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相关概念视频

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Alzheimer's Disease: Treatment01:22

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Dementia01:30

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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
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使用基于fMRI的大脑网络识别阿尔茨海默病的深度学习框架.

Ruofan Wang1, Qiguang He1, Chunxiao Han2

  • 1School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China.

Frontiers in neuroscience
|August 24, 2023
PubMed
概括

这项研究引入了一个新的框架,将大脑网络特征和深度学习结合起来,用于阿尔茨海默病 (AD) 检测. 该方法在识别阿尔茨海默病时达到98.87%的准确性,为该疾病的发病过程提供了新的见解.

关键词:
在2D-CNN中.阿尔茨海默氏症的疾病是阿尔茨海默氏症.投资回报率 (ROI) 是指投资回报率.功能磁力共振成像 (fMRI) 是一种阶段同步指数 阶段同步指数

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科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 阿尔茨海默病 (AD) 诊断是具有挑战性的,因为不清楚的病理机制.
  • 以前的研究集中在脑网络异常或AD分类的深度学习.
  • 卷积神经网络 (CNN) 是临床诊断的突出的深度学习算法.

研究的目的:

  • 开发一种结合大脑拓特征和CNN提取的AD检测深度特征的新框架.
  • 使用静止状态功能磁共振成像 (rs-fMRI) 与相同步指数 (PSI) 和2D-CNN.
  • 在AD患者中检测异常的大脑功能连接.

主要方法:

  • 使用PSI从感兴趣区域 (ROI) 信号中构建大脑网络并提取八个拓特征.
  • 将2D-CNN应用于PSI矩阵以提取八个深度特征,分析本地和全球网络模式.
  • 使用支持矢量机 (SVM) 进行分类,对组合特征进行5倍交叉验证.

主要成果:

  • 结合PSI和2D-CNN框架,在AD检测方面实现了98.869%的分类准确度.
  • 该方法有效地识别了异常的大脑功能连接模式.
  • 提取的特征标志着AD中的大脑功能异常.

结论:

  • 拟议的框架适应性地结合了大脑网络特征,以增强AD检测.
  • 这种方法为阿尔茨海默病的潜在病原发生提供了新的见解.
  • 该框架有效地探索了网络同步和功能连接.