Directed effective connectivity of in vitro neuronal networks revealed from electrophysiological recordings

Studying connectivity of in vitro neuronal network revealed from electrophysiological recordings can provide insights for understanding the brain network. Existing methods focus on estimating functional connectivity defined by statistical dependencies between neuronal activities but it is effective connectivity that captures the relevant direct casual interactions. We present a method that makes explicit use of a theoretical result that effective connectivity is contained in the relation between time-lagged cross-covariance and equal-time cross-covariance. Applying this method to data recorded by multi-electrode arrays of over 4000 electrodes, we estimate the directed effective connectivity and synaptic weights of neuronal cultures at different days in vitro. Our analyses show that the neuronal networks are highly nonrandom with a fraction of inhibitory nodes close to the values measured in monkey cerebral cortex, have small-world topology and feeder hubs of large outgoing degree and the distributions of the average incoming and outgoing synaptic strength are non-Gaussian with long tails.