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

Neural Circuits01:25

Neural Circuits

3.0K
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...
3.0K
Block Diagram Reduction01:22

Block Diagram Reduction

720
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
720
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

712
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
712

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

Multi-metric evaluations of acute psychedelic effects on fMRI brain entropy.

Nature communications·2026
Same author

Modeling roles and trade-offs in multiplex networks.

Nature communications·2026
Same author

Optimal Variable Flip Angle Schemes for Hyperpolarized MR Kinetic Modeling Robust to RF Field Variations.

NMR in biomedicine·2025
Same author

Mapping cell density and hypoxia in glioblastoma using time-dependent diffusion MRI: improved cell density assessment compared to conventional diffusion metrics.

Physics in medicine and biology·2025
Same author

Assessing tumor microstructure with time-dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI-Linac.

Medical physics·2024
Same author

Probabilistic PARAFAC2.

Entropy (Basel, Switzerland)·2024
Same journal

Cortical similarity networks in the rat brain: Postnatal development and sensitivity to early life stress.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Restoring oscillatory dynamics in Alzheimer's disease: A laminar whole-brain model of serotonergic psychedelic effects.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Distributed cortical network dynamics of binocular convergent eye movements in humans.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

High-resolution Bayesian Virtual Epileptic Patient using neural field models.

Network neuroscience (Cambridge, Mass.)·2026
查看所有相关文章

相关实验视频

Updated: Apr 25, 2026

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

6.9K

发现结构和功能连接体的突出差异,使用多项性随机区块模型.

Nina Braad Iskov1, Anders Stevnhoved Olsen1, Kristoffer Hougaard Madsen1,2

  • 1Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.

Network neuroscience (Cambridge, Mass.)
|December 30, 2024
PubMed
概括
此摘要是机器生成的。

多项随机区块模型 (MSBM) 揭示了人类大脑之间的关键差异.

关键词:
贝叶斯的推理 贝叶斯的推理不同模型的差异建模.功能连接性的功能连接性.多项式随机区块模型的多项式随机区块模型结构性的连接性 结构性的连接性

更多相关视频

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

949
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

4.5K

相关实验视频

Last Updated: Apr 25, 2026

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

6.9K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

949
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

4.5K

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
  • 医疗成像医学成像

背景情况:

  • 人类大脑连接性研究广泛研究功能和结构差异.
  • 现有的方法在准确地描述这些区别时面临挑战.

研究的目的:

  • 应用一种新的多项性随机区块模型 (MSBM) 来识别大脑连接的结构-功能差异.
  • 用先进的建模技术分析和比较结构和功能连接体.

主要方法:

  • 在结构和功能连接组上使用了多项性随机区块模型 (MSBM).
  • 分析了来自人类结合体项目的高分辨率扩散加权MRI和fMRI数据 (n=250个受试者).
  • 在50名受试者中进行了组连接分析,分辨率不同 (K={3,4}和K≥25).

主要成果:

  • MSBM确定了一致的,空间均的大脑分区,突出了结构功能差异.
  • 低分辨率分析 (K={3,4}) 显示前叶的功能连接性较弱.
  • 高分辨率分析 (K≥25) 显示,半球间功能连接性比结构连接性更强.

结论:

  • 高分辨率的功能和结构连接体之间存在显著的差异.
  • 挑战包括通过体追踪纤维和前叶的fMRI噪声.
  • MSBM是图形分析和连接经济学的宝贵工具,为模式特定的区别提供了洞察力.