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 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.
This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.
This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.
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.
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 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.
This lecture gives an overview of how to prepare and preprocess neuroimaging (EEG/MEG) data for use in TVB.
In this lecture, you will learn about various neuroinformatic resources which allow for 3D reconstruction of brain models.
While the previous lesson in the Neuro4ML course dealt with the mechanisms involved in individual synapses, this lesson discusses how synapses and their neurons' firing patterns may change over time.
This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.
This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.
This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses.