Methods in Cognitive Neuroscience

NEU 502B — Neuroscience: From Molecules to Systems to Behavior
Princeton University, Spring 2024
Time: M/W 1:00–4:00 pm
Location: PNI A03
Instructor: Sam Nastase (snastase@princeton.edu)
AI: Declan Campbell (idcampbell@princeton.edu)
Syllabus: Syllabus
GitHub: GitHub
Scratch: Scratch

This lab course covers the methodological landscape of human cognitive neuroscience research. Students will learn the fundamentals of experimental design, data collection, preprocessing, and statistical analysis for fMRI, EEG/MEG, and ECoG, 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), functional connectivity, intersubject correlation (ISC) analysis, and regularized encoding models. Students will be expected to design, analyze, and write up an fMRI experiment as a final project.

Lecture schedule

Date	Topic	Slides/code	Homework	Optional reading
M 1/29	Introduction and computing tools; MR physics	`Slides` `Code`		`Logothetis et al., 2001` `Buxton, 2013`
W 1/31	Biological basis of BOLD	`Slides` `Code`		`Ogawa et al., 1992` `Bandettini et al., 1992` `Kwong et al., 1992`
M 2/5	fMRI experimental design and confounds Homework 1 (due 2/14)	`Slides` `Code`	`Homework 1`	`Boynton et al., 1996` `Dale & Buckner, 1997` `Power et al., 2012`
W 2/7	fMRI preprocessing and subject-level modeling (GLM)	`Slides` `Code`		`Friston et al., 1994` `Lindquist, 2008` `Esteban et al., 2019`
M 2/12	Group-level analysis and correction for multiple tests	`Slides` `Code`		`Nichols & Holmes, 2002` `Eklund et al., 2016`
W 2/14	Best practices in reproducible neuroimaging Homework 2 (due 2/26)	`Slides` `Code`	`Homework 2`	`Carp, 2012` `Nichols et al., 2017` `Poldrack et al., 2019`
M 2/19	Multivariate pattern analysis (MVPA)	`Slides` `Code`		`Haxby et al., 2001` `Norman et al., 2006` `Tong & Pratte, 2012`
W 2/21	Representational similarity analysis (RSA) and searchlights	`Slides` `Code`		`Edelman, 1998` `Kriegeskorte et al., 2006` `Kriegeskorte et al., 2008`
M 2/26	~~Voxelwise encoding models~~ ~~Homework 3 (due 3/6)~~ Class canceled
W 2/28	Class canceled
M 3/4	Voxelwise encoding models Homework 3 (due 3/20)	`Slides` `Code`	`Homework 3`	`Mitchell et al., 2008` `Naselaris et al., 2011` `Huth et al., 2016`
M 3/6	Naturalistic design, intersubject correlation, hyperalignment; Structural and functional connectivity	`Slides` `Code`		`Hasson et al., 2004` `Nastase et al., 2019` `Nastase et al., 2020` `Haxby et al., 2020` `Bullmore & Sporns, 2009` `Biswal et al., 2010` `Yeo et al., 2011`
M 3/11	No class (spring recess)
W 3/13	No class (spring recess)
M 3/18	EEG preprocessing Homework 4 (due 3/27)	`Slides` `Code`	`Homework 4`	`Buzsáki et al., 2012` `Gramfort et al., 2013`
W 3/20	EEG ERP and time-frequency analysis; Project proposal presentations	`Slides` `Code`		`Hillyard & Kutas, 1983` `Kutas & Federmeier, 2010` `Fries, 2015` `Cohen, 2017`
M 3/25	Experimental design in PsychoPy			`Peirce et al., 2019`
W 3/27	fMRI data collection
M 4/1	fMRI data collection
W 4/3	fMRI data collection Homework 5 (due 4/15)		`Homework 5`
M 4/8	EEG facility demonstration
W 4/10	ECoG preprocessing and analysis	`Slides` `Code`		`Mukamel et al., 2012` `Parvizi & Kastner, 2018`
M 4/15	Parallel distributed processing ~~Homework 6 (due 4/24)~~	`Code`		`Rumelhart et al., 1986` `McClelland et al., 2010` `Richards et al., 2019` `Hasson et al., 2020`
W 4/17	Deep learning: vision	`Slides` `Code`		`Kriegeskorte et al., 2015` `Yamins & DiCarlo, 2016` `Lindsay, 2021`
M 4/22	Progress report presentations
W 4/24	Deep learning: language	`Slides` `Code`		`Manning et al., 2020` `Schrimpf et al., 2021` `Goldstein et al., 2022`
M 5/6	No class (final written report due)

The content of this course is inspired by related courses designed by Leigh Nystrom, Jonathan Cohen, Jody Culham, and Jim Haxby.

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