EE-619 / 2 credits

Teacher: Amico Enrico

Language: English

Remark: Next time: Fall 2021


Frequency

Every year

Summary

The main goal of this course is to give the student a solid introduction into approaches, methods, and tools for brain network analysis. The student will learn about principles of network science and how to implement and develop methods and tools for graph theoretical analysis of brain data.

Content

Keywords

Brain Networks, Network Science, Brain Connectomics.

 

Learning Prerequisites

Important concepts to start the course

Basic knowledge of MATLAB is preferred, but not required.

Learning Outcomes

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

  • Exploit functional and structural brain graphs from neuroimaging data, to master and extract advanced network science methodologies on brain networks, and to in-terpret the results.

Assessment methods

One Midterm exam, at the end of the 8th week. At the end of the course there will be a Final Exam. Each of these two exams (the Midterm and the Final) will impact 1/2 on the final grade.

Resources

Bibliography

Fornito, Alex, Andrew Zalesky, and Edward Bullmore. Fundamentals of brain network analysis. Academic Press, 2016.

 

 

Ressources en bibliothèque

In the programs

  • Number of places: 60
  • Exam form: Written (session free)
  • Subject examined: Advanced topics in network neuroscience
  • Lecture: 28 Hour(s)
  • Number of places: 60
  • Exam form: Written (session free)
  • Subject examined: Advanced topics in network neuroscience
  • Lecture: 28 Hour(s)

Reference week