大规模脑网络

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Large-scale brain networks (also known as intrinsic brain networks) are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal[1] or other recording methods such as EEG,[2] PET[3] and MEG.[4] An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be "functionally connected". Functional connectivity networks may be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), seed based, and others.[5] Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.[6]


Large-scale brain networks (also known as intrinsic brain networks) are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal or other recording methods such as EEG, PET and MEG. An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be "functionally connected". Functional connectivity networks may be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), seed based, and others. Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.

大规模脑网络Large-scale brain networks(也称为内在大脑网络Intrinsic brain networks)是在对基于血氧水平依赖效应BOLD的功能性磁共振成像fMRI信号的统计分析或其他记录方法(如脑电图EEG、正电子发射断层扫描技术PET和脑磁图MEG)中，表现出功能连接Functional connectivity的脑区Brain regions的集合。根据神经科学中一个新出现的范式，认知任务不是由单个脑区独立执行的，而是由几个互不相连的脑区“功能连接”组成的网络执行的。功能连接网络可以通过数据聚类Cluster analysis、空间独立元素分析ICA、种子点方法等算法来发现。同步的脑区也可以用脑电图、脑磁图或其他动态脑信号的远程同步来识别。

The set of identified brain areas that are linked together in a large-scale network varies with cognitive function.[7] When the cognitive state is not explicit (i.e., the subject is at "rest"), the large-scale brain network is a resting state network (RSN). As a physical system with graph-like properties,[6] a large-scale brain network has both nodes and edges and cannot be identified simply by the co-activation of brain areas. In recent decades, the analysis of brain networks was made feasible by advances in imaging techniques as well as new tools from graph theory and dynamical systems.

The set of identified brain areas that are linked together in a large-scale network varies with cognitive function. When the cognitive state is not explicit (i.e., the subject is at "rest"), the large-scale brain network is a resting state network (RSN). As a physical system with graph-like properties, a large-scale brain network has both nodes and edges and cannot be identified simply by the co-activation of brain areas. In recent decades, the analysis of brain networks was made feasible by advances in imaging techniques as well as new tools from graph theory and dynamical systems.

在一个大规模的网络中，连接在一起的大脑区域的集合因认知功能的不同而不同。当认知状态不明确(即主体处于“静止”状态)时，大规模的脑网络是一个静息状态网络(RSN)。作为一个具有图形特征的物理系统，大规模的脑网络既有节点又有边，不能简单地通过脑区的共同激活来识别。近几十年来，由于成像技术的进步以及图论和动力学系统的新工具，脑网络的分析变得可行。

Large-scale brain networks are identified by their function and provide a coherent framework for understanding cognition by offering a neural model of how different cognitive functions emerge when different sets of brain regions join together as self-organized coalitions. The number and composition of the coalitions will vary with the algorithm and parameters used to identify them.[8][9] In one model, there is only the default mode network and the task-positive network, but most current analyses show several networks, from a small handful to 17.[8] The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.[5][10]

Large-scale brain networks are identified by their function and provide a coherent framework for understanding cognition by offering a neural model of how different cognitive functions emerge when different sets of brain regions join together as self-organized coalitions. The number and composition of the coalitions will vary with the algorithm and parameters used to identify them. In one model, there is only the default mode network and the task-positive network, but most current analyses show several networks, from a small handful to 17. The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.

大规模的大脑网络是通过其功能识别的，并通过提供一个神经模型，说明当不同的大脑区域组合在一起形成自组织的联盟时，不同的认知功能是如何产生的，从而为理解认知提供了一个连贯的框架。联盟的数量和组成将根据识别联盟的算法和参数而有所不同。在一个模型中，只有默认模式网络和任务正向网络，但目前大多数分析显示，从少数到17个网络。下面列举了最常见和最稳定的网络。可以动态地重新配置参与功能网络的区域。

Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia, ADHD[11] and bipolar disorder.[12]

Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia, ADHD and bipolar disorder.

