Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/46885
Title: Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals
Author: Stetter, Olav
Battaglia, Demian
Soriano i Fradera, Jordi
Geisel, Theo
Keywords: Bioinformàtica
Xarxes neuronals (Neurobiologia)
Electrofisiologia
Algorismes computacionals
Sinapsi
Neurofisiologia
Bioinformatics
Neural networks (Neurobiology)
Electrophysiology
Computer algorithms
Synapses
Neurophysiology
Issue Date: 2012
Publisher: Public Library of Science (PLoS)
Abstract: A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.
Note: Reproducció del document publicat a: http://dx.doi.org/10.1371/journal.pcbi.1002653
It is part of: PLoS Computational Biology, 2012, vol. 8, num. 8, p. e1002653
Related resource: http://dx.doi.org/10.1371/journal.pcbi.1002653
URI: http://hdl.handle.net/2445/46885
ISSN: 1553-734X
Appears in Collections:Articles publicats en revistes (Física Quàntica i Astrofísica)

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