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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.

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.

大规模脑网络是通过其功能来进行识别的。通过研究大规模脑网络建立神经模型，对不同脑区组合所形成的自组织联合体如何实现不同的'''<font color="#ff8000">认知Cognition</font>'''功能进行解释，就能够为认知理解提供一个连贯的框架。识别算法和参数的不同会导致所识别出的上述联合体的数量和组成有所不同。（一个模型理论认为，符合上述条件的神经模型只包含'''<font color="#ff8000">默认模式网络Default mode network</font>'''和'''<font color="#ff8000">任务激活网络Task-positive network</font>'''，但目前大多数分析理论都包括从几个到17个不等的网络。）CY下面列举了最常见且稳定的网络。'''<font color="#32CD32"> （人脑）</font>'''可以动态地重新配置参与功能网络的脑区。  

大规模脑网络是通过其功能来进行识别的。通过研究大规模脑网络建立神经模型，对不同脑区组合所形成的自组织联合体如何实现不同的'''<font color="#ff8000">认知Cognition</font>'''功能进行解释，就能够为认知理解提供一个连贯的框架。识别算法和参数的不同会导致所识别出的上述联合体的数量和组成有所不同。一个模型理论认为，符合上述条件的神经模型只包含'''<font color="#ff8000">默认模式网络Default mode network</font>'''和'''<font color="#ff8000">任务激活网络Task-positive network</font>'''，但目前'''<font color="#32CD32">大多数分析理论都包括从几个到17个不等的网络。</font>'''下面列举了最常见且稳定的网络。'''<font color="#32CD32"> 人脑</font>'''可以动态地重新配置参与功能网络的脑区。  

Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as [[Depression (mood)|depression]], [[Alzheimer's disease|Alzheimer's]], [[Autism-spectrum disorder|autism spectrum disorder]], [[schizophrenia]], [[ADHD]]<ref>{{cite journal |last1=Griffiths |first1=Kristi R. |last2=Braund |first2=Taylor A. |last3=Kohn |first3=Michael R. |last4=Clarke |first4=Simon |last5=Williams |first5=Leanne M. |last6=Korgaonkar |first6=Mayuresh S. |title=Structural brain network topology underpinning ADHD and response to methylphenidate treatment |journal=Translational Psychiatry |date=2 March 2021 |volume=11 |issue=1 |pages=1–9 |doi=10.1038/s41398-021-01278-x | pmc=7925571 |pmid=33654073 |url=https://www.nature.com/articles/s41398-021-01278-x#citeas |access-date=16 November 2021}}</ref> and [[bipolar disorder]].<ref>{{Cite journal|url=https://www.researchgate.net/publication/51639686|title=Large-scale brain networks and psychopathology: A unifying triple network model|last=Menon|first=Vinod|s2cid=26653572|journal=Trends in Cognitive Sciences|date=2011-09-09|volume=15|issue=10|pages=483–506|doi=10.1016/j.tics.2011.08.003|pmid=21908230}}</ref>

Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as [[Depression (mood)|depression]], [[Alzheimer's disease|Alzheimer's]], [[Autism-spectrum disorder|autism spectrum disorder]], [[schizophrenia]], [[ADHD]]<ref>{{cite journal |last1=Griffiths |first1=Kristi R. |last2=Braund |first2=Taylor A. |last3=Kohn |first3=Michael R. |last4=Clarke |first4=Simon |last5=Williams |first5=Leanne M. |last6=Korgaonkar |first6=Mayuresh S. |title=Structural brain network topology underpinning ADHD and response to methylphenidate treatment |journal=Translational Psychiatry |date=2 March 2021 |volume=11 |issue=1 |pages=1–9 |doi=10.1038/s41398-021-01278-x | pmc=7925571 |pmid=33654073 |url=https://www.nature.com/articles/s41398-021-01278-x#citeas |access-date=16 November 2021}}</ref> and [[bipolar disorder]].<ref>{{Cite journal|url=https://www.researchgate.net/publication/51639686|title=Large-scale brain networks and psychopathology: A unifying triple network model|last=Menon|first=Vinod|s2cid=26653572|journal=Trends in Cognitive Sciences|date=2011-09-09|volume=15|issue=10|pages=483–506|doi=10.1016/j.tics.2011.08.003|pmid=21908230}}</ref>

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.

