Impairment of LTP, LTD and Metaplasticity During the Early Stages of Alzheimer'S Disease: a Computational Study

Azam Shirrafiardekani, Hany Alashwal*, Ahmed A. Moustafa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Alzheimer's disease is a common form of dementia that is caused by the accumulation of a protein called amyloid beta in the brain. This protein impairs certain mechanisms of information processing in the brain, such as long-term potentiation (LTP) and long-term depression (LTD), which play a critical role in the development of the disease. The exact mechanism underlying this effect is not yet known. In this study, a computational model is used to investigate the effect of blocking various potassium channels on the impairment of LTP and LTD during the early stages of Alzheimer's disease. The results suggest that the blockage of specific potassium channels during the early stages of Alzheimer's disease can lead to a reduction in LTP and an increase in LTD induction in dentate granule cells, which accelerates the development of the disease. These findings have implications for the treatment of Alzheimer's disease.

Original languageEnglish
Title of host publicationProceedings of 2023 International Conference on Machine Learning and Cybernetics
PublisherIEEE Computer Society
Pages165-171
Number of pages7
ISBN (Electronic)9798350303780
ISBN (Print)9798350303797
DOIs
Publication statusPublished - Jul 2023
Event2023 International Conference on Machine Learning and Cybernetics - Adelaide, Australia
Duration: 9 Jul 202311 Jul 2023

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference2023 International Conference on Machine Learning and Cybernetics
Abbreviated titleICMLC 2023
Country/TerritoryAustralia
CityAdelaide
Period9/07/2311/07/23

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