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.

This course introduces researchers to the Brain Imaging Data Structure (BIDS), the official community standard for organizing and sharing PET data. BIDS simplifies collaboration, streamlines analysis, and ensures your research remains future-proof by enabling compatibility with an ever-growing ecosystem of open datasets and community-developed tools.

Given the extreme interconnectedness of the human brain, studying any one cerebral area in isolation may lead to spurious results or incomplete, if not problematic, interpretations. This course introduces participants to the various spatial scales of neuroscience and the fundamentals of whole-brain modelling, used to generate a more thorough picture of brain activity.

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. 

Bayesian inference (using prior knowledge to generate more accurate predictions about future events or outcomes) has become increasingly applied to the fields of neuroscience and neuroinformatics. In this course, participants are taught how Bayesian statistics may be used to build cognitive models of processes like learning or perception. This course also offers theoretical and practical instruction on dynamic causal modeling as applied to fMRI and EEG data.

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.

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.

Sessions from the INCF Neuroinformatics Assembly 2022 Day 3. 

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This module provides an introduction to the problem of speech recognition using neural models, emphasizing the CTC loss for training and inference when input and output sequences are of different lengths. It also covers beam search for use during inference, and how that procedure may be modeled at training time using a Graph Transformer Network.

A virtual workshop with lectures and hands-on tutorials that will teach participants how to use open-source Miniscopes for in vivo calcium imaging. This workshop is designed to introduce all aspects of using Miniscopes, including basic principles of Miniscope design and imaging, how to build and attach a Miniscope, how to implant a GRIN lens for imaging deep structures, and how to analyze imaging data.

A virtual workshop with lectures and hands-on tutorials that will teach participants how to use open-source Miniscopes for in vivo calcium imaging. This workshop is designed to introduce all aspects of using Miniscopes, including basic principles of Miniscope design and imaging, how to build and attach a Miniscope, how to implant a GRIN lens for imaging deep structures, and how to analyze imaging data.

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”. 

These courses give introductions and overviews of some of the major statistics software packages currently used in neuroscience research.

This course contains sessions from the first day of INCF's Neuroinformatics Assembly 2022.

This course is intended for those interested in electroencephalography (EEG) and event-related potentials (ERPs) techniques, and those interested in collecting, annotating, standardizing, storing, processing, sharing, and publishing data from electrical activity of the human brain.

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 workshop will include interactive seminars given by selected experts in the field covering all aspects of (FAIR) small animal MRI data acquisition, analysis, and sharing. The seminars will be followed by hands-on training where participants will perform use case scenarios using software established by the organizers. This will include an introduction to the basics of using command line interfaces, Python installation, working with Docker/Singularity containers, Datalad/Git, and BIDS.

Neurohackademy is a two-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute. Participants learn about technologies used to analyze human neuroscience data, and to make analyses and results shareable and reproducible.

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.

The dimensionality and size of datasets in many fields of neuroscience research require massively parallel computing power.  Fortunately, the maturity and accessibility of virtualization technologies has made it feasible to run the same analysis environments on platforms ranging from single laptop computers up to high-performance computing networks.