Network Neuroscience: Methods & Applications
Frequency
Every year
Summary
This course provides students with a solid background on theory and applications for brain network analysis. It involves concepts from signal processing and graph tehory, applied to neuroimaging data to construct and analyze brain networks and their dynamics.
Content
Lecture 1 - Introduction to Network Neuroscience (2 hours)
- Motivation: the brain as a complex network
- Historical overview and key concepts
- Nodes, edges, and multiscale organization (neurons → brain regions)
- Structural vs. functional networks
Lecture 2 - Graph Theory Fundamentals (1 hour + 1 hour exercise)
- Graph types (binary, weighted, directed, dynamic)
- Basic measures: degree, strength, path length, clustering, centrality
- Community structure and modularity
Lecture 3 — Structural Brain Connectivity (2 hours)
- Diffusion MRI and tractography principles
- Building structural connectomes
- Issues: parcellation choice, tractography biases, thresholding
Lecture 4 - Functional Connectivity (1 hour + 1 hour exercise)
- MRI principles: resting-state vs task
- Correlation-based FC from fMRI
- Alternative measures: partial correlation, coherence, mutual information
- Confounds: motion, global signal, preprocessing impact
Lecture 5 - Functional Connectivity (1 hour + 1 hour exercise)
- Seed based FC, ICA
- Resting-state networks in healthy and disease
Lecture 6 - Dynamic and Time-Varying Connectivity (1 hour + 1 hour exercise)
- Sliding-window correlation
- Coactivation patterns, Hidden Markov Models, dynamic graph measures
- Interpretation and controversies
Lecture 7 - Multimodal Networks: Intro to Graph Signal Processing (GSP) (1 hour + 1 hour exercise)
- Introduction of GSP theory
- Application of GSP to neuroimaging
- Integration of structural and functional connectivity
Lecture 8 - Midterm exam
Lecture 9 - Network Neuroscience of Cognition (2 hours)
- Brain network reconfiguration during cognitive tasks
- Network flexibility and efficiency
- Linking networks to behavior and cognitive performance
Lecture 10 - Translational Applications: Connectome Fingerprinting (2 hours)
- Connectome fingerprinting to identify individuals
- Signal processing and machine learning perspectives
- Cognitive and clinical relevance
Lecture 11 - Clinical Applications (2 hours)
- Network alterations in neurological and psychiatric disorders
- Biomarker discovery and predictive models
Lecture 12 - Network Neuroscience in Alzheimer’s Disease (1 hour + 1 hour exercise)
- Network alterations in AD
- Brain GSP methods to find new biomarkers
Lecture 13 - Investigating high resolution (1 hour + 1 hour exercise)
- Increasing the field to increase spatial resolution: 7T MRI
- Principles of Layer fMRI
- Connectivity at super high resolution: Future promises and challenges
Lecture 14 - Final Exam
- Presentation of student projects
Keywords
Brain Networks, Functional Connectivity, fMRI, Neuroimaging, Network Neuroscience.
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.
Resources
Bibliography
Bassett, D., Sporns, O. Network neuroscience. Nat Neurosci 20, 353–364 (2017). https://doi.org/10.1038/nn.4502.
Fornito, Alex, Andrew Zalesky, and Edward Bullmore. Fundamentals of brain network analysis. Academic Press, 2016.
Moodle Link
In the programs
- Number of places: 60
- Exam form: Oral presentation (session free)
- Subject examined: Network Neuroscience: Methods & Applications
- Courses: 17 Hour(s)
- Exercises: 7 Hour(s)
- Project: 2 Hour(s)
- TP: 2 Hour(s)
- Type: optional