title: Functional connectivity estimation over large networks at cellular resolution based on electrophysiological recordings and structural prior.
abstract: Despite many structural and functional aspects of the brain organization have been extensively studied in neuroscience, we are still far from a clear understanding of the intricate structure-function interactions occurring in the multi-layered brain architecture, where billions of different neurons are involved. Although structure and function can individually convey a large amount of information, only a combined study of these two aspects can probably shade light on how brain circuits develop and operate at the cellular scale. Here, we propose a novel approach for refining functional connectivity estimates within neuronal networks using the structural connectivity as prior. This is done at the mesoscale, dealing with thousands of neurons while reaching, at the microscale, an unprecedented cellular resolution. The High-Density Micro Electrode Array (HD-MEA) technology, combined with fluorescence microscopy, offers the unique opportunity to acquire structural and functional data from large neuronal cultures approaching the granularity of the single cell. In this work, an advanced method based on probabilistic directional features and heat propagation is introduced to estimate the structural connectivity from the fluorescence image while functional connectivity graphs are obtained from the cross-correlation analysis of the spiking activity. Structural and functional information are then integrated by reweighting the functional connectivity graph based on the structural prior. Results show that the resulting functional connectivity estimates are more coherent with the network topology, as compared to standard measures purely based on cross-correlations and spatio-temporal filters. We finally use the obtained results to gain some insights on which features of the functional activity are more relevant to characterize actual neuronal interactions.
authors.affiliation: Department of Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Simona
authors.initials: S
authors.lastname: Ullo
authors.affiliation: Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Thierry R
authors.initials: TR
authors.lastname: Nieus
authors.affiliation: Department of Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Diego
authors.initials: D
authors.lastname: Sona
authors.affiliation: Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Maccione
authors.affiliation: Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Luca
authors.initials: L
authors.lastname: Berdondini
authors.affiliation: Department of Pattern Analysis and Computer Vision, Istituto Italiano di Tecnologia Genova, Italy ; Department of Computer Science, University of Verona Verona, Italy.
authors.forename: Vittorio
authors.initials: V
authors.lastname: Murino
keywords: Von Mises distribution; connectomics; electrophysiology; functional connectivity; graph heat kernel; high-density Micro Electrode Array; probabilistic directional feature; structural connectivity