Coursebooks

Advanced topics in network neuroscience

EE-619

Lecturer(s) :

Amico Enrico

Language:

English

Frequency

Every year

Remark

Next time: Fall 2020

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

1-       Introduction to Networks and Brain Networks (2 hours)

I will introduce fundamentals of graph theory and network science, with a focus on the application of these concept to brain networks 

2-       Observing the brain: basics of neuroimaging (2 hours)

I will give an overview of the main instruments to study in vivo human brains, and the physics behind it: magnetic resonance imaging (MRI) and electroencelography (EEG).

3-       Fundamentals of Brain Connectomics (2 hours)

I will introduce the concepts of functional and structural brain networks, and how to define links and nodes from brain data.

4-       Node Degree and Strength (1 hour + 1 hour exercise)

I will show how to define and extract brain node measurements. One hour of hands-on session will follow

5-       I MID TERM EXAM

6-       Centrality and Hubs (1 hour + 1 hour exercise)

I will explore the connectivity distribution of brain networks, and the existence of hub and peripheral nodes. We will learn how to quantify the importance or influence of a node on network function. One hour of hands-on session will follow

7-       Paths, Diffusion, and Navigation (1 hour + 1 hour exercise)

I will introduce the main tools to study information transfer and communication in brain networks, such as path, diffusion and navigation brain connectomes. One hour of hands-on session will follow.

8-       Modules and community structure of brain networks (1 hour + 1 hour exercise)

I will define concepts of modules and will explore the community structure of functional and structural brain networks. One hour of hands-on session will follow

9-        II MID TERM EXAM

10-    Individual differences and brain fingerprinting (1 hour + 1 hour exercise)

I will show how to detect individual differences from brain graphs, and on how to maximize the 'fingerprint' of functional and structural brain networks. One hour of hands-on session will follow

11-    Introduction to dynamic functional connectivity (1 hour + 1 hour exercise)

I will teach the basics of how to look at dynamical properties of functional brain connectomes. One hour of hands-on session will follow.

12-    Consciousness and brain connectomics (1 hour + 1 hour exercise)

I will overview the state of the art of scientific study of consciousness, and go through definitions and ways to assess levels of consciousness through the lens neuroimaging and brain networks.

13-    Invited lecture (1 hour + 1 hour exercise)

An expert in brain connectomics lecturing us on some cutting-edge topic (TBD). One hour of hands-on session will follow.

14 - FINAL EXAM

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:

Assessment methods

Course evaluation: 2 Mid Term exams: one at the end of the 5th week, one at the end of 10th week. At the end of the course there will be a Final Exam. Each of these three exams (the 2 Mid Terms and the Final) will impact 1/3 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

Reference week

 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German