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

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

Delirium in diabetic ketoacidosis: a multicenter observational study of occurrence, associated factors and outcomes from 578,204 hospitalizations.

Journal of endocrinological investigation·2026
Same author

Temporal variations, fate, and influencing factors of organophosphate esters in tropical mariculture ponds near the Northern South China Sea.

Marine pollution bulletin·2026
Same author

Isolation and Characterization of Small Extracellular Vesicles from Infrapatellar Fat Pads of Osteoarthritis Patients.

Journal of visualized experiments : JoVE·2026
Same author

Identification of COL4A2 as a Biomarker of Extracellular Matrix Remodeling and Vascular Scaffold in Choroid for Myopia.

Investigative ophthalmology & visual science·2026
Same author

Quantitative comparative analysis of different CT modalities in systemic sclerosis-associated interstitial lung disease: correlations with lung function, serological biomarkers, and disease stratification.

Journal of thoracic disease·2026
Same author

Biophysical Modeling of Thalamocortical Circuit Dynamics: Species-Specific Insights into Neural Synchrony, Sleep Spindles, and Mechanisms of Neuropsychiatric Disorders.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Jan 12, 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

结合组织病理学和机器学习的人工智能网络可以提取自闭症谱系障碍中的轴突病理.

Arash Yazdanbakhsh1,2,3, Kim T M Dang1, Kelvin Kuang1

  • 1Computational Neuroscience and Vision Laboratory, Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA.

Autism research : official journal of the International Society for Autism Research
|November 3, 2025
PubMed
概括

机器学习准确地分类自闭症谱系障碍 (ASD) 脑组织,区分白质类型. 这种自动化分析有助于研究自闭症的神经发育差异.

关键词:
前带状皮层前带状皮层卷积神经网络是一种卷积神经网络.深度神经网络是一个神经网络.远程通道是远程通道.短距离的路径是短距离的路径.白质是白色物质的组成部分.

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K
Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

25.7K

相关实验视频

Last Updated: Jan 12, 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
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K
Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

25.7K

科学领域:

  • 神经科学是一个神经科学.
  • 计算生物学 计算生物学
  • 病理学 病理学 病理学

背景情况:

  • 自闭症谱系障碍 (ASD) 的特点是与神经类型对照 (CTR) 相比,皮质通路中的轴突组织不同.
  • 对死后人类大脑组织的传统分析是劳动密集型和耗时的,限制了大脑区域的系统研究.

研究的目的:

  • 开发一种机器学习方法,用于自动分类ASD和CTR大脑中的白质.
  • 区分表面白质 (SWM) 和深白质 (DWM) 途径.

主要方法:

  • 一个深度神经网络被训练来分类来自ASD和CTR个体的微观白质部分.
  • 该模型考虑了不同的白质区域:SWM (短距离连接) 和DWM (远距离通路).
  • 灵敏度图和多维缩放被用于对分类和病理标志物的分析.

主要成果:

  • 深度神经网络在分类ASD与CTR白质下面的前带皮层 (ACC) 中达到98%的准确性.
  • 该模型区分了SWM和DWM路径组成,平均准确率高达80%.
  • 分析确定了ASD中的关键病理标志物,并突出了ASD频谱内的异质性.

结论:

  • 机器学习为死后脑组织的高分辨率显微镜分析提供了一个自动化解决方案.
  • 这种方法可以系统地研究健康和疾病中的白质,进步我们对神经发育障碍的理解.
  • 这些发现有助于理解ASD异质性,并与神经类型大脑特征重叠.