In Spring 2021, all teaching of this course will be organized online.
Start date: 12.1.2021, Place: Zoom (log in to access the link)
Teaching: Tuesdays 10.15–12.00, Wednesdays 10.15-12.00
Teacher: Linda Henriksson, email@example.com
Core content: This course gives an overview of advanced approaches for analyzing and modeling human brain imaging data. The student will learn the main differences between univariate and multivariate analyses, and between encoding and decoding models. The students will also apply the different techniques to real data. The course will also give basic knowledge about the functional organization of the human visual system which will be used as a case example of how the different techniques are used in the context of neuroscientific questions.
Learning outcomes: After this course the student can
(1) list approaches for analyzing and modeling functional brain imaging data
(2) explain the difference between decoding and encoding models
(3) analyze brain imaging data using different approaches
(4) choose the appropriate analysis or modeling approach to new data
(5) critically review scientific literature on “brain decoding”
Prerequisites: Matlab (or python if you don't need help..). Basic knowledge of functional brain imaging (preferably fMRI), human brain function and signal processing.
Recommended course: NBE-E4045 Functional Brain Imaging
Credits: 5 ECTSWorkload: Lectures/contact teaching 24 h, Reading and homework 30 h, Project works 60 h, Compiling a portfolio 8 h, Getting ready for exam 8 h, Exam 3 h (Total: 133 h)
Teaching period: III, 12.1.-17.2.2021 (exam 24.2.2021)
Level of the course: Master’s and doctoral level
Study Material: Lecture slides, book chapters, scientific articles.
Assessment Methods and Criteria: Project works (60%), Homework & Portfolio (20%), Exam (20%).
Tue 12.1. Introduction Wed 13.1. Lecture 1: fMRI (standard analysis) Tue 19.1. Q&A: Project work 1 (GLM, activation) Wed 20.1. L2: mapping Tue 26.1. Q&A: P2 (mapping, ROIs) Wed 27.1. L3: univariate vs. multivariate analysis, decoding Tue 2.2. Q&A: P3 (classification) Wed 3.2. L4: representational similarity analysis, models Tue 9.2. Q&A: P4 (RSA) Wed 10.2. L5: voxel receptive field modeling, encoding Tue 16.2. Q&A Wed 17.2. Discussion and conclusions Wed 24.2. Exam
*5 points from returning the first version before weekly Q&A and active contribution during the session
max per week Max total points (100) Min points to pass Homework (5) 3 15 4 Project works (4) 5* + 10** 60 + 5 (bonus) 15 Portfolio 5 1 Exam 20 5
**10 more points for the final version, returned at the end of the course (see also portfolio)
Here you can ask questions related to the course. Everyone is encouraged to submit questions and answers.