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相关概念视频

Lateralization01:28

Lateralization

330
Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
330

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相关实验视频

Updated: Jul 2, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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建模功能连接和横向化与活动流程框架之间的关联.

Xue Zhan1, Jinwei Lang1, Li-Zhuang Yang2

  • 1Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, PR China; University of Science and Technology of China, Hefei 230026, PR China.

Brain research
|February 27, 2024
PubMed
概括

因果有效的大脑连接指标比简单的相关性更好地预测大脑侧向化. 这一发现适用于不同的认知任务和临床人群,表明因果相互作用塑造了大脑组织.

关键词:
活动流动活动流.功能连接性的功能连接性.功能横向化的功能横向化网络机制 网络机制任务 fMRI 的任务

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Evaluation of Hemisphere Lateralization with Bilateral Local Field Potential Recording in Secondary Motor Cortex of Mice
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相关实验视频

Last Updated: Jul 2, 2025

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科学领域:

  • 神经科学是一个神经科学.
  • 认知神经科学 认知神经科学
  • 网络科学 网络科学

背景情况:

  • 大脑功能包括局部处理 (侧面化) 和分布式网络交互 (功能连接).
  • 大脑横向化和功能连接 (FC) 之间的关系是积极研究的领域.
  • 现有的FC指标在捕捉因果关联的能力上有所不同,这影响了它们对理解大脑组织的有用性.

研究的目的:

  • 调查因果有效的功能连接度量是否比基于关联的FC更好地预测横向化指数 (LI).
  • 在活动流框架内比较四个大脑连接指标 (基于相关联的FC,多重回归FC,部分相关联的FC,组合FC).
  • 评估两种建模方法 (一步和两步) 来评估横向化和FC之间的关联.

主要方法:

  • 采用活动流框架来分析大脑连接.
  • 我们比较了四个FC指标:基于相关性,多重回归,部分相关性和组合FC.
  • 利用一步式和两步式建模方法,从FC指标预测LI.

主要成果:

  • 与基于关联的FC相比,多重回归FC,部分相关FC和组合FC显著改善了模型预测.
  • 这些发现在空间工作记忆任务和语言任务中是一致的.
  • 这些结果在临床样本中复制,包括精神分裂症,双相情感障碍和ADHD.

结论:

  • 因果有效的功能连接指标比基于简单的相关联的方法更好地了解大脑侧向化.
  • 大脑网络相互作用,特别是因果相互作用,在塑造横向化模式方面发挥着至关重要的作用.
  • 这些发现对理解健康和临床人群中大脑组织有意义.