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

Aging01:26

Aging

188
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
188
The Effect of Aging on Tissues01:19

The Effect of Aging on Tissues

2.5K
Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
2.5K

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

Cardiovascular risk and hippocampal-cognitive coupling in Alzheimer's disease.

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

A digital twin methodology using retrospective patient data for sample size reduction in Alzheimer's disease clinical trials.

Alzheimer's research & therapy·2026
Same author

Ventricular enlargement is associated with early Alzheimer's disease pathophysiology.

Brain communications·2026
Same author

Choroidal-ventricular system abnormalities are linked to amyloid-β aggregation in Alzheimer's disease.

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

Quantitative susceptibility mapping of the brain is associated with inflammatory changes in Alzheimer's disease related areas.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Biomarkers.

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

相关实验视频

Updated: Sep 15, 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

大脑年龄预测:深度模型需要一只手来概括.

Reza Rajabli1, Mahdie Soltaninejad1, Vladimir S Fonov1

  • 1McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.

Human brain mapping
|July 16, 2025
PubMed
概括

使用深度学习模型预测大脑年龄显示出理解大脑衰老的前景. 这项研究通过改进数据预处理和培训技术,显著减少了预测错误,提高了临床适用性.

关键词:
按T1加权的MRI测试结果.大脑年龄预测预测深度学习是一种深度学习.可以概括的概括性.强度 坚固性 坚固性

更多相关视频

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine
08:53

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

1.2K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

881

相关实验视频

Last Updated: Sep 15, 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
Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine
08:53

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

1.2K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

881

科学领域:

  • 神经成像是一种神经成像.
  • 人工智能的人工智能
  • 医学诊断 医学诊断 医学诊断

背景情况:

  • 从T1加权MRI中预测大脑年龄是大脑衰老的关键标志物.
  • 深度学习模型显示出潜力,但由于训练数据有限和模型复杂性,难以将新数据推广为新数据.
  • 在培训和未见的数据性能之间经常存在概括差距.

研究的目的:

  • 评估SFCN-reg深度学习模型用于大脑年龄预测.
  • 为了解决大脑年龄预测模型中的概括差距.
  • 提高神经成像中深度学习的临床适用性.

主要方法:

  • 使用基于VGG-16的深度模型 (SFCN-reg).
  • 采用了全面的预处理,广泛的数据增强和模型规范化.
  • 使用英国生物银行数据训练模型.

主要成果:

  • 减少了ADNI数据集 (2.79年) 中平均绝对误差 (MAE) 的47%的概括.
  • 在AIBL数据集 (3.75年) 上减少了12%的概括MAE.
  • 实现了高达13%的扫描-重新扫描误差 (0.70年) 的降低,并提高了强度.

结论:

  • 高质量的预处理和强大的训练对于准确预测大脑年龄至关重要.
  • 这项研究表明了缩小临床使用的概括差距的途径.
  • 改进的模型增强了神经成像研究和大脑年龄预测的临床应用.