In this tutorial, users will learn how to create a trial-averaged BOLD response and store it in a matrix in MATLAB. 

This tutorial teaches users how to create animations of BOLD responses over time, to allow researchers and clinicians to visualize time-course activity patterns.

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

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 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 and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

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.