Advanced topics in network neuroscience
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
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- 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.
6- 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 of brain connectomes. One hour of hands-on session will follow.
7- 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.
8- MIDTERM EXAM
9- Individual differences and brain fingerprinting (1 hour + 1 hour exercise)
I will show how to detect individual differences from brain graphs, and how to maximize the “fingerprint†of functional and structural brain networks. One hour of hands-on session will follow
10- 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.
11- 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 of neuroimaging and brain networks.
12- 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.
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:
- 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.
Références suggérées par la 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)