April 14, 2021

Inferring large-scale structural connectivity from local activity in a model of the mouse large-scale cortical network

Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the Generalized Partial Directed Correlation (GPDC), provide estimates of the causal influence between areas. However, such methods have a limitation because their estimates depend on the number of brain regions simultaneously recorded. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale mouse cortical network. The model contains 19 cortical areas modeled using spiking neural populations, and directed weights for long-range projections were obtained from a tract-tracing cortical connectome. We show that the GPDC estimates correlate positively with structural connectivity. Moreover, the correlation between structural and directed functional connectivity is comparable even when using only a few cortical areas for GPDC estimation, a typical scenario for electrophysiological recordings. Finally, GPDC measures also provided a measure of the flow of information among cortical areas.

 bioRxiv Subject Collection: Neuroscience

 Read More

Leave a Reply

%d bloggers like this: