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Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Advanced
Duration: 50:28
Speaker: : Pierre Bellec
Difficulty level: Beginner
Duration: 6:10
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 15:10
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 2:49
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 6:10
Speaker: : MATLAB®

This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.

Difficulty level: Beginner
Duration: 6:27
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 3:55
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 3:52
Speaker: : MATLAB®

A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner
Duration: 1:16:36
Speaker: : Tal Yarkoni

This tutorial was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner
Duration: 1:21:40
Speaker: : Tal Yarkoni

This lecture will provide an overview of neuroimaging techniques and their clinical applications.

Difficulty level: Beginner
Duration: 45:29
Speaker: : Dafna Ben Bashat
Course:

BioImage Suite is an integrated image analysis software suite developed at Yale University. BioImage Suite has been extensively used at different labs at Yale since about 2001.

Difficulty level: Beginner
Duration: 01:47
Speaker: : BioImage Suite
Course:

Fibr is an app for quality control of diffusion MRI images from the Healthy Brain Network, a landmark mental health study that is collecting MRI images and other assessment data from 10,000 New York City area children. The purpose of the app is to train a computer algorithm to analyze the Healthy Brain Network dataset. By playing fibr, you are helping to teach the computer which images have sufficiently good quality and which images do not. 

Difficulty level: Beginner
Duration: 02:26
Speaker: : Ariel Rokem

This lecture provides guidance on the ethical considerations the clinical neuroimaging community faces when applying the FAIR principles to their research. This lecture was part of the FAIR approaches for neuroimaging research session at the 2020 INCF Assembly.

Difficulty level: Beginner
Duration: 13:11
Speaker: : Gustav Nilsonne

This module covers many of the types of non-invasive neurotech and neuroimaging devices including Electroencephalography (EEG), Electromyography (EMG), Electroneurography (ENG), Magnetoencephalography (MEG), functional Near-Infrared Spectroscopy (fNRIs), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography

Difficulty level: Beginner
Duration: 13:36
Speaker: : Harrison Canning

Demo of the BRIAN Simulator. BRIAN is a free, open source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms. We believe that a simulator should not only save the time of processors, but also the time of scientists. Brian is therefore designed to be easy to learn and use, highly flexible and easily extensible.

Difficulty level: Beginner
Duration: 1:27:32
Speaker: : Marcel Stimberg

NeuroFedora is a volunteer driven initiative to provide a ready to use Fedora based Free/Open Source Software platform for neuroscience. We believe that similar to Free Software, science should be free for all to use, share, modify, and study. The use of Free Software also aids reproducibility, data sharing, and collaboration in the research community. By making the tools used in the scientific process easier to use, NeuroFedora aims to take a step to enable this ideal. The CompNeuro Fedora Lab was specially to enable computational neuroscience. It includes everything you will need to get your work done—modelling software, analysis tools, general productivity tools—all well integrated with the modern GNOME platform to give you a complete operating system.

Difficulty level: Beginner
Duration: 1:06:08
Speaker: : Ankur Sinha

neurolib is a computational framework for simulating coupled neural mass models written in Python. It helps you to easily load structural brain scan data to construct brain networks where each node is a neural mass representing a single brain area. This network model can be used to simulate whole-brain dynamics. neurolib provides a simulation and optimization framework which allows you to easily implement your own neural mass model, simulate fMRI BOLD activity, analyse the results and fit your model to empirical data.

Difficulty level: Beginner
Duration: 1:06:53
Speaker: : Çağlar Çakan

GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users.

Difficulty level: Beginner
Duration: 59:00

This video gives a short introduction to the EBRAINS data sharing platform, why it was developed, and how it contributes to open data sharing.

Difficulty level: Beginner
Duration: 17:32
Speaker: : Ida Aasebø