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相关概念视频

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
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Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
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一种基于预测器的多主体贝叶斯式方法,用于动态功能连接.

Jaylen Lee1, Sana Hussain2, Ryan Warnick3

  • 1Department of Statistics, University of California, Irvine, Irvine, California, United States of America.

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概括
此摘要是机器生成的。

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科学领域:

  • 神经成像是一种神经成像.
  • 计算神经科学是一种神经科学.
  • 统计建模 统计建模

背景情况:

  • 动态功能连接 (dFC) 在fMRI期间跟踪改变大脑区域相互作用.
  • 像注意力和认知努力这样的生理因素可以调节这些连接状态.
  • 现有的方法可能无法完全捕捉时间变化的生理信号对大脑网络动态的影响.

研究的目的:

  • 开发一个新的多主题贝叶斯框架来估计动态功能网络.
  • 将时间变化的生理共变体纳入功能磁共振成像 (fMRI) 数据的分析中.
  • 研究生理变化 (如瞳孔膨胀) 和大脑连接状态转换之间的关系.

主要方法:

  • 利用动态高斯图形模型与非均隐藏的马尔科夫模型来识别潜在的神经状态.
  • 整合了时间变化的外源生理共变量,以模拟各个受试者的状态过渡概率.
  • 采用了用于网络稀疏性的收缩先验和用于边缘选择的贝叶斯虚假发现率控制.

主要成果:

  • 开发的贝叶斯框架成功估计了由生理共变量告知的动态功能网络.
  • 该模型确定了反复出现的连接模式,并允许受试者之间共享网络.
  • 对静止状态fMRI和瞳孔测量数据的分析揭示了瞳孔扩张对大脑状态转换的异质影响.

结论:

  • 提出的贝叶斯框架为分析受生理因素影响的动态功能连接提供了一个强大的方法.
  • 了解各个学科的状态占用异质性,可以了解认知处理.
  • 这种方法通过将神经动态与并发的生理测量联系起来,提高了fMRI数据的解释.