This lecture introduces neuroscience concepts and methods such as fMRI, visual respones in BOLD data, and the eccentricity of visual receptive fields.
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 lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.
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.
This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models.
Following the previous lesson on neuronal structure, this lesson discusses neuronal function, particularly focusing on spike triggering and propogation.
This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted.