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

Ronaldo V. Nunes, Marcelo Bussotti Reyes, Jorge F. Mejias, Raphael Y. de Camargo
doi: https://doi.org/10.1101/2021.01.28.428656
This article is a preprint and has not been certified by peer review [what does this mean?].

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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 29, 2021.

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