Computational Methods for Brain Connectivity Modelling

Franfurtk, Max Planck Institute for Empirical Aesthetics From June 17th (Monday) to June 18th (Tuesday).

Learn about advanced biophysical modeling and high-order functional connectivity techniques from world-leading experts. Discover how these methods are transforming our understanding of aging, neurodegeneration, and consciousness.

Full program here: https://ncclabmpi.github.io/comp_neuro_workshop/program

Program.

Information Theory

• Fundamentals of information theory.
• Connectivity analyses from information theory.
• Examples of applications in neuroscience.

High-order interdependencies

• Basics of high-order functional connectivity.
• High-order interactions in aging and dementia.
• Examples in consciousness and brain stimulation.

Tutorial I: information theory applied to fMRI and/or M/EEG data

Basics of whole-brain modeling Part I

• Why use mathematical models in neuroscience?
• Different scales of modeling: neuron-level, neural masses, and whole-brain.
• Neural mass models: Wilson-Cowan and Jansen & Rit.
• Basic elements of whole-brain models.
• Fitting models to empirical data.

Basics of whole-brain modeling Part II

• Real examples of modeling.
• Brain aging and neurodegeneration.
• Brain clocks.

Tutorial II: Example of whole-brain modeling and fitting

Basics of connectivity analyses Part I

• Types of connectivity (structural, functional, effective).
• Model-free and model-based connectivity measures.
• Modeling dynamics of brain activity through differential equations.
• Dynamic causal modeling for fMRI data.

Basics of connectivity analyses Part II

• Dynamic causal modeling for M/EEG data.
• Model comparison.
• Group analyses.
• Real examples of modeling.

Tutorial III: DCM for fMRI and/or M/EEG, depending on the participants’ interests.**

Basic Bibliography

• Timme, N. M., & Lapish, C. (2018). A tutorial for information theory in neuroscience. eneuro, 5(3).

• Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory.

• Battiston, F., Amico, E., Barrat, A., Bianconi, G., Ferraz de Arruda, G., Franceschiello, B., … & Petri, G. (2021). The physics of higher-order interactions in complex systems. Nature Physics, 17(10).

• Kiebel, S. J., Garrido, M. I., Moran, R. J., & Friston, K. J. (2008). Dynamic causal modelling for EEG and MEG. Cognitive neurodynamics, 2.

• Marreiros, A. C., Stephan, K. E., & Friston, K. J. (2010). Dynamic causal modeling. Scholarpedia, 5(7).

• Friston, K. J. (2011). Functional and effective connectivity: a review. Brain connectivity, 1(1).

• Lynn, C. W., & Bassett, D. S. (2019). The physics of brain network structure, function and control. Nature Reviews Physics, 1(5).

• Cofré, R., et al. (2020). Whole-brain models to explore altered states of consciousness from the bottom up. Brain Sciences, 10(9).

• Blohm, G., et al. (2020). A how-to-model guide for neuroscience. Eneuro, 7(1).