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.

==Core networks==

== Core networks==

[[File:Heine2012x3010.png|thumb|An example that identified 10 large-scale brain networks from [[resting state fMRI]] activity through [[independent component analysis]].<ref name="Heine" />|链接=Special:FilePath/Heine2012x3010.png]]

[[File:Heine2012x3010.png|thumb|An example that identified 10 large-scale brain networks from [[resting state fMRI]] activity through [[independent component analysis]].<ref name="Heine" />|链接=Special:FilePath/Heine2012x3010.png]]

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.<ref>{{cite journal|last1=Eickhoff|first1=SB|last2=Yeo|first2=BTT|last3=Genon|first3=S|title=Imaging-based parcellations of the human brain.|journal=Nature Reviews. Neuroscience|date=November 2018|volume=19|issue=11|pages=672–686|doi=10.1038/s41583-018-0071-7|pmid=30305712|s2cid=52954265|url=http://juser.fz-juelich.de/record/856633/files/Eickhoff_Yeo_Genon_NRN_MainManuscriptInclFigures.pdf}}</ref> While acknowledging this problem, Uddin, Yeo, and Spreng proposed in 2019<ref name="Uddin2019">{{cite journal|last1=Uddin|first1=LQ|last2=Yeo|first2=BTT|last3=Spreng|first3=RN|title=Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks.|journal=Brain Topography|date=November 2019|volume=32|issue=6|pages=926–942|doi=10.1007/s10548-019-00744-6|pmid=31707621|pmc=7325607}}</ref> that the following six networks should be defined as core networks based on converging evidences from multiple studies<ref>{{cite journal|last1=Doucet|first1=GE|last2=Lee|first2=WH|last3=Frangou|first3=S|title=Evaluation of the spatial variability in the major resting-state networks across human brain functional atlases.|journal=Human Brain Mapping|date=2019-10-15|volume=40|issue=15|pages=4577–4587|doi=10.1002/hbm.24722|pmid=31322303|pmc=6771873}}</ref><ref name="Yeo" /><ref>{{cite journal|last1=Smith|first1=SM|last2=Fox|first2=PT|last3=Miller|first3=KL|last4=Glahn|first4=DC|last5=Fox|first5=PM|last6=Mackay|first6=CE|last7=Filippini|first7=N|last8=Watkins|first8=KE|last9=Toro|first9=R|last10=Laird|first10=AR|last11=Beckmann|first11=CF|title=Correspondence of the brain's functional architecture during activation and rest.|journal=Proceedings of the National Academy of Sciences of the United States of America|date=2009-08-04|volume=106|issue=31|pages=13040–5|doi=10.1073/pnas.0905267106|pmid=19620724|pmc=2722273|bibcode=2009PNAS..10613040S|doi-access=free}}</ref> 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.<ref>{{cite journal|last1=Eickhoff|first1=SB|last2=Yeo|first2=BTT|last3=Genon|first3=S|title=Imaging-based parcellations of the human brain.|journal=Nature Reviews. Neuroscience|date=November 2018|volume=19|issue=11|pages=672–686|doi=10.1038/s41583-018-0071-7|pmid=30305712|s2cid=52954265|url=http://juser.fz-juelich.de/record/856633/files/Eickhoff_Yeo_Genon_NRN_MainManuscriptInclFigures.pdf}}</ref> While acknowledging this problem, Uddin, Yeo, and Spreng proposed in 2019<ref name="Uddin2019">{{cite journal|last1=Uddin|first1=LQ|last2=Yeo|first2=BTT|last3=Spreng|first3=RN|title=Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks.|journal=Brain Topography|date=November 2019|volume=32|issue=6|pages=926–942|doi=10.1007/s10548-019-00744-6|pmid=31707621|pmc=7325607}}</ref> that the following six networks should be defined as core networks based on converging evidences from multiple studies<ref>{{cite journal|last1=Doucet|first1=GE|last2=Lee|first2=WH|last3=Frangou|first3=S|title=Evaluation of the spatial variability in the major resting-state networks across human brain functional atlases.|journal=Human Brain Mapping|date=2019-10-15|volume=40|issue=15|pages=4577–4587|doi=10.1002/hbm.24722|pmid=31322303|pmc=6771873}}</ref><ref name="Yeo" /><ref>{{cite journal|last1=Smith|first1=SM|last2=Fox|first2=PT|last3=Miller|first3=KL|last4=Glahn|first4=DC|last5=Fox|first5=PM|last6=Mackay|first6=CE|last7=Filippini|first7=N|last8=Watkins|first8=KE|last9=Toro|first9=R|last10=Laird|first10=AR|last11=Beckmann|first11=CF|title=Correspondence of the brain's functional architecture during activation and rest.|journal=Proceedings of the National Academy of Sciences of the United States of America|date=2009-08-04|volume=106|issue=31|pages=13040–5|doi=10.1073/pnas.0905267106|pmid=19620724|pmc=2722273|bibcode=2009PNAS..10613040S|doi-access=free}}</ref> to facilitate communication between researchers.