各种网络活动的中断牵连到神经精神疾病，如抑郁症、老年痴呆症、自闭症光谱、精神分裂症、多动症和躁郁症。

Core networks

An example that identified 10 large-scale brain networks from resting state fMRI activity through independent component analysis.[13]

Because brain networks can be identified at various different resolutions and with various different neurobiological properties, there is no such thing as a universal atlas of brain networks that fits all circumstances.[14] While acknowledging this problem, Uddin, Yeo, and Spreng proposed in 2019[15] that the following six networks should be defined as core networks based on converging evidences from multiple studies[16][8][17] to facilitate communication between researchers.


Because brain networks can be identified at various different resolutions and with various different neurobiological properties, there is no such thing as a universal atlas of brain networks that fits all circumstances. While acknowledging this problem, Uddin, Yeo, and Spreng proposed in 2019 that the following six networks should be defined as core networks based on converging evidences from multiple studies to facilitate communication between researchers.

因为大脑网络可以用不同的分辨率和不同的神经生物学特性来识别，所以没有适合所有情况的通用大脑网络图谱。在承认这个问题的同时，Uddin，Yeo，和 Spreng 在2019年提出，以下六个网络应该被定义为核心网络，基于来自多个研究的聚合证据，以促进研究人员之间的交流。

Default Mode (Medial frontoparietal)

Main article: Default mode network
  • The default mode network is active when an individual is awake and at rest. It preferentially activates when individuals focus on internally-oriented tasks such as daydreaming, envisioning the future, retrieving memories, and theory of mind. It is negatively correlated with brain systems that focus on external visual signals. It is the most widely researched network.[6][10][18][1][19][20][13][8][21][22]


  • The default mode network is active when an individual is awake and at rest. It preferentially activates when individuals focus on internally-oriented tasks such as daydreaming, envisioning the future, retrieving memories, and theory of mind. It is negatively correlated with brain systems that focus on external visual signals. It is the most widely researched network.


= = 默认模式(Medial frontoparietal) = =

  • 默认模式网络在个人清醒和休息时处于活动状态。当个体专注于面向内部的任务时，比如做白日梦、展望未来、回忆和心理理论，它就会优先激活。它与专注于外部视觉信号的大脑系统负相关。它是研究最广泛的网络。

Salience (Midcingulo-Insular)

Main article: Salience network
  • The salience network consists of several structures, including the anterior (bilateral) insula, dorsal anterior cingulate cortex, and three subcortical structures which are the ventral striatum, substantia nigra/ventral tegmental region.[23][24] It plays the key role of monitoring the salience of external inputs and internal brain events.[1][6][10][19][13][8][21] Specifically, it aids in directing attention by identifying important biological and cognitive events.[24][22]
  • This network includes the ventral attention network, which primarily includes the temporoparietal junction and the ventral frontal cortex of the right hemisphere.[15][25] These areas respond when behaviorally relevant stimuli occur unexpectedly.[25] The ventral attention network is inhibited during focused attention in which top-down processing is being used, such as when visually searching for something. This response may prevent goal-driven attention from being distracted by non-relevant stimuli. It becomes active again when the target or relevant information about the target is found.[25][26]


  • The salience network consists of several structures, including the anterior (bilateral) insula, dorsal anterior cingulate cortex, and three subcortical structures which are the ventral striatum, substantia nigra/ventral tegmental region. It plays the key role of monitoring the salience of external inputs and internal brain events. Specifically, it aids in directing attention by identifying important biological and cognitive events.
  • This network includes the ventral attention network, which primarily includes the temporoparietal junction and the ventral frontal cortex of the right hemisphere. These areas respond when behaviorally relevant stimuli occur unexpectedly. The ventral attention network is inhibited during focused attention in which top-down processing is being used, such as when visually searching for something. This response may prevent goal-driven attention from being distracted by non-relevant stimuli. It becomes active again when the target or relevant information about the target is found.

