This is a tutorial introducing participants to the basics of RNA-sequencing data and how to analyze its features using Seurat.
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.
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.
This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein.
This lesson explores how researchers try to understand neural networks, particularly in the case of observing neural activity.
In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?
This tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.
The tutorial on modelling strokes in TVB includes a didactic video and jupyter notebooks (reproducing publication: Falcon et al. 2016 eNeuro).
This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research.
In this lesson, you will learn about the Python project Nipype, an open-source, community-developed initiative under the umbrella of NiPy. Nipype provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow.
This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and goes through both the motivations and processes involved in moving your research computing to the cloud.
This lesson introduces population models and the phase plane, and is part of the The Virtual Brain (TVB) Node 10 Series, a 4-day workshop dedicated to learning about the full brain simulation platform TVB, as well as brain imaging, brain simulation, personalised brain models, and TVB use cases.
In this tutorial, you will learn how to run a typical TVB simulation.
This lesson introduces TVB-multi-scale extensions and other TVB tools which facilitate modeling and analyses of multi-scale data.
This tutorial introduces The Virtual Mouse Brain (TVMB), walking users through the necessary steps for performing simulation operations on animal brain data.
In this tutorial, you will learn the necessary steps in modeling the brain of one of the most commonly studied animals among non-human primates, the macaque.
This lecture delves into cortical (i.e., surface-based) brain simulations, as well as subcortical (i.e., deep brain) stimulations, covering the definitions, motivations, and implementations of both.
This lecture provides an introduction to entropy in general, and multi-scale entropy (MSE) in particular, highlighting the potential clinical applications of the latter.