Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

490
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...
490
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

195
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...
195
Dementia01:30

Dementia

115
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....
115
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Genetic variants of p21 and p27 and hepatocellular cancer risk in a Chinese Han population: a case-control study.

International journal of cancer·2012
Same author

Inhibition of TGF-β/Smad signaling by BAMBI blocks differentiation of human mesenchymal stem cells to carcinoma-associated fibroblasts and abolishes their protumor effects.

Stem cells (Dayton, Ohio)·2012
Same author

MAIGO2 is involved in abscisic acid-mediated response to abiotic stresses and Golgi-to-ER retrograde transport.

Physiologia plantarum·2012
Same author

The internal dynamics of mini c TAR DNA probed by electron paramagnetic resonance of nitroxide spin-labels at the lower stem, the loop, and the bulge.

Biochemistry·2012
Same author

Electrochemical depassivation of zero-valent iron for trichloroethene reduction.

Journal of hazardous materials·2012
Same author

Derivation of quantum work equalities using a quantum Feynman-Kac formula.

Physical review. E, Statistical, nonlinear, and soft matter physics·2012

相关实验视频

Updated: Jul 5, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

阿尔茨海默病预测算法基于脱相关性约束和多模式特征相互作用.

Jiayuan Cheng1, Huabin Wang1, Shicheng Wei2

  • 1Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.

Computers in biology and medicine
|January 17, 2024
PubMed
概括

这项研究使用大脑成像数据引入了一种新的阿尔茨海默病 (AD) 预测模型. 该模型有效地整合了氧葡萄糖正子发射断层扫描 (FDG-PET) 和磁共振成像 (MRI) 功能,实现了高预测准确性.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.脱相关性约束的限制磁共振成像技术 磁共振成像技术多模式功能交互的多模式功能交互.定子发射断层扫描 (PET) 是一种定子发射断层扫描.

更多相关视频

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

相关实验视频

Last Updated: Jul 5, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.9K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

科学领域:

  • 神经成像是一种神经成像.
  • 人工智能的人工智能
  • 生物医学数据分析

背景情况:

  • 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病.
  • 多模式脑成像,包括FDG-PET和MRI,提供了对AD病理学的补充见解.
  • 整合多式联运数据由于不同特征空间而存在挑战,可能会阻碍预测准确性.

研究的目的:

  • 开发一个先进的阿尔茨海默病预测模型.
  • 为了有效地解决多式脑成像数据中的特征空间差异.
  • 加强FDG-PET和MRI之间的互补信息的利用,以改善AD预测.

主要方法:

  • 一种新的AD预测模型,结合了脱相关性约束和多模式特征交互.
  • 用剩余连接和注意力机制为FDG-PET和MRI数据进行特征提取.
  • 一个相互关注的功能融合模块,旨在增强模式间的功能交互和自适应加权.

主要成果:

  • 拟议的模型在区分阿尔茨海默病 (AD),轻度认知障碍 (MCI) 和正常认知 (NC) 方面实现了86.79%的预测准确度.
  • 与现有的多模式AD预测模型相比,表现出优越的性能.
  • 脱相关性约束和相互注意力融合有效地提高了模型利用不同成像模式的互补信息的能力.

结论:

  • 开发的模型有效地整合了FDG-PET和MRI数据,以准确预测阿尔茨海默病.
  • 拟议的脱相关性约束和相互关注融合策略是克服多式联运数据挑战的关键.
  • 这种方法显示出改善早期诊断和预测阿尔茨海默病的巨大潜力.