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 provides instruction on how to interact with and leverage Python packages to get the most out of Python's suite of available tools for the manipulation, management, analysis, and visualization of neuroscientific data. 

This video will document the process of creating a pipeline rule for batch processing on brainlife.

This video will document the process of launching a Jupyter Notebook for group-level analyses directly from brainlife.

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 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. 

This tutorial covers the fundamentals of collaborating with Git and GitHub.