From functional to structural connectivity using partial correlation in neuronal assemblies

Journal of Neural Engineering
Daniele PoliPaolo Massobrio

Abstract

Our goal is to re-introduce an optimized version of the partial correlation to infer structural connections from functional-effective ones in dissociated neuronal cultures coupled to microelectrode arrays. We first validate our partialization procedure on in silico networks, mimicking different experimental conditions (i.e., different connectivity degrees and number of nodes) and comparing the partial correlation's performance with two gold-standard methods: cross-correlation and transfer entropy. Afterwards, to infer the structural connections in in vitro neuronal networks where the ground truth is unknown, we propose a thresholding heuristic approach. Then, to validate whether the partialization process correctly reconstructs macroscopic features of the network structure, we extract a modularity index from segregated in silico and in vitro models. Finally, as a case study, we apply our partialization procedure to analyze connectivity and topology on spontaneous developing and electrically stimulated in vitro cultures. In simulated networks, partial correlation outperforms cross-correlation and transfer entropy at low and medium connectivity degrees, not only in relatively small (60 nodes) but also in larger (120-240 nodes) as...Continue Reading

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Citations

Apr 12, 2016·Frontiers in Neuroinformatics·Vito Paolo PastorePaolo Massobrio
Oct 6, 2016·Brain Sciences·Alianna J Maren
Aug 16, 2017·Scientific Reports·Balasundaram KadirveluSlawomir J Nasuto
Jun 2, 2018·Journal of Neural Engineering·Anjali Vijay DhobaleD Kacy Cullen
Feb 23, 2020·Journal of Computational Neuroscience·Cody BakerRobert Rosenbaum
Oct 13, 2020·Journal of Neural Engineering·Martina BrofigaPaolo Massobrio

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