Spontaneous persistent activity and inactivity in vivo reveals differential cortico-entorhinal functional connectivity

• Published in: Nature communications Vol. 15; no. 1; p. 3542
• Main Authors: Choudhary, Krishna, Berberich, Sven, Hahn, Thomas T. G., McFarland, James M., Mehta, Mayank R.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.05.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116/2393; 631/378/1595/1554; 631/378/3920; 639/766/747; 9/30; 9/74; Animals; Computational neuroscience; Computer Simulation; Connectome; Cortex (entorhinal); Entorhinal Cortex - physiology; Humanities and Social Sciences; In vivo methods and tests; Iterative methods; Male; Membrane potential; Membrane Potentials - physiology; Mice; Models, Neurological; multidisciplinary; Nerve Net - physiology; Neural networks; Neural Pathways - physiology; Neurons; Neurons - physiology; Neurosciences; Oscillations; Physics; Psychotherapy; Science; Science (multidisciplinary); Substrates
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-47617-6

Understanding the functional connectivity between brain regions and its emergent dynamics is a central challenge. Here we present a theory-experiment hybrid approach involving iteration between a minimal computational model and in vivo electrophysiological measurements. Our model not only predicted spontaneous persistent activity (SPA) during Up-Down-State oscillations, but also inactivity (SPI), which has never been reported. These were confirmed in vivo in the membrane potential of neurons, especially from layer 3 of the medial and lateral entorhinal cortices. The data was then used to constrain two free parameters, yielding a unique, experimentally determined model for each neuron. Analytic and computational analysis of the model generated a dozen quantitative predictions about network dynamics, which were all confirmed in vivo to high accuracy. Our technique predicted functional connectivity; e. g. the recurrent excitation is stronger in the medial than lateral entorhinal cortex. This too was confirmed with connectomics data. This technique uncovers how differential cortico-entorhinal dialogue generates SPA and SPI, which could form an energetically efficient working-memory substrate and influence the consolidation of memories during sleep. More broadly, our procedure can reveal the functional connectivity of large networks and a theory of their emergent dynamics. Cortico-entorhinal interactions remain poorly understood. Here, the authors demonstrate that a model of interacting networks predicts spontaneous persistent activity and inactivity in the medial, but not lateral, entorhinal cortex in vivo.