NBE-E4250 - Mapping, Decoding and Modeling the Human Brain D, Lecture, 10.1.2023-22.2.2023
This course space end date is set to 22.02.2023 Search Courses: NBE-E4250
Topic outline
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We have a set of different assignments to help you learn the topics of this course. Before each lecture on Wednesdays, I will provide you some reading material + a few questions related to this material (this is called "homework"). In addition, you will analyze brain imaging data using the different analysis approaches introduced in the lectures ("project works"). Finally, you will compile a portfolio of the completed homework and project works ("portfolio"). You will get points for doing the assignments. At the end of the course, we will also have an exam; if you have done your homework & project works, you should be ready for the exam without much extra work.
Homework
Each week I will select you something to read (book chapter, scientific article) to get you tuned for the next lecture. The homework for each lecture will be a few questions related to this material. You should return your answers each week before the lecture. No need to write long answers.
Project works
In the weekly project works, you are analyzing brain imaging (functional magnetic resonance imaging, fMRI) data using the different analysis approaches introduced in the lectures. The project works are cumulative---you will need to do the first project work to be able to do the second step. The projects can be done in small groups, but everyone must write his/her own report about the analysis and the results. All analysis, including visualizations, should be done with Matlab (or Python) without using any existing fMRI analysis toolboxes.
The results of the project works will be discussed during the Q&A sessions each Tuesday and a report of each project work will be included in a portfolio. Each project report included in the portfolio should include (1) introduction to the topic of the week’s topic, (2) description of the analysis (including the Matlab/Python code), (3) main results of the analysis, and (4) discussion and conclusions on the results.
60% of your grade will be based on the project works. The idea is that to earn the maximum points from a project work, you must return the first version of the completed project work by the Q/A session each Tuesday and be prepared to discuss and present some analysis/results during the session. The final versions of the completed projects must be included in a portfolio and returned by the end of the course.
I suggest that you use the openly available dataset by Haxby et al: http://data.pymvpa.org/datasets/haxby2001/ . Feel free to look for other openly available datasets too (e.g., from https://openneuro.org/)---the minimum requirements are that the data has been preprocessed, there are different stimulus categories (or tasks) and there are multiple repetitions for each stimulus category (i.e., the data can be split to training and test sets).
**please use the Discussion forum ('Need help?' on the front page) for asking questions related to the project works**
Portfolio
During the course, compile a portfolio of the completed homework and project works that you will return at the end of the course. The portfolio should help in recognizing the big picture of the content of the course. The lectures, reading material and homework provide the theoretical background, and the project works provide the practical hands-on learning.
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Return the Portfolio by Wednesday February 22 at 18:00
Include:
- Final versions of all project works (4 rounds + possible bonus)
- All homework (answers to questions; 5 rounds)
Tie all the material together to a coherent document!
Points:
- max 10 more points per project work (total 40)
- max 5 for the overall looks of the portfolio