In this short series of lectures, participants will take a look at articles using TVB in a clinical context. Specifically, participants will see how TVB can help to predict recovery after stroke and how individual epileptic seizures are simulated. The course lecturers will briefly describe the methods used and results achieved in the articles.

This course outlines how versioning code, data, and analysis software is crucially important to rigorous and open neuroscience workflows that maximize reproducibility and minimize errors.Version control systems, code-capable notebooks, and virtualization containers such as Git, Jupyter, and Docker, respectively, have become essential tools in data science.

This course contains videos, lectures, and hands-on tutorials as part of INCF's Neuroinformatics Assembly 2023 workshop on developing robust and reproducible research workflows to foster greater collaborative efforts in neuroscience.

This workshop hosted by HBP, EBRAINS, and the European Academy of Neurology (EAN) aimed to identify and openly discuss all issues and challenges associated with data sharing in Europe: from ethics to data safety and privacy including those specific to data federation such as the development and validation of federated algorithms. 

This lecture series is presented by NeuroTechEU, an alliance between eight European universities with the goal to build a trans-European network of excellence in brain research and technologies. By following along with this series, participants will learn about the history of cognitive science and the development of the field in a sociocultural context, as well as its trajectory into the future with the advent of artificial intelligence and neural network development.

This course includes both lectures and tutorials around the management and analysis of genomic data in clinical research and care. Participants are led through the basics of genome-wide association studies (GWAS), genotypes, and polygenic risk scores, as well as novel concepts and tools for more sophisticated consideration of population stratification in GWAS.

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

Neuroanatomy provides one of the unifying frameworks for neuroscience and thus it is not surprising that it provides the basis for many neuroinformatics tools and approaches.  Regardless of whether one is working at the subcellular, cellular or gross anatomical level or whether one is modeling circuitry, molecular pathways or function, at some point, this work will include an anatomical reference.

The emergence of data-intensive science creates a demand for neuroscience educators worldwide to deliver better neuroinformatics education and training in order to raise a generation of modern neuroscientists with FAIR capabilities, awareness of the value of standards and best practices, knowledge in dealing with big datasets, and the ability to integrate knowledge over multiple scales and methods.

Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience. FAIR defines a set of high level principles and practices for making digital objects, including data, software and workflows, Findable, Accessible, Interoperable and Reusable. But FAIR is not a specification; it leaves many of the specifics up to individual scientific disciplines to define.

This module covers the concept of associative memories in deep learning. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Introduction to Deep Learning (module 1 of the course), Parameter Sharing (module 2 of the course), 

In this course, you will learn about working with calcium-imaging data, including image processing to remove background "blur", identifying cells based on threshold spatial contiguity, time-series filtering, and principal component analysis (PCA). The MATLAB code shows data animations, capabilities of the image processing toolbox, and PCA.

The emergence of data-intensive science creates a demand for neuroscience educators worldwide to deliver better neuroinformatics education and training in order to raise a generation of modern neuroscientists with FAIR capabilities, awareness of the value of standards and best practices, knowledge in dealing with big datasets, and the ability to integrate knowledge over multiple scales and methods.

Notebook systems are proving invaluable to skill acquisition, research documentation, publication, and reproducibility.  This series of presentations introduces the most popular platform for computational notebooks, Project Jupyter, as well as other resources like Binder and NeuroLibre. 

This course provides a general overview about brain simulation, including its fundamentals as well as clinical applications in populations with stroke, neurodegeneration, epilepsy, and brain tumors. This course also introduces the mathematical framework of multi-scale brain modeling and its analysis.

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This session will introduce the IBI and the current efforts of the Data Standards and Sharing Working Group with a view to gain input from a wider neuroscience and neuroinformatics community. 

This workshop provides an opportunity to explore the advanced tools and techniques for data sharing, analysis, visualization, and simulation.

This course is intended to introduce researchers to the Open Science Framework (OSF). OSF is a free, open source web application built by the Center for Open Science, a non-profit dedicated to improving the alignment between scientific values and scientific practices. OSF is part collaboration tool, part version control software, and part data archive.

This course consists of a three-part session from the second day of INCF's Neuroinformatics Assembly 2023. The lessons describe various on-going efforts within the fields of neuroinformatics and clinical neuroscience to adjust to the increasingly vast volumes of brain data being collected and stored.

This course offers lectures on the origin and functional significance of certain electrophysiological signals in the brain, as well as a hands-on tutorial on how to simulate, statistically evaluate, and visualize such signals. Participants will learn the simulation of signals at different spatial scales, including single-cell (neuronal spiking) and global (EEG), and how these may serve as biomarkers in the evaluation of mental health data.