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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. 

Difficulty level: Intermediate
Duration: 1:24:44
Speaker: : John Griffiths

This lecture covers FAIR atlases, including their background and construction, as well as how they can be created in line with the FAIR principles.

Difficulty level: Beginner
Duration: 14:24
Speaker: : Heidi Kleven

This lesson discusses the need for and approaches to integrating data across the various temporal and spatial scales in which brain activity can be measured. 

Difficulty level: Beginner
Duration: 1:35:37

This lesson consists of lecture and tutorial components, focusing on resources and tools which facilitate multi-scale brain modeling and simulation. 

Difficulty level: Beginner
Duration: 3:46:21

In this talk, challenges of handling complex neuroscientific data are discussed, as well as tools and services for the annotation, organization, storage, and sharing of these data. 

Difficulty level: Beginner
Duration: 21:49
Speaker: : Thomas Wachtler

This lecture describes the neuroscience data respository G-Node Infrastructure (GIN), which provides platform independent data access and enables easy data publishing. 

Difficulty level: Beginner
Duration: 22:23
Speaker: : Michael Sonntag

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

Difficulty level: Intermediate
Duration: 1:09:33
Speaker: : Sean Hill

This lesson explains the fundamental principles of neuronal communication, such as neuronal spiking, membrane potentials, and cellular excitability, and how these electrophysiological features of the brain may be modelled and simulated digitally. 

Difficulty level: Intermediate
Duration: 1:20:42
Speaker: : Etay Hay

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health. 

Difficulty level: Intermediate
Duration: 1:47:22

This lecture covers the emergence of cognitive science after the Second World War as an interdisciplinary field for studying the mind, with influences from anthropology, cybernetics, and artificial intelligence.

Difficulty level: Beginner
Duration: 51:07

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

Difficulty level: Intermediate
Duration: 3:54
Speaker: : Dan Goodman

In this lesson you will learn about the motivation behind manipulating neural activity, and what forms that may take in various experimental designs. 

Difficulty level: Intermediate
Duration: 8:42
Speaker: : Marcus Ghosh

This lecture explains the concept of federated analysis in the context of medical data, associated challenges. The lecture also presents an example of hospital federations via the Medical Informatics Platform.

Difficulty level: Intermediate
Duration: 19:15
Speaker: : Yannis Ioannidis

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. 

Difficulty level: Beginner
Duration: 52:26

This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes. 

Difficulty level: Intermediate
Duration: 1:29:08

In this lecture, you will learn about current methods, approaches, and challenges to studying human neuroanatomy, particularly through the lense of neuroimaging data such as fMRI and diffusion tensor imaging (DTI). 

Difficulty level: Intermediate
Duration: 1:35:14
Speaker: : Matt Glasser

This lesson provides a thorough description of neuroimaging development over time, both conceptually and technologically. You will learn about the fundamentals of imaging techniques such as MRI and PET, as well as how the resultant data may be used to generate novel data visualization schemas. 

Difficulty level: Beginner
Duration: 1:43:57
Speaker: : Jack Van Horn

This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.

Difficulty level: Beginner
Duration: 54:58
Speaker: : Franco Pestilli

As the previous lesson of this course described how researchers acquire neural data, this lesson will discuss how to go about interpreting and analysing the data. 

Difficulty level: Intermediate
Duration: 9:24
Speaker: : Marcus Ghosh
Course:

This lesson gives a quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science, including the use of readr, dplyr, tidyr, and ggplot2.

Difficulty level: Beginner
Duration: 1:01:39
Speaker: : Thomas Mock