Syllabus#
NEU 502B
Princeton University, Spring 2025
Time: Tu/W 2:00–5:00 pm
Location: PNI A03
Instructor: Sam Nastase (snastase@princeton.edu)
AIs: Kirsten Ziman (kz0108@princeton.edu), Ariadne Letrou (ariadne@princeton.edu)
Syllabus: Syllabus
GitHub: GitHub
Scratch: Scratch
This lab course closely accompanies NEU 502A and surveys the methodological landscape of cognitive computational neuroscience research. Students will learn the fundamentals of experimental design, data collection, preprocessing, and statistical analysis for fMRI and EEG/MEG, with an emphasis on best practices in reproducible neuroscience. Lectures will set the conceptual foundation for interactive, hands-on lab work using Jupyter notebooks. Advanced topics include multivariate pattern analysis (MVPA), representational similarity analysis (RSA), and regularized encoding models. This year, we’re experimenting with a new format that will interleave two threads: empirical (E) classes focused on fMRI and MEG projects, and computational (C) classes focused on modeling and neural networks. E classes will provide the empirical backbone of the course, while C classes (typically on Wednesdays) will focus on interactive modeling exercises paralleling the topics discussed in 502A. These two threads will converge over the course of the term. Students will be expected to design, analyze, and write up both an fMRI experiment and an OPM-MEG experiment as graded projects.
Lecture schedule#
Date |
Topic |
Slides/code |
Optional reading |
|---|---|---|---|
Tu 1/28 (E) |
Introduction and computing; MR physics and BOLD biology |
|
|
W 1/29 (E) |
fMRI design, preprocessing, and subject-level modeling (GLM) |
|
|
Tu 2/4 (E) |
fMRI group-level analysis and correction for multiple tests |
||
W 2/5 (C) |
Dynamics in perception |
||
Tu 2/11 (E) |
Multivariate pattern analysis (MVPA) |
||
W 2/12 (C) |
Decision making |
||
Tu 2/18 (E) |
Representational similarity analysis (RSA) and searchlights |
|
|
W 2/19 (C) |
Reinforcement learning |
||
Tu 2/25 (E) |
Naturalistic neuroimaging and voxelwise encoding models |
|
|
W 3/4 (C) |
Statistical learning and backpropagation |
||
Tu 3/4 (E) |
fMRI project data collection |
||
W 3/5 (E) |
fMRI project data collection |
||
Tu 3/11 |
No class (spring recess) |
||
W 3/12 |
No class (spring recess) |
||
Tu 3/18 (E) |
fMRI project data analysis |
||
W 3/19 (C) |
~~Statistical learning and language processing~~ |
||
Tu 3/25 (E) |
~~EEG/MEG signal, preprocessing, and modeling~~ |
||
W 3/26 (C) |
Selective attention, automaticity, and control |
||
Tu 4/1 (E) |
~~MEG project proposals and experimental design~~ |
||
W 4/2 (C) |
Conflict monitoring, effort, and control |
||
Tu 4/8 (E) |
MEG project data collection |
||
W 4/9 (E) |
MEG project data analysis |
||
Tu 4/15 (E) |
Deep learning models for vision |
|
|
W 4/16 (E) |
Progress report presentations |
||
Tu 4/22 (E) |
Deep learning models for language |
|
|
W 4/23 (E) |
fMRI and MEG project presentations |
||
M 5/6 |
No class (final written reports due) |
The content of this course is inspired by related courses designed by Jonathan Cohen, Leigh Nystrom, Jody Culham, and Jim Haxby, and built on top of lots of hard work by Younes Strittmatter, Zaid Zada, and many others.
Supplementary reading#
Rokem, A., & Yarkoni, T. (2024). Data Science for Neuroimaging: An Introduction. Princeton University Press. link
Huettel, S. A., Song, A. W., & McCarthy, G. (2014). Functional Magnetic Resonance Imaging (3rd Ed.). Sinauer Associates. link
Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Handbook of Functional MRI Data Analysis. Cambridge University Press. DOI
Bandettini, P. A. (2020). fMRI. MIT Press. link
Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique (2nd ed.). MIT Press. link
McClelland, J. L. (2015). Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises (2nd ed.). MIT Press. PDF
Duvernoy, H. M. (1999). The Human Brain: Surface, Three-Dimensional Sectional Anatomy with MRI, and Blood Supply (2nd ed.). Springer. DOI