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

Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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

Fracture risk prediction using bone mineral density and biochemical markers of bone and mineral metabolism in dialysis and non-dialysis CKD patients.

BMC nephrology·2026
Same author

Wearable Myoelectric Interface for Neurorehabilitation (MINT) to Recover Arm Activity After Stroke: A Randomized Controlled Trial.

Neurorehabilitation and neural repair·2026
Same author

Prevalence and Global Distribution of Bacterial Species Associated with Acute Otitis Media in Children: Systematic Review and Meta-Analysis.

Antibiotics (Basel, Switzerland)·2026
Same author

N2G calibrator: a cross-subject adversarial learning framework for neural signal-driven gait tracking in Parkinson's disease.

Communications engineering·2026
Same author

Pontine Microtubular Signal Intensity in Hemifacial Spasm: Association with Outcome After Microvascular Decompression Surgery.

Life (Basel, Switzerland)·2026
Same author

Common Electrophysiology Biomarkers Collected at Home Robustly Track Depression Recovery With Deep Brain Stimulation.

medRxiv : the preprint server for health sciences·2026
Same journal

Cerebellum crus II cortical thickness as a cognitive brain reserve in Parkinson's disease.

Parkinsonism & related disorders·2026
Same journal

Serious games and virtual reality in Parkinson's disease rehabilitation: A systematic review of randomized controlled trials.

Parkinsonism & related disorders·2026
Same journal

Respiratory-swallow training in Parkinson's disease: Effects of a single session on swallowing safety and efficiency with exploratory comparisons of biofeedback.

Parkinsonism & related disorders·2026
Same journal

Levodopa responsive childhood-onset generalized dystonia with diaphragmatic involvement associated with an SPTBN2 variant.

Parkinsonism & related disorders·2026
Same journal

Approach to a case of Neuro-Gonadal disorder: Perrault syndrome.

Parkinsonism & related disorders·2026
Same journal

Parakinesia in Chorea: Why attribution of intention is misleading.

Parkinsonism & related disorders·2026
查看所有相关文章

相关实验视频

Updated: May 8, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

8.8K

关闭DBS中的循环:以数据为导向的方法

Prerana Acharyya1, Kerry W Daley1, Jin Woo Choi1

  • 1Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.

Parkinsonism & related disorders
|March 4, 2025
PubMed
概括
此摘要是机器生成的。

适应性深度大脑刺激 (aDBS) 通过使用人工智能来解释神经信号来提供个性化的帕金森病治疗. 未来集成多种数据类型的系统有望增强运动障碍的治疗效果.

更多相关视频

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.1K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K

相关实验视频

Last Updated: May 8, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

8.8K
Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.1K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.2K

科学领域:

  • 神经学 神经学
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 深度大脑刺激 (DBS) 是治疗像帕金森病 (PD) 这样的运动障碍的基石.
  • 适应性DBS (aDBS) 代表了一项进步,利用闭环系统进行个性化神经调节.
  • 创新正在推动aDBS技术及其治疗应用的发展.

研究的目的:

  • 审查最近适应性深度大脑刺激 (aDBS) 的进展.
  • 分析aDBS生物标志物检测,控制策略和疗效机制方面的进展.
  • 探索人工智能 (AI) 在解码aDBS的运动状态中的作用.

主要方法:

  • 对适应式DBS技术和应用的当前文献的审查.
  • 对数据驱动方法的分析,包括用于神经信号处理的AI.
  • 研究用于增强生物标志物检测的多模式信号集成.

主要成果:

  • 人工智能驱动的方法将生物标志物检测扩展到传统的皮质下β振荡之外.
  • 利用多种神经和动力学信号改善了运动状态解码.
  • 多模式输入系统显示了在aDBS中更广泛的症状管理的潜力.

结论:

  • 适应性DBS在个性化治疗运动障碍方面具有显著的前景.
  • 人工智能和多模式传感是推动aDBS有效性的关键.
  • 需要进一步的研究,以克服临床使用的技术和计算挑战.