/.
	620	)))
	621	1. (((
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	623
	624	io/en/latest/.
	625	)))
	626	1. (((
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	628
	629	erebral EEG data. [Online; accessed 22-November-2022]. url: https:~/~/www.humanbrainproject.
	630
	631	eu/en/medicine/human-intracerebral-eeg-platform/.
	632	)))
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Wiki source code of Developing workflows on the EBRAINS research infrastructure – an early career researchers’ perspective.

EBRAINS Scientific Liaison Unit (SLU)