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Sleep–Wake Cycle and EEG–Based Biomarkers during Late Neonate to Adult Transition

During the transition from neonate to adulthood, brain maturation establishes coherence between behavioral states—wakefulness, non-rapid eye movement, and rapid eye movement sleep. In animal models few studies have characterized and analyzed cerebral rhythms and the sleep–wake cycle in early ages, i...

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Detalles Bibliográficos
Autores principales: Herrero, Miguel A., Gallego, Rebeca, Ramos, Milagros, Lopez, Juan Manuel, de Arcas, Guillermo, Gonzalez-Nieto, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7996792/
https://www.ncbi.nlm.nih.gov/pubmed/33673399
http://dx.doi.org/10.3390/brainsci11030298
Descripción
Sumario:During the transition from neonate to adulthood, brain maturation establishes coherence between behavioral states—wakefulness, non-rapid eye movement, and rapid eye movement sleep. In animal models few studies have characterized and analyzed cerebral rhythms and the sleep–wake cycle in early ages, in relation to adulthood. Since the analysis of sleep in early ages can be used as a predictive model of brain development and the subsequent emergence of neural disturbances in adults, we performed a study on late neonatal mice, an age not previously characterized. We acquired longitudinal 24 h electroencephalogram and electromyogram recordings and performed time and spectral analyses. We compared both age groups and found that late neonates: (i) spent more time in wakefulness and less time in non-rapid eye movement sleep, (ii) showed an increased relative band power in delta, which, however, reduced in theta during each behavioral state, (iii) showed a reduced relative band power in beta during wakefulness and non-rapid eye movement sleep, and (iv) manifested an increased total power over all frequencies. The data presented here might have implications expanding our knowledge of cerebral rhythms in early ages for identification of potential biomarkers in preclinical models of neurodegeneration.