Global sleep homeostasis reflects temporally and spatially integrated local cortical neuronal activity
| Autor: | Thomas, Christopher W, Guillaumin, Mathilde C C, McKillop, Laura E, Achermann, Peter, Vyazovskiy, Vladyslav V |
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| Přispěvatelé: | University of Zurich, Vyazovskiy, Vladyslav V |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
Mouse Computer science Neuronal firing 10050 Institute of Pharmacology and Toxicology Electroencephalography 0302 clinical medicine 2400 General Immunology and Microbiology Homeostasis Premovement neuronal activity Biology (General) media_common Cerebral Cortex Neurons 0303 health sciences medicine.diagnostic_test General Neuroscience 2800 General Neuroscience General Medicine cortex Medicine Research Article Vigilance (psychology) QH301-705.5 media_common.quotation_subject Science sleep homeostasis 610 Medicine & health Genetics and Molecular Biology neuronal dynamics Models Biological General Biochemistry Genetics and Molecular Biology 03 medical and health sciences 1300 General Biochemistry Genetics and Molecular Biology medicine Animals mathematical modelling Wakefulness sleep 030304 developmental biology General Immunology and Microbiology firing rate homeostasis 10074 The KEY Institute for Brain-Mind Research Rats Mice Inbred C57BL 10054 Clinic for Psychiatry Psychotherapy and Psychosomatics General Biochemistry Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | eLife, Vol 9 (2020) eLife |
| Popis: | Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define ‘Process S’, a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5–4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space. |
| Databáze: | OpenAIRE |
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