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P300 Latency with Memory Performance: A Promising Biomarker for Preclinical Stages of Alzheimer’s Disease

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Abstract
Detecting and tracking the preclinical stages of Alzheimer’s disease (AD) is now of particular interest due to the aging of the world’s population. AD is the most common cause of dementia, affecting the daily lives of those afflicted. Approaches in development can accelerate the evaluation of the preclinical stages of AD and facilitate early treatment and the prevention of symptom progression. Shifts in P300 amplitude and latency, together with neuropsychological assessments, could serve as biomarkers in the early screening of declines in cognitive abilities. In this study, we investigated the ability of the P300 indices evoked during a visual oddball task to differentiate pre-clinically diagnosed participants from normal healthy adults (HCs). Two preclinical stages, named asymptomatic AD (AAD) and prodromal AD (PAD), were included in this study, and a total of 79 subjects participated, including 35 HCs, 22 AAD patients, and 22 PAD patients. A mixed-design ANOVA test was performed to compare the P300 indices among groups during the processing of the target and non-target stimuli. Additionally, the correlation between these neurophysiological variables and the neuropsychological tests was evaluated. Our results revealed that neither the peak amplitude nor latency of P300 can distinguish AAD from HCs. Conversely, the peak latency of P300 can be used as a biomarker to differentiate PAD from AAD and HCs. The correlation results revealed a significant relationship between the peak latency of P300 and memory domain tasks, showing that less time-demanding neuropsychological assessments can be used. In summary, our findings showed that a combination of P300 latency and memory-requiring tasks can be used as an efficient biomarker to differentiate individuals with AAD from HCs. © 2024 by the authors.
Author(s)
Mohamed, ManalMohamed, NourelhudaKim, Jae Gwan
Issued Date
2024-12
Type
Article
DOI
10.3390/bios14120616
URI
https://scholar.gist.ac.kr/handle/local/9153
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Biosensors, v.14, no.12
ISSN
2079-6374
Appears in Collections:
Department of Biomedical Science and Engineering > 1. Journal Articles
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