Human Neuroimaging Methods

Psych204B: Human Neuroimaging Data Analysis Methods

This course introduces the student to data analyses and design considerations of human neuroimaging experiments. Emphasis is on building deep understanding of the underlying signals and computational approaches. The goal is to not only gain mathematical understanding of data analysis choices but also understand their consequences on interpreting brain function.

The course is a mixture of lectures, hands-on software tutorials, and a fMRI project where the class scans and analyzes fMRI data.  

The course will begin with fundamental topics including understanding the nature of fMRI signals, temporal and spatial resolution; signal to noise in fMRI and preprocessing; general linear models; statistics of fMRI. Then, the course will introduce advanced approaches including fMRI adaptation, multivoxel pattern analyses, representational similarity analyses, as well as decoding and encoding algorithms. Finally, we will discuss best practices in fMRI research.

Tu/Th 9-10:20am, 420-419

Instructor: Prof. Grill-Spector

Contact: kalanit@stanford.edu; Office hours by appointment

TA: Lior Bugatus

Contact: liorbu@stanford.edu; Office hours by appointment

Required prerequisites: Psych 204a

Recommended: Cognitive Neuroscience;  Matlab.

Textbook:  Functional Magnetic Resonance Imaging   Huettel et al., FMRI (Third Edition, Sinauer).  The book can be rented from the VitalSource site  (Links to an external site.) or purchased from Sinauer for a discount (about $90 dollars (Links to an external site.)).