Speakers:
Moein Khajenejad &
Forough HabibollahiThe study of neuronal populations through graph and network theory provides powerful insights into the organizational principles of the brain. This half-day tutorial will focus on leveraging computational tools and methodologies to analyze neuronal activity as networks, uncovering patterns of functional and effective connectivity, and exploring causal relationships in brain circuits.
The tutorial will feature two main components:
- Graph Analysis of Neuronal Populations
Participants will learn how to construct and analyze networks from neuronal activity data using graph-theoretic approaches. Topics will include the creation of adjacency matrices, modularity detection, and the use of metrics like clustering coefficients, centrality, and small-worldness to understand neuronal communication and information flow. Practical examples will be drawn from experimental datasets, such as in vitro spike trains from DishBrain, a pioneering system demonstrating rudimentary biological intelligence by leveraging the adaptive properties of neurons, and also from fMRI time series. This section will also discuss common pitfalls in interpreting functional connectivity data.
- Causal Graph Discovery in Neural Systems
This segment will introduce techniques for inferring causal relationships in neuronal populations, emphasizing methods such as Granger causality and machine learning-based approaches, including Graph Neural Networks and Large Language Models. Participants will gain hands-on experience with Python tools for implementing these methods, identifying directed functional connections, and understanding the limitations and assumptions of causal inference in neuroscience.
By the end of the session, attendees will have gained practical knowledge and exposure to state-of-the-art methodologies for analyzing neuronal networks and inferring connectivity maps, equipping them to apply these tools to their research.
