Anatomical connectivity reconstruction of biological neuronal networks using Granger causality

Accessing to the anatomical connectivity of the cortical network is crucial for understanding the dynamics and functions of the brain. While direct experimental measurements of anatomical connectivity are costly, effective connectivity methods offer a potential alternative approach to reconstructing anatomical connections with the help of statistical tools. Granger causality (GC) is one of the most widely used tools for network effective connection estimations in brain networks. However, whether the effective connectivity estimated by GC can help to reliably capture the information of the underlying anatomical connectivity of neuronal networks remains largely unknown. In this work, we demonstrate that GC can effectively reconstruct the anatomical connectivity of Hodgkin-Huxley (HH) neuronal networks using neuronal voltage time series data across various dynamical regimes. Moreover, we uncover the quantitative mechanisms underlying the accurate reconstruction capabilities of GC. Furthermore, we extend our analysis from HH type point neuronal networks to multi-compartment neuronal networks, and from voltage data to spike-train data. The GC-based reconstruction remains consistently effective across these different scenarios. Finally, we investigate GC-based reconstruction using real experimental data from Allen Institute, demonstrating that GC-reconstructed connectivities exhibit high consistency across different stimulus conditions. Overall, our findings provide a strong theoretical foundation for the use of GC in realistic neuronal network reconstructions.