Manipulate the default connectome provided with TVB to see how structural lesions effect brain dynamics. In this hands-on session you will insert lesions into the connectome within the TVB graphical user interface (GUI). Afterwards, the modified connectome will be used for simulations and the resulting activity will be analysed using functional connectivity.

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 lecture covers the linking neuronal activity to behavior using AI-based online detection. 

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course. 

This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course. 

This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, Understanding Neural Networks.

This lesson gives an introduction to the central concepts of machine learning, and how they can be applied in Python using the scikit-learn package. 

This lesson provides a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.

This lesson from freeCodeCamp introduces Scikit-learn, the most widely used machine learning Python library.

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 covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.

This lecture covers visualizing extracellular neurotransmitter dynamics

This tutorial demonstrates how to perform cell-type deconvolution in order to estimate how proportions of cell-types in the brain change in response to various conditions. While these techniques may be useful in addressing a wide range of scientific questions, this tutorial will focus on the cellular changes associated with major depression (MDD). 

This is an in-depth guide on EEG signals and their interaction within brain microcircuits. Participants are also shown techniques and software for simulating, analyzing, and visualizing these signals.

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. 

In this third and final hands-on tutorial from the Research Workflows for Collaborative Neuroscience workshop, you will learn about workflow orchestration using open source tools like DataJoint and Flyte. 

In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks.