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      A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

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          Abstract

          Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirically observed spontaneous and stimulus-evoked dynamics in space, time, phase, and frequency domains. Large-scale properties of cortical dynamics are shown to emerge from both microscopic-scale adaptation that control transitions between wake-like to sleep-like activity, and the organization of the human structural connectome; together, they shape the spatial extent of synchrony and phase coherence across brain regions consistent with the propagation of sleep-like spontaneous traveling waves at intermediate scales. Remarkably, the model also reproduces brain-wide, enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.

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          Most cited references48

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          Breakdown of cortical effective connectivity during sleep.

          When we fall asleep, consciousness fades yet the brain remains active. Why is this so? To investigate whether changes in cortical information transmission play a role, we used transcranial magnetic stimulation together with high-density electroencephalography and asked how the activation of one cortical area (the premotor area) is transmitted to the rest of the brain. During quiet wakefulness, an initial response (approximately 15 milliseconds) at the stimulation site was followed by a sequence of waves that moved to connected cortical areas several centimeters away. During non-rapid eye movement sleep, the initial response was stronger but was rapidly extinguished and did not propagate beyond the stimulation site. Thus, the fading of consciousness during certain stages of sleep may be related to a breakdown in cortical effective connectivity.
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            Neural correlates of consciousness: progress and problems.

            There have been a number of advances in the search for the neural correlates of consciousness--the minimum neural mechanisms sufficient for any one specific conscious percept. In this Review, we describe recent findings showing that the anatomical neural correlates of consciousness are primarily localized to a posterior cortical hot zone that includes sensory areas, rather than to a fronto-parietal network involved in task monitoring and reporting. We also discuss some candidate neurophysiological markers of consciousness that have proved illusory, and measures of differentiation and integration of neural activity that offer more promising quantitative indices of consciousness.
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              Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

              We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. We describe a systematic method to estimate its parameters with simple electrophysiological protocols (current-clamp injection of pulses and ramps) and apply it to a detailed conductance-based model of a regular spiking neuron. Our simple model predicts correctly the timing of 96% of the spikes (+/-2 ms) of the detailed model in response to injection of noisy synaptic conductances. The model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro.
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                Author and article information

                Contributors
                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                13 January 2023
                2022
                : 16
                : 1058957
                Affiliations
                [1] 1CNRS, Institute of Neuroscience (NeuroPSI), Paris-Saclay University , Saclay, France
                [2] 2Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM , Marseille, France
                [3] 3Laboratoire de Physique, Ecole Normale Supérieure, Université PSL, CNRS, Sorbonne Université, Université de Paris , Paris, France
                Author notes

                Edited by: Fernando Soler-Toscano, Sevilla University, Spain

                Reviewed by: Federico Stella, Radboud University, Netherlands; Yu Zhang, Zhejiang Lab, China

                *Correspondence: Jennifer S. Goldman ✉ jennifer.goldman@ 123456mail.mcgill.ca
                Article
                10.3389/fncom.2022.1058957
                9880280
                36714530
                f7429550-796e-47b6-9311-f691c205f8f7
                Copyright © 2023 Goldman, Kusch, Aquilue, Yalçınkaya, Depannemaecker, Ancourt, Nghiem, Jirsa and Destexhe.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 September 2022
                : 21 December 2022
                Page count
                Figures: 7, Tables: 1, Equations: 20, References: 48, Pages: 17, Words: 10687
                Funding
                Funded by: European Commission, doi 10.13039/501100000780;
                Award ID: H2020-785907
                Award ID: H2020-945539
                This research was funded by the European Community (Human Brain Project, H2020-785907, and H2020-945539), the Centre National de la Recherche Scientifique (CNRS, France), the ANR PARADOX, and the ICODE excellence network.
                Categories
                Neuroscience
                Original Research

                Neurosciences
                neural simulation,mean-field model,spontaneous activity,evoked responses,wake,synchronous,slow-wave sleep,human brain

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