Summary

Computational methods for the analysis of human brain imaging data

Content

Keywords

neuroimaging, functional MRI, EEG, brain mapping, systems-level neuroscience

Learning Prerequisites

Important concepts to start the course

Mathematics at the engineering level (i.e., matrix algebra, probability theory)

Basic signal processing, statistics, and machine-learning concepts

Basic knowledge of Python programming language

Learning Outcomes

By the end of the course, the student must be able to:

• Analyze processing steps of neuroimaging data
• Assemble a neuroimaging pipeline
• Critique suitability of analysis methods
• Interpret results of neuroimaging analysis
• Explain choice of methodology

Transversal skills

• Use a work methodology appropriate to the task.
• Make an oral presentation.
• Give feedback (critique) in an appropriate fashion.

Teaching methods

Weekly lectures (2h) following by an exercise session (1h)

Three lab exercises during the semester

Journal club at the end of the semester

Expected student activities

attendance at lectures and exercises. one journal club.

Assessment methods

Attendance and completion of three lab exercises during the semester

Written exam

Supervision

Resources

Moodle Link

• http://moodle.epfl.ch/course/view.php?id=14944