Reverse Engineering Structural Connectivity in Brain Networks

Explore the intricate relationship between causal and structural connectivity in brain networks through this illuminating conference talk. Delve into the theoretical foundations of mapping structural and causal connectivity using voltage signals from simulated neuronal networks. Examine the application of four key causality measures - time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy - to nonlinear networks with pulse signal outputs. Discover how these measures interconnect when applied to pulse signals and validate their relationships through case studies involving simulated and empirical brain networks. Learn about the potential for reconstructing structural connectivity in networks with pulse outputs using pairwise methods, avoiding the need for global information and overcoming the curse of dimensionality. Gain insights into a robust methodology for understanding and mapping neural circuitry, with significant implications for brain network analysis and neuroscience research.

Douglas Zhou: Reverse engineering structural connectivity in brain networks #ICBS2024