Altered slow (<1 Hz) and fast (beta and gamma) neocortical oscillations in the 3xTg-AD mouse model of Alzheimer's disease under anesthesia

The 3xTg-AD mouse model reproduces the main features associated with the etiology of familial Alzheimer's disease (AD). To investigate whether these features imply functional cortical network alterations and their evolution with age, we studied spontaneous slow oscillations, activity that integ...

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Detalles Bibliográficos
Autores: Castano-Prat, Patricia, Perez Mendez, Lorena, Perez-Zabalza, María|||0000-0002-3194-7796, Sanfeliu i Pujol, Coral|||0000-0002-9004-1206, Gimenez-Llort, Lydia|||0000-0002-4091-489X, Sanchez Vives, Maria V.
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:223091
Acceso en línea:https://ddd.uab.cat/record/223091
https://dx.doi.org/urn:doi:10.1016/j.neurobiolaging.2019.02.009
Access Level:acceso abierto
Descripción
Sumario:The 3xTg-AD mouse model reproduces the main features associated with the etiology of familial Alzheimer's disease (AD). To investigate whether these features imply functional cortical network alterations and their evolution with age, we studied spontaneous slow oscillations, activity that integrates cellular and network properties. We quantified different parameters of the emergent slow oscillations-alternating Up and Down states-and of the embedded beta-gamma rhythms of 3xTg-AD and wild-type mice at 7 and 20 months of age. Most group differences occurred at 20 months of age: 3xTg-AD mice presented lower oscillatory frequency, higher cycle variability, and reduced relative (Up/Down) firing rate with respect to controls. The high-frequency analysis revealed a shift toward lower frequencies in older 3xTg-AD animals, reminiscent of one of the electroencephalography hallmarks of patients with AD. This first systematic characterization of the cortical emergent rhythms in 3xTg-AD strain provides insights into the network mechanisms underlying associated network activity alterations.