Perceptual Neuroimaging in Practice
During term time we meet for weekly sessions lasting 1-2hrs.
Generally, each topic is covered by two sessions, the first being an (interactive) lecture introducing the topic and leading up to a coding homework. The second session is a flipped classroom in which we review the coding homework and any problems we ran into.
PNiP is split into two terms. Roughly, the first term emulates SPM's Methods for Dummies and covers the basics. Topics range from programming a minimal experiment, the HRF, preprocessing, quality assurance and GLMs to optimising designs and statistical considerations. Most topics during the first term will be presented by participants.
Your presentation should last 30-45 minutes (plus 15 minutes discussion). It should cover an intuitive explanation of the theory as well as a walk-through of the practical implementation in SPM12, which should culminate in a coding homework. Fear not - you can always ask an expert for help and don't have to start from scratch. You can build on slides of your predecessors or MfD. However, you should make a real effort to learn about 'your' topic, so everyone will profit from each other.
The second term covers more advanced topics, like tractography, surface reconstruction, MVPA, RSA, deep nets and encoding models. These will mostly be presented by local and international experts on the respective topic. Typically, coding exercises will require installing additional software which will be provided during the first session covering a topic.
-> We will provide example data for the practical parts. However, if you have data of your own, feel free to apply what you learned to that!
-> At least one expert will be available for help with questions - either in person or as an e-mail joker (PI with MRI experience, usually Ben)
-> If you like, you can also use a slot to present and discuss your own work
Laptop or computer
Willingness to participate actively and regularly
2 volunteers organising the schedule and liaising w Ben
Handbook of Functional MRI Data Analysis by Poldrack et al. is an excellent conceptual introduction
Statistical Analysis of fMRI Data by Ashby covers many topics in depth and code
All participants are encouraged to work through these coding exercises, to get into neuroimaging data in MATLAB. If you are new to programming and/or MATLAB, I further recommend working through Chapter 2 in Pascal Wallisch's MATLAB for Neuroscientists.