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Comput. Neurosci."],"abstract":"<jats:p>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 <jats:italic>via<\/jats:italic> 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.<\/jats:p>","DOI":"10.3389\/fncom.2022.1058957","type":"journal-article","created":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T05:27:38Z","timestamp":1673587658000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":50,"title":["A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics"],"prefix":"10.3389","volume":"16","author":[{"given":"Jennifer S.","family":"Goldman","sequence":"first","affiliation":[]},{"given":"Lionel","family":"Kusch","sequence":"additional","affiliation":[]},{"given":"David","family":"Aquilue","sequence":"additional","affiliation":[]},{"given":"Bahar Hazal","family":"Yal\u00e7\u0131nkaya","sequence":"additional","affiliation":[]},{"given":"Damien","family":"Depannemaecker","sequence":"additional","affiliation":[]},{"given":"Kevin","family":"Ancourt","sequence":"additional","affiliation":[]},{"given":"Trang-Anh E.","family":"Nghiem","sequence":"additional","affiliation":[]},{"given":"Viktor","family":"Jirsa","sequence":"additional","affiliation":[]},{"given":"Alain","family":"Destexhe","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.1152\/jn.00686.2005","article-title":"Adaptive exponential integrate-and-fire model as an effective description of neuronal activity","volume":"94","author":"Brette","year":"2005","journal-title":"J. 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