Tutorials & E-Library

This video provides a comprehensive tour of the simulation tools and services available in EBRAINS, a cutting-edge platform for brain research. It covers the subcellular scale, where biomolecular simulations and model fitting tool chains are used to investigate complex biological processes. The video also explores the cellular scale, where reaction networks and spatial volume simulations are employed to understand the behavior of cells and tissues. Furthermore, the video highlights the importance of combining these tools to address complex research questions and the need for standards in neuroscience and systems biology communities.

This tutorial explores the application of algorithms and artificial intelligence in epilepsy and electroencephalography (EEG). The speaker presents two examples of using AI models to detect seizures, one utilising surface electromyography (EMG) and the other using heart rate signals. The EMG-based algorithm achieves high sensitivity and accuracy in detecting generalized tonic-clonic seizures, while the heart rate-based algorithm detects seizures characterised by a marked change in heart rate. The speaker also highlights the importance of reducing the treatment gap in epilepsy and the potential of AI to provide expertise in areas where specialists are scarce, leveraging its ability to analyse large datasets and identify patterns.

The QUINT workflow takes you through a series of steps enabling researchers to quantify and analyse labelled features in series of histological section images from rodent brain using a reference brain atlas. The tutorial demonstrates the use of the QuickNII and VisuAlign software to perform registration to an atlas; the use of the ilastik software for identifying the features to be quantified; and the use of the Nutil software for performing quantification per atlas region. This combination of steps facilitates semi-automated quantification, eliminating the need for more time-consuming methods, such as stereological analysis with manual delineation of brain regions. More on https://ebrains.eu/service/quint/

This lecture, the first of two parts, explores the factors that drive successful innovation and research excellence. It highlights the significance of local talent density and diversity, alongside a supportive ecosystem. Using the Biomed X Innovation Center in Heidelberg as an example, it showcases the benefits of interdisciplinary collaboration, secure funding, and mentorship from both academia and industry. Participants enjoy autonomy in research, freedom to publish, and leadership training, fostering career flexibility. The discussion emphasizes combining outstanding science with practical application, creating an environment where top talent thrives and innovation flourishes.

In this example, we will set up a single dendrite represented by a passive cable. On one end the dendrite is stimulated by a short current pulse. We will investigate how this pulse travels along the dendrite and calculate the conduction velocity.

This learning resource explores the key factors involved in the successful exploitation of research results and technology transfer. It covers the fundamentals of exploitation, including understanding what it means, when and why to protect research results, and the process of transferring these results to relevant stakeholders. The session highlights the various factors that influence the effective exploitation of research, focusing on how to maximize impact and value. The resource concludes with a summary of key takeaways and practical considerations for applying these concepts.

In this notebook we will look more closely at the EBRAINS dataset "[Excitability of mouse hippocampal CA1 pyramidal neurons during sustained (500 ms) strong depolarization (v1)](https://search.kg.ebrains.eu/#07554ebd-95a2-46f0-8065-d961d56ce098)", contributed by Machhindra Garad while at Otto-von-Guericke University, Magdeburg, Germany and Volkmar Lessmann from the Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany.

As we can see from the dataset description,

<i>This dataset provides an analysis of electrophysiological feature often observed in recordings from mouse CA1 pyramidal cells that has so far been ignored by experimentalists and modelers. It consists of a large and dynamic increase in the depolarization baseline (i.e., the minimum value of the membrane potential between successive action potentials during a sustained depolarizing input) in response to strong somatic current injections.</i>
More information is available in the [Data Descriptor](https://search.kg.ebrains.eu/instances/07554ebd-95a2-46f0-8065-d961d56ce098).

This dataset forms part of the results reported in:

Bianchi, D., Migliore, R., Vitale, P., Garad, M., Pousinha, P. A., Marie, H., Lessmann, V., & Migliore, M. (2022). Membrane electrical properties of mouse hippocampal CA1 pyramidal neurons during strong inputs. *Biophysical Journal*, **121**(4), 644–657. https://doi.org/10.1016/j.bpj.2022.01.002

In this notebook we will demonstrate how to access the data files and the metadata from this study, and how these data can be analysed and visualised, with the goal of reproducing some of the figures from the article.

Brainstorm is a collaborative, open-source application dedicated to MEG/EEG/sEEG/ECoG data analysis (visualization, processing and advanced source modeling).

Our objective is to share a comprehensive set of user-friendly tools with the scientific community using MEG/EEG as an experimental technique. For physicians and researchers, the main advantage of Brainstorm is its rich and intuitive graphic interface, which does not require any programming knowledge. We are also putting the emphasis on practical aspects of data analysis (e.g., with scripting for batch analysis and intuitive design of analysis pipelines) to promote reproducibility and productivity in MEG/EEG research. Finally, although Brainstorm is developed with Matlab (and Java), it does not require users to own a Matlab license: an executable, platform-independent (Windows, MacOS, Linux) version is made available in the downloadable package.

Since the project started by the end of the 1990's, our server has registered more than 12,000 accounts. See our reference page for a list of published studies featuring Brainstorm at work.

http://neuroimage.usc.edu/brainstorm

A hands-on Jupyter notebook on finding and reusing EBRAINS research products with the fairgraph Python library. It covers what the Knowledge Graph contains and how its nodes and typed links are represented as Python objects; retrieving a node by identifier, by name, or by filtering on its properties, including filters that reach across a link; following links efficiently, and why the starting point of a query determines what it costs; controlled terms and the vocabularies that make metadata comparable; and answering one scientific question across datasets, models and software at once. Later sections, for working through afterwards, cover local JSON-LD snapshots with Collection, writing custom queries, release status, spaces and result ordering, and error handling. The notebook only reads from the Knowledge Graph; nothing is saved. A companion notebook covers creating metadata, for data providers.

A hands-on Jupyter notebook on creating and updating metadata in the EBRAINS Knowledge Graph with the fairgraph Python library. It covers building openMINDS nodes in Python and validating them locally, how fairgraph decides between creating a node and updating an existing one, assembling a complete dataset record, collab spaces and why metadata in a private space can never be curated, the curation workflow and what only EBRAINS curators can do, and bulk upload with Collection. Most data providers describe their datasets through the Metadata Wizard instead; this tutorial is for those with many datasets to register, or who want to generate metadata from a pipeline. All exercises write to the pre-production Knowledge Graph, so nothing reaches the production KG. A companion notebook covers reading metadata, for data consumers.

This tutorial is a parenthesis to explain how the figures are positioned on the screen and how you can organize your workspace more efficiently. One interesting feature of the Brainstorm interface is the ability to open easily multiple views or multiple datasets simultaneously.

This tutorial provides a comprehensive overview of event markers (also referred to as triggers or markers) within MEG recordings, focusing on their identification, manipulation, and management in Brainstorm software. Event markers denote key points in the data, including stimulus presentations and subject responses. Users learn how to visualize, add, edit, and remove events, including simple time-point markers and extended time segments, such as bad data intervals caused by artifacts. Advanced functionalities are also covered, including channel-specific events, event annotations, display customization, keyboard shortcuts, and event group management. The tutorial highlights best practices for marking noisy segments as bad to improve analysis quality and explains how event data are saved separately from raw recordings. Overall, it equips users with practical skills to efficiently handle event data in MEG experiments for enhanced data inspection and processing.