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

Exercise Stress Test01:26

Exercise Stress Test

1.1K
Introduction
Exercise stress testing, commonly known as a treadmill test, is a noninvasive procedure used to evaluate cardiovascular function and diagnose heart conditions.
Definition
An exercise stress test measures the heart's response to exertion using a treadmill or stationary bicycle. Chest electrodes record the heart's electrical activity through an ECG, and blood pressure is monitored regularly.
Purposes
1.1K

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

Mobgap: A State-of-the-Art Python Framework for Reproducible Estimation and Algorithm Validation of Digital Mobility Outcomes from a Single Wearable Device.

Sensors (Basel, Switzerland)·2026
Same author

Steps against the burden of Parkinson's disease (StepuP): Protocol of a randomized controlled trial elucidating the biomechanical and neurophysiological mechanisms of a speed dependent treadmill training intervention.

PloS one·2026
Same author

A Practice Framework for Genetic Testing in Asymptomatic Relatives of Patients With Creutzfeldt-Jakob Disease: Experience and Insights From Israel.

European journal of neurology·2026
Same author

Essential genetic testing in movement disorders - results from a Delphi study.

Parkinsonism & related disorders·2026
Same author

Prefrontal and Occipital Network Excitability Differences in Dementia with Lewy Bodies and Alzheimer's Disease.

Clinical EEG and neuroscience·2026
Same author

Correction: The association between amyloid-beta deposition on dual-task gait performance is partially moderated by cognitive functions in healthy older adults.

Scientific reports·2026

相关实验视频

Updated: Jan 11, 2026

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients
07:42

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

3.6K

使用一种新的多式压力测试来量化运动认知储备.

Tal Kozlovski1,2, Inbal Maidan1,3,4, Eran Gazit5

  • 1Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Neurological Institute, Tel Aviv 6423906, Israel.

Brain communications
|November 17, 2025
PubMed
概括

一个新的运动认知储备 (MCR) 指数使用虚拟现实压力测试准确量化了联合运动和认知储备. 这种经过验证的工具在检测神经系统缺陷和预测衰退方面表现出高灵敏度.

关键词:
认知储备是一个认知储备.发动机储备 发动机储备神经退行症的神经退行症压力测试是一种压力测试.

更多相关视频

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K
Assessment of Stress Effects on Cognitive Flexibility using an Operant Strategy Shifting Paradigm
07:26

Assessment of Stress Effects on Cognitive Flexibility using an Operant Strategy Shifting Paradigm

Published on: May 4, 2020

3.9K

相关实验视频

Last Updated: Jan 11, 2026

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients
07:42

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

3.6K
Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.9K
Assessment of Stress Effects on Cognitive Flexibility using an Operant Strategy Shifting Paradigm
07:26

Assessment of Stress Effects on Cognitive Flexibility using an Operant Strategy Shifting Paradigm

Published on: May 4, 2020

3.9K

科学领域:

  • 神经科学是一个神经科学.
  • 老年学是一门学科.
  • 生物医学工程 生物医学工程

背景情况:

  • 生理储备能力对于苛刻的情况至关重要,但量化运动和认知储备 (MCR) 是一个挑战.
  • 像教育和大脑容量这样的当前代理是间接的和有限的.
  • 现有的运动和认知储备的定义被人为地分开了,忽视了它们在日常功能中的综合性.

研究的目的:

  • 评估一种新型分级运动认知压力测试的有效性,该测试旨在量化联合运动和认知储备 (MCR).
  • 使用机器学习算法和可穿戴传感器数据开发和验证新的MCR指数得分.

主要方法:

  • 一组144名参与者 (18-85岁) 具有不同的储备能力,包括健康的个人和患有帕金森病,阿尔茨海默病,勒维体痴呆症和轻度认知障碍的人.
  • 参与者进行了基于虚拟现实的压力测试,涉及跑步机上行走与并发的运动和认知挑战.
  • 一个半监督的机器学习算法集成了性能数据和可穿戴传感器指标,以生成MCR指数得分.

主要成果:

  • 该MCR指数表现出强大的面部有效性,随着运动和认知挑战的增加,性能显著下降 (P < 0.001).
  • 该指数准确地区分了健康对照和患有神经疾病的个体 (AUC = 0.89),表现优于现有的代理.
  • 在MCR指数和已确定的MCR代理 (0.56 ≤ r ≤ 0.79) 和MRI衍生的大脑体积 (灰质,白质,尾状核,下额头) 之间发现了显著的相关性.

结论:

  • 新的MCR指数是量化个体运动和认知储备的有效和敏感工具.
  • 这种创新方法可以帮助查运动和认知缺陷.
  • 该MCR指数具有预测神经退行性疾病中的运动和认知衰退的潜力.