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We designed this class to teach you:

  • the ideas and practice of functional MRI analysis;
  • how to work efficiently with your computer;
  • how to collaborate with code and analysis;
  • making your work reproducible.

During the class you’ll see us put a constant emphasis on collaboration for learning and for increasing the quality of your work.

The best way to learn this, is with a substantial shared project. So, a major part of this class is the final project.

A group project

Because we want you to learn how to share work, the projects should be in groups of 2 or 3. Please agree your projects and mentors in discussion with the others in the class.

Choosing a project

The project should be an open-ended exploration and analysis of functional MRI data on multiple subjects.

A typical example would be to for you to explore a dataset, replicate a previous analysis, and extend the previous analysis. For example, you might:

  • take an OpenFMRI dataset;
  • investigate the data for outliers and sources of noise;
  • attempt to replicate the original analysis of the data;
  • explore the assumptions of the original analysis and whether these assumptions and met;
  • explore other analyses of the data to test new hypotheses.


For examples of projects in a previous class, please see https://github.com/berkeley-stat159, in particular:

You’ll notice that these analysis are fully reproducible – the students had to provide instructions that their graders could follow to reproduce their whole analysis, including their project figures.

Project data

Your project must be reproducible. That means, that we, your graders, must be able to get the raw data for your project onto our own computers, and run your analysis on this data.

We strongly prefer that the data that you use be available to anyone who agrees to license for the data. For example, all the OpenFMRI datasets have a liberal license (PDDL 1.0) allowing re-use and redistribution of the data.


As for the Examples, you should submit the final version of your project as a github git repository.

The project should have a README file that gives an introduction to the project, and lists the steps that your readers should follow in order to get the data, and run your analysis.

Your repository should also contain the information necessary to build your final project report, including the figures.


A major part of collaboration is learning to learn from your more experienced peers. We are expecting that they, like us, will learn from you.

Each project will have a mentor with experience of functional FMRI analysis. See Project mentors for the instructions we give to the project mentors.

Of most use to you will be to find a mentor that you will work with later in your Berkeley career. If you have an idea which lab you will be working with after this course, please try and find a mentor from that lab. Please point potential mentors at the Project mentors page and tell them to get in contact with us as soon as possible.


We’ll ask you to start thinking about your project in the first class.

  • September 30 : deadline for choosing a mentor;
  • October 17 : project pitches to the class. Each group makes a 10 minute presentation describing:
    • the dataset;
    • any exploration you have already done;
    • your initial plan of investigation.
  • December 5 : project presentation;
  • December 17 : final project submission.