This tutorial demonstrates how to use MATLAB to create event-related BOLD time courses from fMRI datasets. 

In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.

This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging. 

This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time. 

This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.

In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data. 

This lesson teaches users how MATLAB can be used to apply image processing techniques to identify cell bodies based on contiguity.

This tutorial demonstrates how to extract the time course of calcium activity from each clusters of neuron somata, and store the data in a MATLAB matrix.

This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.

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. 

This is a tutorial introducing participants to the basics of RNA-sequencing data and how to analyze its features using Seurat. 

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 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 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. 

Similarity Network Fusion (SNF) is a computational method for data integration across various kinds of measurements, aimed at taking advantage of the common as well as complementary information in different data types. This workshop walks participants through running SNF on EEG and genomic data using RStudio.

Explore how to setup an epileptic seizure simulation with the TVB graphical user interface. This lesson will show you how to program the epileptor model in the brain network to simulate a epileptic seizure originating in the hippocampus. It will also show how to upload and view mouse connectivity data, as well as give a short introduction to the python script interface of TVB.

Brain network reconstruction from empirical data is of key importance to generate personalized virtual brain models. This lecture will introduce the basic concepts of preprocessing structural, functional and diffusion weighted neuroimages. It highlights the latest methods and pipelines to extract structural as well as functional connectomes according to a multimodal parcellation.

This lecture presents two recent clinical case studies using TVB: stroke recovery and dementia (due to Alzheimer’s Disease (AD)). Using a multi-scale neurophysiological model based on empirical multi-modal neuroimaging data, we show how local and global biophysical parameters characterize changes in individualized patient-specific brain dynamics, predict recovery of motor function for stroke patients, and correlate with individual differences in cognition for AD patients.