This lecture contains an overview of the China-Cuba-Canada neuroinformatics ecosystem for Quantitative Tomographic EEG Analysis (qEEGt).

In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks. 

This video will document the process of launching a Jupyter Notebook for group-level analyses directly from brainlife.

This lesson consists of a talk about the history and future of academic publishing and the need for transparency, as well as a live demo of an alpha version of NeuroLibre, a preprint server that goes beyond the PDF to complement research articles. This video was part of a virutal QBIN SciComm seminar.

This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below. 

This is an introductory lecture on whole-brain modelling, delving into the various spatial scales of neuroscience, neural population models, and whole-brain modelling. Additionally, the clinical applications of building and testing such models are characterized. 

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below. 

This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling. 

Similarity Network Fusion (SNF) is a computational method for data integration across various kinds of measurements, aimed at taking advantage of the common as well as complementary information in different data types. This workshop walks participants through running SNF on EEG and genomic data using RStudio.

In this lesson, you will learn about one particular aspect of decision making: reaction times. In other words, how long does it take to take a decision based on a stream of information arriving continuously over time?

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture covers an Introduction to neuron anatomy and signaling, as well as different types of models, including the Hodgkin-Huxley model.

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

This lecture provides an introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.

This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.

This lesson gives an introductory presentation on how data science can help with scientific reproducibility.

This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research.