Inferring large-scale structural connectivity from local activity in a model of the mouse large-scale cortical network
作者： J. F. Mejias , M. B. Reyes , R. V. Nunes , R. Y. de Camargo
DOI: 10.1101/2021.01.28.428656
关键词: Systems neuroscience 、 Electrophysiology 、 Connectome 、 Cortical network 、 Artificial intelligence 、 Scale (ratio) 、 Pattern recognition 、 Functional connectivity 、 Computer science
摘要: Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional measures, such as Generalized Partial Correlation (GPDC), provide estimates of causal influence between areas. However, methods have limitation because their depend on number brain regions simultaneously recorded. We analyzed this problem by evaluating effectiveness GPDC to estimate ground-truth, data-constrained computational model large-scale mouse cortical network. The contains 19 areas modeled using spiking neural populations, and directed weights for long-range projections were obtained tract-tracing connectome. show that correlate positively with connectivity. Moreover, correlation comparable even when only few estimation, typical scenario recordings. Finally, measures also provided measure flow information among
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