This tutorial walks users through the creation and visualization of activation flat maps from fMRI datasets.
This tutorial demonstrates to users the conventional preprocessing steps when working with BOLD signal datasets from fMRI.
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 lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course.
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 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 lecture provides an introduction to entropy in general, and multi-scale entropy (MSE) in particular, highlighting the potential clinical applications of the latter.