* 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.

*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.

* 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.

*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.

*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.

*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。

*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)===

===Attention (Dorsal frontoparietal) ===

{{Main|Dorsal attention network}}

{{Main|Dorsal attention network}}

*This network is involved in the voluntary, top-down deployment of attention.<ref name="Riedl" /><ref name="Yuan" /><ref name="Bell" /><ref name="Yeo" /><ref name="Shafiei" /><ref name="Vossel">{{cite journal|last1=Vossel|first1=Simone|last2=Geng|first2=Joy J.|last3=Fink|first3=Gereon R.|title=Dorsal and Ventral Attention Systems: Distinct Neural Circuits but Collaborative Roles|journal=The Neuroscientist|date=2014|volume=20|issue=2|pages=150–159|doi=10.1177/1073858413494269|pmid=23835449|pmc=4107817}}</ref><ref name="Hutton">{{cite journal|last1=Hutton|first1=John S.|last2=Dudley|first2=Jonathan|last3=Horowitz-Kraus|first3=Tzipi|last4=DeWitt|first4=Tom|last5=Holland|first5=Scott K.|title=Functional Connectivity of Attention, Visual, and Language Networks During Audio, Illustrated, and Animated Stories in Preschool-Age Children|journal=Brain Connectivity|date=1 September 2019|volume=9|issue=7|pages=580–592|doi=10.1089/brain.2019.0679|pmid=31144523|pmc=6775495|ref=Hutton}}</ref> 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.<ref>{{Cite journal|last1=Fox|first1=Michael D.|last2=Corbetta|first2=Maurizio|last3=Snyder|first3=Abraham Z.|last4=Vincent|first4=Justin L.|last5=Raichle|first5=Marcus E.|date=2006-06-27|title=Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems|journal=Proceedings of the National Academy of Sciences|language=en|volume=103|issue=26|pages=10046–10051|doi=10.1073/pnas.0604187103|issn=0027-8424|pmid=16788060|pmc=1480402|bibcode=2006PNAS..10310046F|doi-access=free}}</ref><ref name="Vossel" /><ref name="Bailey" />

