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

669
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...
669

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

Does genetic diagnosis influence long-term seizure and developmental outcomes in childhood developmental and epileptic encephalopathies? A longitudinal audit.

Seizure·2026
Same author

Impact of valproate discontinuation on seizure control in women of reproductive age.

Epileptic disorders : international epilepsy journal with videotape·2026
Same author

Do classical semiology ("5As") predict the post-operative seizure outcome in patients with temporal lobe epilepsy?

Epileptic disorders : international epilepsy journal with videotape·2026
Same author

Long term surgical outcome and its predictors in lesional posterior cortex epilepsy.

Epilepsy research·2026
Same author

Electroclinical features and surgical outcomes in cingulate epilepsy - A single- centre experience.

Clinical neurology and neurosurgery·2026
Same author

Impact of antiseizure medications on thyroid function in persons with epilepsy.

Epilepsy & behavior : E&B·2026

相关实验视频

Updated: Sep 14, 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

1.3K

在DAVF中使用机器学习进行认知障碍分类的碎形分析.

Jithin Sivan Sulaja1, Santhosh Kumar Kannath1, Ramshekhar N Menon2

  • 1Dept. of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Medical College PO, Trivandrum, Kerala, 695011, India.

Biomedical physics & engineering express
|July 24, 2025
PubMed
概括

对大脑信号的非骨折连接性分析有效地识别了内持续动脉静脉囊 (DAVF) 患者的认知障碍,为诊断提供了一个有前途的生物标志物.

关键词:
碎形和非碎形连接性的连接性.内 DAVFF 的发生.机器学习是机器学习.休息状态 fMRI 的状态.

更多相关视频

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K
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

8.0K

相关实验视频

Last Updated: Sep 14, 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

1.3K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K
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

8.0K

科学领域:

  • 神经科学是一个神经科学.
  • 生物标志物发现发现
  • 医学成像分析 医学成像分析

背景情况:

  • 内持续性动脉静脉 (DAVFs) 是一种获得的血管异常.
  • 认知障碍是DAVF的常见症状,与大脑网络连接受损有关.
  • 休息状态功能性MRI (rsfMRI) 用于研究大脑连接,但信号中的碎形模式使分析复杂化.

研究的目的:

  • 探索非骨折连接性作为DAVF患者认知障碍的潜在生物标志物.
  • 在 BOLD 信号中隔离短内存组件以改进连接分析.
  • 使用机器学习来区分DAVF患者的认知障碍.

主要方法:

  • 50名DAVF患者和50名对照患者接受了神经心理评估和rsfMRI.
  • 波段将分解的BOLD信号转化为碎形和非碎形组件.
  • 机器学习分类器 (SVM,决策树) 被训练在连接矩阵上进行分类.

主要成果:

  • 非碎形连接性在使用SVM对认知障碍进行分类时实现了89.82%的准确性.
  • 非碎形测量方法的表现优于碎形和皮尔森相关性方法.
  • 高灵敏度 (86.54%),特异性 (92.4%) 和AUC (0.96) 为非碎形连接性获得.

结论:

  • 非碎形连接性显示为DAVF患者认知障碍的生物标志物具有前途.
  • 这种方法可能有助于早期诊断和干预DAVF相关的认知缺陷.
  • 建议使用更大的数据集进行进一步验证,以确认发现并探索更广泛的应用.