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Updated: Jun 3, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Cognitive aging and early diagnosis challenges in Alzheimer's disease
Gabrielle B Britton1, K S J Rao
1Center for Neuroscience, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología, Panamá, República de Panamá. gbritton@indicasat.org.pa
Abstract:
Despite the profound burden of Alzheimer's disease (AD) on public health, research to understand its underlying pathology has not yet produced new therapeutic approaches to improve symptoms or halt disease progression. AD is characterized by early cognitive deficits, particularly in short-term memory, followed by a gradual decline in other cognitive functions. Functional imaging studies indicate that hippocampal and medial temporal lobe cortices are the sites of early pathology underlying the initial memory impairments. Behaviors that rely on hippocampal integrity have been the focus of extensive research using animal models and represent useful functional endpoints in pre-clinical AD research. In this review, we argue that relevant information can be derived from studying normal, aging animals performing hippocampal-sensitive tasks. Because age is the greatest risk factor for developing clinical AD, the aspects of cognitive decline occurring in normal, aging animals that resemble those seen in aging humans are reliable endpoints that can be applied to improving human therapies. Ultimately, pre-clinical studies that employ tasks sensitive to hippocampal function can be applied toward novel hypotheses in AD intervention and could provide important insights for developing early detection devices for AD patients.
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