Touchscreen-based location discrimination and paired associate learning tasks detect cognitive impairment at an early stage in an App knock-in mouse model of Alzheimer’s disease

MOLECULAR BRAIN(2020)

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摘要
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline with accumulation of amyloid beta (Aβ) and neurofibrillary tangles that usually begins 15–30 years before clinical diagnosis. Rodent models that recapitulate aggressive Aβ and/or the pathology of neurofibrillary tangles are essential for AD research. Accordingly, non-invasive early detection systems in these animal models are required to evaluate the phenotypic changes, elucidate the mechanism of disease progression, and facilitate development of novel therapeutic approaches. Although many behavioral tests efficiently reveal cognitive impairments at the later stage of the disease in AD models, it has been challenging to detect such impairments at the early stage. To address this issue, we subjected 4–6-month-old male App NL − G − F/NL − G − F knock-in ( App -KI) mice to touchscreen-based location discrimination (LD), different object–location paired-associate learning (dPAL), and reversal learning tests, and compared the results with those of the classical Morris water maze test. These tests are mainly dependent on the brain regions prone to Aβ accumulation at the earliest stages of the disease. At 4–6 months, considered to represent the early stage of disease when mice exhibit initial deposition of Aβ and slight gliosis, the classical Morris water maze test revealed no difference between groups, whereas touchscreen-based LD and dPAL tasks revealed significant impairments in task performance. Our report is the first to confirm that a systematic touchscreen-based behavioral test battery can sensitively detect the early stage of cognitive decline in an AD-linked App -KI mouse model. This system could be applied in future translational research.
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关键词
Alzheimer&apos, s disease, Touchscreen, Amyloid beta, Early stage, App-KI
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