*This network is involved in the voluntary, top-down deployment of attention.<ref name="Riedl" /><ref name="Yuan" /><ref name="Bell" /><ref name="Yeo" /><ref name="Shafiei" /><ref name="Vossel">{{cite journal|last1=Vossel|first1=Simone|last2=Geng|first2=Joy J.|last3=Fink|first3=Gereon R.|title=Dorsal and Ventral Attention Systems: Distinct Neural Circuits but Collaborative Roles|journal=The Neuroscientist|date=2014|volume=20|issue=2|pages=150–159|doi=10.1177/1073858413494269|pmid=23835449|pmc=4107817}}</ref><ref name="Hutton">{{cite journal|last1=Hutton|first1=John S.|last2=Dudley|first2=Jonathan|last3=Horowitz-Kraus|first3=Tzipi|last4=DeWitt|first4=Tom|last5=Holland|first5=Scott K.|title=Functional Connectivity of Attention, Visual, and Language Networks During Audio, Illustrated, and Animated Stories in Preschool-Age Children|journal=Brain Connectivity|date=1 September 2019|volume=9|issue=7|pages=580–592|doi=10.1089/brain.2019.0679|pmid=31144523|pmc=6775495|ref=Hutton}}</ref> 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.<ref>{{Cite journal|last1=Fox|first1=Michael D.|last2=Corbetta|first2=Maurizio|last3=Snyder|first3=Abraham Z.|last4=Vincent|first4=Justin L.|last5=Raichle|first5=Marcus E.|date=2006-06-27|title=Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems|journal=Proceedings of the National Academy of Sciences|language=en|volume=103|issue=26|pages=10046–10051|doi=10.1073/pnas.0604187103|issn=0027-8424|pmid=16788060|pmc=1480402|bibcode=2006PNAS..10310046F|doi-access=free}}</ref><ref name="Vossel" /><ref name="Bailey" />

*Versions of this network have also been called the central executive (or executive control) network and the cognitive control network.<ref name="Uddin2019" />

*Versions of this network have also been called the central executive (or executive control) network and the cognitive control network.<ref name="Uddin2019" />

*Versions of this network have also been called the central executive (or executive control) network and the cognitive control network.

* 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。

*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)===

===Sensorimotor or Somatomotor (Pericentral)===

{{Main|Sensorimotor network}}

{{Main|Sensorimotor network}}

*This network processes somatosensory information and coordinates motion.<ref name="Heine" /><ref name="Yeo" /><ref name="Shafiei" /><ref name="Bassett" /><ref name="Yuan" /> The [[auditory cortex]] may be included.<ref name="Uddin2019" /><ref name="Yeo" />

* This network processes somatosensory information and coordinates motion.<ref name="Heine" /><ref name="Yeo" /><ref name="Shafiei" /><ref name="Bassett" /><ref name="Yuan" /> The [[auditory cortex]] may be included.<ref name="Uddin2019" /><ref name="Yeo" />

===Visual (Occipital)===

===Visual (Occipital)===

{{See|Visual cortex}}

{{See|Visual cortex}}

* This network handles visual information processing.<ref name="Yang">{{cite journal|last1=Yang|first1=Yan-li|last2=Deng|first2=Hong-xia|last3=Xing|first3=Gui-yang|last4=Xia|first4=Xiao-luan|last5=Li|first5=Hai-fang|title=Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state|journal=Neural Regeneration Research|date=2015|volume=10|issue=2|pages=298–307|doi=10.4103/1673-5374.152386|pmid=25883631|pmc=4392680 }}</ref>

*This network handles visual information processing.<ref name="Yang">{{cite journal|last1=Yang|first1=Yan-li|last2=Deng|first2=Hong-xia|last3=Xing|first3=Gui-yang|last4=Xia|first4=Xiao-luan|last5=Li|first5=Hai-fang|title=Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state|journal=Neural Regeneration Research|date=2015|volume=10|issue=2|pages=298–307|doi=10.4103/1673-5374.152386|pmid=25883631|pmc=4392680 }}</ref>

*Cerebellar

*Cerebellar

*Spatial attention

*Spatial attention

* Language

*Language

*Lateral visual

*Lateral visual

*Temporal

*Temporal

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。

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

*边缘

*听觉

*听觉

*右/左执行

*右/左执行

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*复杂网络

* 复杂网络

*

*

*神经网络

*神经网络