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

Brain Imaging01:14

Brain Imaging

663
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
663
Sampling Methods: Overview01:06

Sampling Methods: Overview

2.4K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
2.4K
Neural Circuits01:25

Neural Circuits

2.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.6K

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

Outcomes and Cost-Effectiveness of an Interdisciplinary Clinic for Functional Neurologic Disorders.

Neurology. Clinical practice·2026
Same author

The Associations of Cerebral Blood Flow and White Matter Hyperintensities with Tau and Amyloid-beta Across the Alzheimer's Disease Spectrum.

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

Development and validation of a dementia risk prediction model for low- and middle-income countries: the 10/66 study.

American journal of epidemiology·2026
Same author

Ultra-rare variants in LAMA2 are risk factors for frontotemporal dementia and motor neuron disease.

Human molecular genetics·2026
Same author

Subjective Sleep Traits and Cognition Across Mid- to Late-Adulthood: A Cross-Sectional Study of Gene-Environment Interplay.

Sleep·2026
Same author

Fermented Dairy Food Intake and Risk of Depression and Dementia in Later Life: Findings from a Prospective Cohort of Older Australians.

Nutrients·2026

相关实验视频

Updated: Jan 16, 2026

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

1.7K

一个预采样条件变异自编码器用于神经成像规范建模:对统计方法进行深度学习的基准测试.

Mai P Ho1, Yang Song2, Perminder S Sachdev1,3

  • 1Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales (UNSW), Sydney, NSW, Australia.

Imaging neuroscience (Cambridge, Mass.)
|January 15, 2026
PubMed
概括

这项研究引入了用于脑成像分析的先进深度学习框架,提供了对个体脑部偏差的更可靠的预测,并提高了对高血压严重性的敏感性.

关键词:
英国生物银行有条件变化的自动编码器.深度学习是一种深度学习.规范性建模 规范性建模精确精神病学是一门精确的精神病学.

更多相关视频

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

600
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K

相关实验视频

Last Updated: Jan 16, 2026

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

1.7K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

600
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.0K

科学领域:

  • 神经成像是一种神经成像.
  • 机器学习 机器学习
  • 生物统计学 生物统计学

背景情况:

  • 规范模型使用共变量量化个体大脑偏差.
  • 深度学习推进了神经成像中的多变量分析.
  • 现有的条件变量自编码器 (cVAEs) 难以进行可靠的概率预测.

研究的目的:

  • 开发一个增强的cVAE框架,以改善神经成像中的规范建模.
  • 为了利用深度学习来进行大脑成像中的高维数据分析.
  • 准确捕捉与高血压严重程度相关的个体偏差.

主要方法:

  • 提议一个增强的cVAE框架与预先采样推断.
  • 从英国生物库参与者 (高血压和正常血压) 中利用了195种成像衍生型态 (IDP).
  • 与GAMLSS,MFPR,HBR和标准cVAE方法进行基准测试.

主要成果:

  • 增强的cVAE框架显示了与既有模型相比的性能.
  • 该模型准确地捕获了与高血压严重程度相关的个体偏差.
  • 与现有的cVAE方法相比,拟议的推理策略显示出更高的共同变量灵敏度.

结论:

  • 基于深度学习的规范建模显示了复杂的神经成像数据集的前景.
  • 增强的cVAE框架为个性化大脑健康评估提供了一个强大的工具.
  • 这种方法有助于早期发现与高血压等疾病相关的神经疾病.