General information

The following tutorials will show you how to analyze a sample dataset with SPM. You will begin by learning the fundamentals of fMRI preprocessing, and then proceed to create a statistical model to estimate brain activity in response to different conditions. After learning about group-level results and how to script your analysis.

This course will show you how to analyze an fMRI dataset from start to finish. We will begin by exploring your dataset by inspecting the anatomical and functional images for each subject. We will then preprocess the data, which removes noise and enhances the signal in the images. Once the images have been preprocessed, we will create a model representing what we think the BOLD signal, a measure of neural activity, should look like in our images. During model fitting we compare this model with the signal in different areas of the image. This model fit is a measure of the strength of the signal under different conditions - for example, we can take the difference of the signal between conditions of the experiment to see which condition leads to a larger BOLD response.

Once a model has been created for each subject and the BOLD response has been estimated for each condition, we can do any kind of group analysis we like: Paired t-tests, between-group t-tests, interactions, and so on. The goal of this course is to calculate a simple within-subjects contrast between two conditions, and test whether it is significant across subjects. You will also learn how to create figures showing whole-brain analyses, similar to what you see published in the neuroimaging journals.