This module covers fMRI data, including creating and interpreting flatmaps, exploring variability and average responses, and visual eccenticity. You will learn about processing BOLD signals, trial-averaging, and t-tests. The MATLAB code introduces data animations, multicolor visualizations, and linear indexing.
Module 4: fMRI
Course Features
Tutorials
Code / Datasets
Exercises
Lessons of this Course
1
1
Duration:
7:15
Speaker:
This lecture introduces neuroscience concepts and methods such as fMRI, visual respones in BOLD data, and the eccentricity of visual receptive fields.
2
2
Duration:
12:15
Speaker:
This tutorial walks users through the creation and visualization of activation flat maps from fMRI datasets.
3
3
Duration:
12:05
Speaker:
This tutorial demonstrates to users the conventional preprocessing steps when working with BOLD signal datasets from fMRI.
4
4
Duration:
20:12
Speaker:
In this tutorial, users will learn how to create a trial-averaged BOLD response and store it in a matrix in MATLAB.
5
5
Duration:
12:52
Speaker:
This tutorial teaches users how to create animations of BOLD responses over time, to allow researchers and clinicians to visualize time-course activity patterns.
6
6
Duration:
13:39
Speaker:
This tutorial demonstrates how to use MATLAB to create event-related BOLD time courses from fMRI datasets.
7
7
Duration:
17:54
Speaker:
In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.
