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• W4386781004 abstract "(1) Background and Objective: Alzheimer's disease (AD) is commonly accompanied by autonomic dysfunction. Investigating autonomic dysfunction's occurrence patterns and severity may aid in making a distinction between different dementia subtypes, as cardiac autonomic dysfunction and AD severity are correlated. Heart rate variability (HRV) allows for a non-invasive assessment of the autonomic nervous system (ANS). AD is characterized by cholinergic depletion. A computational model of ANS based on the kinetics of acetylcholine and norepinephrine is used to simulate HRV for various autonomic states. The model has the flexibility to suitably modulate the concentration of acetylcholine corresponding to different autonomic states. (2) Methods: Twenty clinically plausible AD patients are compared to 20 age- and gender-matched healthy controls using HRV measures. Statistical analysis is performed to identify the HRV parameters that vary significantly in AD. By modulating the acetylcholine concentration in a controlled manner, different autonomic states of Alzheimer's disease are simulated using the ANS model. (3) Results: In patients with AD, there is a significant decrease in vagal activity, sympathovagal imbalance with a dominant sympathetic activity, and change in the time domain, frequency domain, and nonlinear HRV characteristics. Simulated HRV features corresponding to 10 progressive states of AD are presented. (4) Conclusions: There is a significant difference in the HRV features during AD. As cholinergic depletion and autonomic dysfunction have a common neurological basis, autonomic function assessment can help in diagnosis and assessment of AD. Quantitative models may help in better comprehending the pathophysiology of the disease and assessment of its progress." @default.
• W4386781004 created "2023-09-16" @default.
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• W4386781004 date "2023-09-14" @default.
• W4386781004 modified "2023-10-17" @default.
• W4386781004 title "Investigation of Autonomic Dysfunction in Alzheimer’s Disease—A Computational Model-Based Approach" @default.
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• W4386781004 doi "https://doi.org/10.3390/brainsci13091322" @default.
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