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Chaos. 2017 Apr;27(4):047401. doi: 10.1063/1.4979001.

On the role of the entorhinal cortex in the effective connectivity of the hippocampal formation.

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Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, Sant Joan d'Alacant 03550, Spain.
Instituto de Física, Universidade Federal de Alagoas, Maceió, Alagoas 57072-970, Brazil.
Departamento de Ingeniería Industrial, Escuela Superior de Ingeniería y Tecnología & Instituto Universitario de Neurociencia, Universidad de La Laguna, Avda. Astrofísico Fco. Sánchez, s/n, La Laguna, Tenerife 38205, Spain.
Instituto de Fisica Interdisciplinar y Sistemas Complejos, IFISC, CSIC-UIB, Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain.


Inferring effective connectivity from neurophysiological data is a challenging task. In particular, only a finite (and usually small) number of sites are simultaneously recorded, while the response of one of these sites can be influenced by other sites that are not being recorded. In the hippocampal formation, for instance, the connections between areas CA1-CA3, the dentate gyrus (DG), and the entorhinal cortex (EC) are well established. However, little is known about the relations within the EC layers, which might strongly affect the resulting effective connectivity estimations. In this work, we build excitatory/inhibitory neuronal populations representing the four areas CA1, CA3, the DG, and the EC and fix their connectivities. We model the EC by three layers (LII, LIII, and LV) and assume any possible connection between them. Our results, based on Granger Causality (GC) and Partial Transfer Entropy (PTE) measurements, reveal that the estimation of effective connectivity in the hippocampus strongly depends on the connectivities between EC layers. Moreover, we find, for certain EC configurations, very different results when comparing GC and PTE measurements. We further demonstrate that causal links can be robustly inferred regardless of the excitatory or inhibitory nature of the connection, adding complexity to their interpretation. Overall, our work highlights the importance of a careful analysis of the connectivity methods to prevent unrealistic conclusions when only partial information about the experimental system is available, as usually happens in brain networks. Our results suggest that the combination of causality measures with neuronal modeling based on precise neuroanatomical tracing may provide a powerful framework to disambiguate causal interactions in the brain.

[Indexed for MEDLINE]

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