Directed Functional and Structural Connectivity in a Large-Scale Model for the Mouse Cortex

Abstract

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 electro-physiological recordings. Finally, GPDC measures also provided a measure of the flow of information among cortical areas.

Detection of phase coupling in interacting networks of cortical columns

“…In the absence of a specific external input (i.e., restingstate activity), functional connectivity is strongly correlated with structural connectivity at several levels [71] [72] [73]. The results of the simulations of grid (case study 2) and scale-free (case study 3) networks of interacting neural mass models of cortical columns show that our method to detect QPC has proven to be an effective tool for the investigation of functional connectivity [74] [28].…”