Dissociable cognitive impairments in two strains of transgenic Alzheimer’s disease mice revealed by a battery of object-based tests

Object recognition tasks detect cognitive deficits in transgenic Alzheimer’s disease (AD) mouse models. Object recognition, however, is not a unitary process, and there are many uncharacterized facets of object processing with relevance to AD. We therefore systematically evaluated object processing...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scientific reports 2019-01, Vol.9 (1), p.57-57, Article 57
Hauptverfasser: Creighton, Samantha D., Mendell, Ari L., Palmer, Daniel, Kalisch, Bettina E., MacLusky, Neil J., Prado, Vania F., Prado, Marco A. M., Winters, Boyer D.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Object recognition tasks detect cognitive deficits in transgenic Alzheimer’s disease (AD) mouse models. Object recognition, however, is not a unitary process, and there are many uncharacterized facets of object processing with relevance to AD. We therefore systematically evaluated object processing in 5xFAD and 3xTG AD mice to clarify the nature of object recognition-related deficits. Twelve-month-old male and female 5xFAD and 3xTG mice were assessed on tasks for object identity recognition, spatial recognition, and multisensory object perception. Memory and multisensory perceptual impairments were observed, with interesting dissociations between transgenic AD strains and sex that paralleled neuropathological changes. Overreliance on the widespread “object recognition” task threatens to slow discovery of potentially significant and clinically relevant behavioural effects related to this multifaceted cognitive function. The current results support the use of carefully designed object-based test batteries to clarify the relationship between “object recognition” impairments and specific aspects of AD pathology in rodent models.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-37312-0