ArticleNetwork neuroscience (Cambridge, Mass.)2021
Directed functional and structural connectivity in a large-scale model for the mouse cortex.
Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Functional connectivity drifts during sleep as a marker of fluctuations in the level of consciousness.Neuroscience of consciousness · 2025Article
- Spontaneous Brain Activity Emerges from Pairwise Interactions in the Larval Zebrafish Brain.Physical review. X · 2024Article
- Maintenance of delay-period activity in working memory task is modulated by local network structure.PLoS computational biology · 2024Article
- Extracting Dynamical Understanding From Neural-Mass Models of Mouse Cortex.Frontiers in computational neuroscience · 2022Article
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4 authors.
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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 coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. 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 network model of the mouse cortex. The model contains 19 cortical areas composed of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.
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