突显网络由前(双)岛、前扣带回皮层和腹侧纹状体、黑质/腹侧被盖区3个皮层下结构组成。它起着监测外部输入和内部脑事件的显著性的关键作用。具体来说，它通过识别重要的生物学和认知事件来帮助引导注意力。

  • 这个网络包括腹侧注意网络，主要包括右半球的颞顶联合区和腹侧额叶皮层。当行为相关的刺激意外发生时，这些区域会做出反应。腹侧注意网络在使用自上而下加工的集中注意过程中被抑制，例如在视觉搜索某物时。这种反应可以防止目标驱动的注意力被非相关的刺激分散。当找到目标或关于目标的相关信息时，它再次激活。

Attention (Dorsal frontoparietal)

Main article: Dorsal attention network
  • This network is involved in the voluntary, top-down deployment of attention.[1][19][20][8][21][25][27] Within the dorsal attention network, the intraparietal sulcus and frontal eye fields influence the visual areas of the brain. These influencing factors allow for the orientation of attention.[28][25][22]


  • This network is involved in the voluntary, top-down deployment of attention. Within the dorsal attention network, the intraparietal sulcus and frontal eye fields influence the visual areas of the brain. These influencing factors allow for the orientation of attention.

= = 注意力(背侧额顶骨) = =

  • 这个网络参与了自发的、自上而下的注意力分配。在背侧注意网络中，顶内沟和额眼影响大脑的视觉区域。这些影响因素决定了注意力的方向。

Control (Lateral frontoparietal)

Main article: Frontoparietal network
  • This network initiates and modulates cognitive control and comprises 18 sub-regions of the brain.[29] There is a strong correlation between fluid intelligence and the involvement of the fronto-parietal network with other networks.[30]


  • This network initiates and modulates cognitive control and comprises 18 sub-regions of the brain. There is a strong correlation between fluid intelligence and the involvement of the fronto-parietal network with other networks.

= = 控制(侧额顶骨) = =

  • 这个网络启动和调节认知控制，包括大脑的18个亚区。在流体智力和额顶网络与其他网络的参与之间有很强的相关性。
  • Versions of this network have also been called the central executive (or executive control) network and the cognitive control network.[15]
  • Versions of this network have also been called the central executive (or executive control) network and the cognitive control network.


  • 这种网络的版本也被称为中央执行(或执行控制)网络和认知控制网络。

Sensorimotor or Somatomotor (Pericentral)

Main article: Sensorimotor network


  • This network processes somatosensory information and coordinates motion. The auditory cortex may be included.

Sensorimotor or Somatomotor (Pericentral)

  • This network processes somatosensory information and coordinates motion.听觉皮层可能也包括在内。

Visual (Occipital)

模板:See

  • This network handles visual information processing.[31]


  • This network handles visual information processing.

= = = = = = = = = = = = = 这个网络处理视觉信息。

Other networks

Different methods and data have identified several other brain networks, many of which greatly overlap or are subsets of more well-characterized core networks.[15]

Different methods and data have identified several other brain networks, many of which greatly overlap or are subsets of more well-characterized core networks.

  • Limbic
  • Auditory
  • Right/left executive
  • Cerebellar
  • Spatial attention
  • Language
  • Lateral visual
  • Temporal
  • Visual perception/imagery

