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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

132
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...
132
Associative Learning01:27

Associative Learning

450
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
450

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

Neuro-ocular amyloid characterization in Alzheimer's disease via cross-site PET-MRI and hierarchical cross-attention driven multimodal representation learning.

NeuroImage·2026
Same author

Advancing fair and explainable machine learning for neuroimaging dementia pattern classification in multi-racial and multi-ethnic populations.

Nature communications·2026
Same author

Relapsing Polychondritis Mimicking ANCA-Negative Granulomatosis with Polyangiitis: Diagnostic Value of <sup>18</sup>F-FDG PET/CT.

Diagnostics (Basel, Switzerland)·2026
Same author

Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes.

Brain research bulletin·2026
Same author

Prognostic significance of early cortical functional integrity measured by <sup>99m</sup>Tc-DMSA SPECT in kidney transplant recipients.

European journal of nuclear medicine and molecular imaging·2026
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

相关实验视频

Updated: Jul 23, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K

多模式多任务学习用于预测MCI转换为AD使用堆叠多项式注意网络和自适应指数衰变.

Ngoc-Huynh Ho1, Yang-Hyung Jeong2, Jahae Kim1,3

  • 1Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, South Korea.

Scientific reports
|July 11, 2023
PubMed
概括

准确预测阿尔茨海默病从轻度认知障碍 (MCI) 的进展至关重要. 这项研究开发了一个使用临床和MRI数据的多式模式框架,以区分早期的MCI (eMCI) 和晚期的MCI,并预测转化为阿尔茨海默病 (AD).

更多相关视频

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.8K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

相关实验视频

Last Updated: Jul 23, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.8K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

科学领域:

  • 神经科学是一个神经科学.
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 早期识别和治疗轻度认知障碍 (MCI) 对于延缓阿尔茨海默病 (AD) 进展和保持认知功能至关重要.
  • 准确预测MCI阶段和转化为AD是及时诊断和干预策略的必要条件.

研究的目的:

  • 开发和评估一个多式联络框架,以区分早期的MCI (eMCI) 和晚期的MCI (lMCI).
  • 为了预测MCI患者转换到阿尔茨海默病 (AD) 的时间表.
  • 通过使用基于注意力的模块,增强从小型多式联络数据集的表示学习.

主要方法:

  • 采用了利用多任务学习的多式模式框架,整合了MRI的临床数据和放射学特征.
  • 一个基于注意力的模块,堆多项式注意网络 (SPAN),被提出用于从有限的数据中进行强大的特征编码.
  • 使用自适应指数衰减 (AED) 计算了一种改善多式联络数据学习的强大因素.
  • 在阿尔茨海默病神经成像倡议 (ADNI) 队列中进行了实验,其中包括249名eMCI和427名lMCI参与者.

主要成果:

  • 拟议的多式联运战略实现了0.85的c指数得分,用于预测MCI到AD转换时间.
  • 该框架在MCI阶段分类方面表现出了很高的准确性.
  • 开发的模型的性能与当代该领域的研究相提并论.

结论:

  • 多式联络框架有效地区分MCI阶段,并预测阿尔茨海默病转化.
  • 临床数据,放射学和先进的深度学习技术 (SPAN,AED) 的整合对早期AD检测有希望.
  • 这种方法为临床决策和为MCI和AD开发个性化治疗策略提供了有价值的工具.