From functional to structural connectivity using partial correlation in neuronal assemblies.

OBJECTIVE: 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. APPROACH: 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. MAIN
RESULTS: 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) assemblies. Furthermore, partial correlation correctly identifies interconnected neuronal sub-populations and allows one to derive network topology in in vitro cortical networks. SIGNIFICANCE: Our results support the idea that partial correlation is a good method for connectivity studies and can be applied to derive topological and structural features of neuronal assemblies.