Tutorials & E-Library

This tutorial introduces the toolbox BEst – "Brain Entropy in space and time" that implements several EEG/MEG source localization techniques within the “Maximum Entropy on the Mean (MEM)” framework. These methods are particularly dedicated to estimate accurately the source of EEG/MEG generators together with their spatial extent along the cortical surface. Assessing the spatial extent of the sources might be very important in some application context, and notably when localizing spontaneous epileptic discharges. We also proposed two other extensions of the MEM framework within the time frequency domain dedicated to localize oscillatory patterns in specific frequency bands and synchronous sources.

The goal of these introduction tutorials is to guide you through most of the features of the software. All the pages use the same example dataset. The results of one section are most of the time used in the following section, so read these pages in the correct order.

Some pages may contain too many details for your level of interest or competence. The sections marked as [Advanced] are not required for you to follow the tutorials until the end. You can skip them the first time you go through the documentation. You will be able to get back to the theory later if you need.

Please follow first these tutorials with the data we provide. This way you will be able to focus on learning how to use the software. It is better to start with some data that is easy to analyze. After going through all the tutorials, you should be comfortable enough with the software to start analyzing your own recordings.

You will observe minor differences between the screen captures presented in these pages and what you obtain on your computer: different colormaps, different values, etc. The software being constantly improved, some results changed since we produced the illustrations. When the changes are minor and the interpretation of the figures remain the same, we don't necessarily update the images in the tutorial.

If you are interested only in EEG or intra-cranial recordings, don't think that a MEG-based tutorial is not adapted for you. Most of the practical aspects of the data manipulation is very similar in EEG and MEG. First start by reading these introduction tutorials using the MEG example dataset provided, then when you are familiar with the software, go through the tutorial "EEG and Epilepsy" to get some more details about the processing steps that are specific for EEG, or read one of the SEEG/ECOG tutorials available in the section "Other analysis scenarios".

Introduction to the BrainScaleS Tutorial

Brain-inspired Computing - An Introduction into Accelerated Analog Neuromorphic Computing with BrainScaleS.

A video tutorial explaining the BIDS Manager & Pipeline, a tool that allows various users to easily import and explore databases in BIDS format.
Manually driven processes for data storing can lead to human errors, which cannot be tolerated in the context of a research/clinical datasets. BIDS manager offers a secure system to import and structure subject and patient datasets.

BIDS MANAGER aims to achieve the following objectives:

• Provide a software for clinicians and researchers with a user-friendly interface, • Define the appropriate directory for the dataset corresponding to a study,
• Select required data,
• Select the data to be imported,
• Provide a monitoring and reporting system for data importation and storage.

ViSimpl provides 3D particle-based rendering that allows
visualizing simulation data with their associated spatial and temporal information, enhancing the knowledge extraction process. It also provides abstract representations of the time-varying magnitudes supporting different data aggregation and disaggregation operations and giving also focus and context
clues. In addition, ViSimpl tools provide synchronized playback control of the simulation being analyzed. Finally, ViSimpl allows performing selection and filtering operations relying on an application called NeuroScheme. All these views are loosely coupled and can be used independently, but they can also work
together as linked views, both in centralized and distributed computing environments, enhancing the data exploration and analysis procedures.

In case you have data that have already been spike-sorted, you can use the spike-timestamps as separate events for each electrode and import the events from a single events file for your analysis. Brainstorm needs its events to be in a specific format, so they can be imported. Moreover, the functions that use spike-events, will use those events only if you label the events properly and construct the events in the appropriate format.

This tutorial introduces some concepts that are specific to the management of EEG recordings in the Brainstorm environment. It also describes a standard pipeline for analyzing epilepsy recordings. It is based on a clinical case from the Epilepsy Center at the University Hospital of Freiburg, Germany. The anonymized dataset can be downloaded directly from the Brainstorm download page.

The QUINT workflow takes you through a series of steps developed to enable researchers to quantify and analyse labelled features in a series of histological images of rodent brain sections within a known atlas space. The QuickNII and VisuAlign software are used for precise image registration; ilastik allows efficient identification of labelled objects; finally, the Nutil tool enables quantification of features 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

The Innovation Funding Workshop took place on March 13th, 2023, it presented the landscape of European funding instruments available for neurotech start-ups. A one-hour workshop to discover both public and private funding opportunities designed to bring HBP developments to the market and maximize their impact.

In this notebook we will look more closely at the EBRAINS dataset "[Excitability profile of CA1 pyramidal neurons in APPPS1 Alzheimer disease mice and control littermates](https://search.kg.ebrains.eu/instances/bd5f91ff-e829-4b85-92eb-fc56991541f1)", contributed by Ana Rita Salgueiro-Pereira and Hélène Marie from the Université Côte d’Azur in Valbonne, France.

As we can see from the dataset description,

This dataset provides an analysis of the intrinsic electrophysiological properties of CA1 excitatory hippocampal neurons in a mouse model of Alzheimer’s Disease (AD) at two age points: a presymptomatic age (3-4 months) and a symptomatic age: (9-10 months). More information is available in the Data Descriptor.

This dataset forms part of the results reported in Vitale, P., Salgueiro-Pereira, A. R., Lupascu, C. A., Willem, M., Migliore, R., Migliore, M., & Marie, H.(2021) Analysis of Age-Dependent Alterations in Excitability Properties of CA1 Pyramidal Neurons in an APPPS1 Model of Alzheimer’s Disease. Frontiers in Aging Neuroscience 13 [doi:10.3389/fnagi.2021.668948](https://doi.org/10.3389/fnagi.2021.668948)

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.

PyNN is a simulator-independent language for building neuronal network models. You can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON, NEST, and Brian 2) and on several neuromorphic hardware systems.

This tutorial is intended for people with at least a basic knowledge of neuroscience (high-school level or above) and basic familiarity with the Python programming language. It should also be helpful for people who already have advanced knowledge of neuroscience and neural simulation, who simply wish to learn how to use PyNN and how it differs from other simulation tools they know.