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This lecture provides a history of data management, recent developments data management, and a brief description of scientific data management.

Difficulty level: Advanced
Duration: 35:10
Speaker: : Thomas Heinis

This talk covers the Human Connectome Project, which aims to provide an unparalleled compilation of neural data, an interface to graphically navigate this data, and the opportunity to achieve never before realized conclusions about the living human brain.

Difficulty level: Advanced
Duration: 59:06
Speaker: : Jennifer Elam

This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

Difficulty level: Advanced
Duration: 50:28
Speaker: : Pierre Bellec

This lesson provides instructions on how to build and share extensions in NWB.

Difficulty level: Advanced
Duration: 20:29
Speaker: : Ryan Ly

Learn how to build custom APIs for extension.

Difficulty level: Advanced
Duration: 25:40
Speaker: : Andrew Tritt

This lesson provides instruction on advanced writing strategies in HDF5 that are accessible through PyNWB.

Difficulty level: Advanced
Duration: 23:00
Speaker: : Oliver Ruebel

This lesson provides a tutorial on how to handle writing very large data in MatNWB. 

Difficulty level: Advanced
Duration: 16:18
Speaker: : Ben Dichter

This lecture discusses the the importance and need for data sharing in clinical neuroscience.

Difficulty level: Intermediate
Duration: 25:22
Speaker: : Thomas Berger

This lecture gives insights into the Medical Informatics Platform's current and future data privacy model.

Difficulty level: Intermediate
Duration: 17:29
Speaker: : Yannis Ioannidis

This lecture gives an overview on the European Health Dataspace. 

Difficulty level: Intermediate
Duration: 26:33

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 is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

Difficulty level: Intermediate
Duration: 1:11:04
Speaker: : Etay Hay

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices. 

Difficulty level: Intermediate
Duration: 1:39:04

In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity. 

Difficulty level: Intermediate
Duration: 1:16:10
Speaker: : John Griffiths

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment. 

This lesson corresponds to slides 1-64 in the PDF below. 

Difficulty level: Intermediate
Duration: 1:28:14

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. 

Difficulty level: Intermediate
Duration: 1:15:04
Speaker: : Daniel Hauke

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 lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient. 

Difficulty level: Intermediate
Duration: 1:32:53

This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

Difficulty level: Intermediate
Duration: 33:41

This lesson briefly goes over the outline of the Neuroscience for Machine Learners course. 

Difficulty level: Intermediate
Duration: 3:05
Speaker: : Dan Goodman