This workshop delves into the need for, structure of, tools for, and use of hierarchical event descriptor (HED) annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. HED are a controlled vocabulary of terms describing events in a machine-actionable form so that algorithms can use the information without manual recoding.

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.

Sessions from the INCF Neuroinformatics Assembly 2022 day 2. 

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This course consists of three lessons, each corresponding to a lightning talk given at the first day of INCF's Neuroinformatics Assembly 2023. By following along these brief talks, you will hear about topics such as open source tools for computer vision, tools for the integration of various MRI dataset formats, as well as international data governance. 

This course provides a broad, non-technical overview of the field of neurotechnology. It is intended to provide users with a fundamental understanding of how neurotechnology works.

As technological improvements continue to facilitate innovations in the mental health space, researchers and clinicians are faced with novel opportunities and challenges regarding study design, diagnoses, treatments, and follow-up care. This course includes a lecture outlining these new developments, as well as a workshop which introduces users to Synapse, an open-source platform for collaborative data analysis. 

Sessions from the INCF Neuroinformatics Assembly 2022 day 1. 

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This course corresponds to the first session of talks given at INCF's Neuroinformatics Assembly 2023. The sessions consists of several lectures, focusing on using the principles of FAIR (findability, accessibility, interoperability, and reusability) to inform future directions in neuroscience and neuroinformatics. In particular, these talks deal with the development of knowledge graphs and ontologies. 

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.

Sessions from the INCF Neuroinformatics Assembly 2022 Day 3. 

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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. 

In this course we present the TVB-EBRAINS integrated workflows that have been developed in the Human Brain Project in the third funding phase (“SGA2”) in the Co-Design Project 8 “The Virtual Brain”. 

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.

As research methods and experimental technologies become ever more sophisticated, the amount of health-related data per individual which has become accessible is vast, giving rise to a corresponding need for cross-domain data integration, whole-person modelling, and improved precision medicine. This course provides lessons describing state of the art methods and repositories, as well as a tutorial on computational methods for data integration. 

The Neurodata Without Borders: Neurophysiology project (NWB, https://www.nwb.org/) is an effort to standardize the description and storage of neurophysiology data and metadata. NWB enables data sharing and reuse and reduces the energy-barrier to applying data analytics both within and across labs. Several laboratories, including the Allen Institute for Brain Science, have wholeheartedly adopted NWB.

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 consists of three lessons, each corresponding to a lightning talk given at the first day of INCF's Neuroinformatics Assembly 2023. By following along these brief talks, you will hear about topics such as open source tools for computer vision, tools for the integration of various MRI dataset formats, as well as international data governance. 

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.

EEGLAB is an interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data. In this course, you will learn about features incorporated into EEGLAB, including independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data. EEGLAB runs under Linux, Unix, Windows, and Mac OS X.

This course provides several visual walkthroughs documenting how to execute various processes in brainlife.io, an open-source, free and secure reproducible neuroscience analysis platform. The platform allows to analyze Magnetic Resonance Imaging (MRI), electroencephalography (EEG) and magnetoencephalography (MEG) data. Data can either be uploaded from local computers or imported from public archives such as OpenNeuro.org.