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SS9833B/CS9869B: Analysis of brain imaging data
Description:
This interdisciplinary course provides a hands-on introduction into modern statistical approaches to the analysis of brain imaging signals, with special emphasis on modern multivariate techniques to infer on brain representations.
Prerequisites:
The course is targeted at MSc and PhD students in Statistics, Computer Science, Neuroscience, Psychology and Medical Biophysics. Basic programming skills in a high-level language such as Matlab, R, or Python are required for all students. For students with a neuroscience / psychology / medical biophysics background, basic experience with a brain imaging method, as well as basic knowledge in linear algebra and statistics (multiple regression) is required. Ideally neuroscience students would bring a data set from his/her own research for the project. For students with a computer science / statistics background prerequisites are a good grasp of general linear model analysis and an interest in complex data analysis.
Course content:
The class is taught in an inverted classroom style. Students will listen to recorded tutorial videos that cover basic concepts of brain imaging analysis at their own time. Class time will be devoted to student-led discussions about these concepts. Much of the class effort lies in the weekly homework assignments, which are design to deepen the understanding of important concepts. The general topics are:
- Standard analysis / Background
- Basics of different brain imaging signals
- Data formats and preprocessing
- Generalized linear model analysis
- Inference and the multiple testing problem
- Multivariate analysis
- Classification and Decoding
- Representational similarity analysis
- Encoding models
Project:
Students will also conduct an independent analysis project, typically done in teams of 2 students. Ideally, students with neuroscience background pair with students from a statistics / computer science background. The data set will either come from a project of the neuroscience student or will be provided in class.Evaluation:
50% of the grade is determined by the weekly homeworks. The other 50% are determined by the project assessed by an oral presentation.
Class time:
Winter term, 2018:Thursdays, 1pm-2:30pm
Middlesex College, room 204
Instructor
Jörn Diedrichsenjdiedric@uwo.ca
Office: WIRB 4138