其他网络不同的方法和数据已经确定了其他几个大脑网络，其中许多网络极大地重叠或者是更具特色的核心网络的子集。

  • 边缘
  • 听觉
  • 右/左执行
  • 小脑
  • 空间注意
  • 语言
  • 外侧视觉
  • 颞视知觉/图像

See also

  • Complex network
  • Neural network

= = =

  • 复杂网络
  • 神经网络

References

  1. ↑ 1.0 1.1 1.2 1.3 1.4 Riedl, Valentin; Utz, Lukas; Castrillón, Gabriel; Grimmer, Timo; Rauschecker, Josef P.; Ploner, Markus; Friston, Karl J.; Drzezga, Alexander; Sorg, Christian (January 12, 2016). "Metabolic connectivity mapping reveals effective connectivity in the resting human brain". PNAS. 113 (2): 428–433. Bibcode:2016PNAS..113..428R. doi:10.1073/pnas.1513752113. PMC 4720331. PMID 26712010.
  2. ↑ Foster, Brett L.; Parvizi, Josef (2012-03-01). "Resting oscillations and cross-frequency coupling in the human posteromedial cortex". NeuroImage. 60 (1): 384–391. doi:10.1016/j.neuroimage.2011.12.019. ISSN 1053-8119. PMC 3596417. PMID 22227048.
  3. ↑ Buckner, Randy L.; Andrews‐Hanna, Jessica R.; Schacter, Daniel L. (2008). "The Brain's Default Network". Annals of the New York Academy of Sciences (in English). 1124 (1): 1–38. Bibcode:2008NYASA1124....1B. doi:10.1196/annals.1440.011. ISSN 1749-6632. PMID 18400922. S2CID 3167595.
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  13. ↑ 13.0 13.1 13.2 13.3 13.4 13.5 13.6 13.7 Heine, Lizette; Soddu, Andrea; Gomez, Francisco; Vanhaudenhuyse, Audrey; Tshibanda, Luaba; Thonnard, Marie; Charland-Verville, Vanessa; Kirsch, Murielle; Laureys, Steven; Demertzi, Athena (2012). "Resting state networks and consciousness. Alterations of multiple resting state network connectivity in physiological, pharmacological and pathological consciousness states". Frontiers in Psychology. 3: 295. doi:10.3389/fpsyg.2012.00295. PMC 3427917. PMID 22969735.
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  27. ↑ 27.0 27.1 27.2 Hutton, John S.; Dudley, Jonathan; Horowitz-Kraus, Tzipi; DeWitt, Tom; Holland, Scott K. (1 September 2019). "Functional Connectivity of Attention, Visual, and Language Networks During Audio, Illustrated, and Animated Stories in Preschool-Age Children". Brain Connectivity. 9 (7): 580–592. doi:10.1089/brain.2019.0679. PMC 6775495. PMID 31144523.
  28. ↑ Fox, Michael D.; Corbetta, Maurizio; Snyder, Abraham Z.; Vincent, Justin L.; Raichle, Marcus E. (2006-06-27). "Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems". Proceedings of the National Academy of Sciences (in English). 103 (26): 10046–10051. Bibcode:2006PNAS..10310046F. doi:10.1073/pnas.0604187103. ISSN 0027-8424. PMC 1480402. PMID 16788060.
  29. ↑ Scolari, Miranda; Seidl-Rathkopf, Katharina N; Kastner, Sabine (2015-02-01). "Functions of the human frontoparietal attention network: Evidence from neuroimaging". Current Opinion in Behavioral Sciences. Cognitive control. 1: 32–39. doi:10.1016/j.cobeha.2014.08.003. ISSN 2352-1546. PMC 4936532. PMID 27398396.
  30. ↑ Marek, Scott; Dosenbach, Nico U. F. (June 2018). "The frontoparietal network: function, electrophysiology, and importance of individual precision mapping". Dialogues in Clinical Neuroscience. 20 (2): 133–140. doi:10.31887/DCNS.2018.20.2/smarek. ISSN 1294-8322. PMC 6136121. PMID 30250390.
  31. ↑ Yang, Yan-li; Deng, Hong-xia; Xing, Gui-yang; Xia, Xiao-luan; Li, Hai-fang (2015). "Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state". Neural Regeneration Research. 10 (2): 298–307. doi:10.4103/1673-5374.152386. PMC 4392680. PMID 25883631.

模板:Human connectomics

Category:Neuroscience Category:Neural coding Category:Neural circuits Category:Neurophysiology

类别: 神经科学类别: 神经编码类别: 神经回路类别: 神经生理学


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