NBE-E4250 - Mapping, Decoding and Modeling the Human Brain
Start date: Tuesday 10.1.2023 at 10.15, Place: F175a (Otakaari 3)
Teaching: Tuesdays 10.15–12.00, Wednesdays 10.15-12.00
Teacher: Linda Henriksson, linda.henriksson@aalto.fi
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 ECTS
Teaching period: III, 10.1.-22.2.2023 (exam 22.2.2023)
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%).
Preliminary schedule:
| Tue 10.1. | Lecture 0 (L0): Introduction |
|---|---|
| Wed 11.1. | L1: fMRI (standard analysis) |
| Tue 17.1. | Q&A: Project work 1 (GLM, activation) |
| Wed 18.1. | L2: mapping |
| Tue 24.1. | Q&A: P2 (mapping, ROIs) |
| Wed 25.1. | L3: univariate vs. multivariate analysis, decoding |
| Tue 31.1. | Q&A: P3 (classification) |
| Wed 1.2. | L4: representational similarity analysis, models |
| Tue 7.2. | Q&A: P4 (RSA) |
| Wed 8.2. | L5: voxel receptive field modeling, encoding |
| Tue 14.2. | Q&A + L6: Discussion and conclusions |
| Wed 22.2. | Exam + Portfolio deadline |
Grading points:
| 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.