“大规模脑网络”的版本间的差异
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'''Large-scale brain networks''' (also known as '''intrinsic brain networks''') are collections of widespread [[brain regions]] showing [[Resting state fMRI#Functional|functional connectivity]] by statistical analysis of the [[Functional magnetic resonance imaging|fMRI]] [[BOLD signal]]<ref name=Riedl>{{cite journal|last1=Riedl|first1=Valentin|last2=Utz|first2=Lukas|last3=Castrillón|first3=Gabriel|last4=Grimmer|first4=Timo|last5=Rauschecker|first5=Josef P.|last6=Ploner|first6=Markus|last7=Friston|first7=Karl J.|last8=Drzezga|first8=Alexander|last9=Sorg|first9=Christian|title=Metabolic connectivity mapping reveals effective connectivity in the resting human brain|journal=PNAS|date=January 12, 2016|volume=113|issue=2|pages=428–433|doi=10.1073/pnas.1513752113|pmid=26712010|pmc=4720331|bibcode=2016PNAS..113..428R|doi-access=free}}</ref> or other recording methods such as [[Electroencephalography|EEG]],<ref name=":1">{{Cite journal|last1=Foster|first1=Brett L.|last2=Parvizi|first2=Josef|date=2012-03-01|title=Resting oscillations and cross-frequency coupling in the human posteromedial cortex|journal=NeuroImage|volume=60|issue=1|pages=384–391|doi=10.1016/j.neuroimage.2011.12.019|pmid=22227048|issn=1053-8119|pmc=3596417}}</ref> [[Positron emission tomography|PET]]<ref name=":2">{{Cite journal|last1=Buckner|first1=Randy L.|last2=Andrews‐Hanna|first2=Jessica R.|last3=Schacter|first3=Daniel L.|date=2008|title=The Brain's Default Network|journal=Annals of the New York Academy of Sciences|language=en|volume=1124|issue=1|pages=1–38|doi=10.1196/annals.1440.011|pmid=18400922|issn=1749-6632|bibcode=2008NYASA1124....1B|s2cid=3167595}}</ref> and [[Magnetoencephalography|MEG]].<ref name=":3">{{Cite journal|last1=Morris|first1=Peter G.|last2=Smith|first2=Stephen M.|last3=Barnes|first3=Gareth R.|last4=Stephenson|first4=Mary C.|last5=Hale|first5=Joanne R.|last6=Price|first6=Darren|last7=Luckhoo|first7=Henry|last8=Woolrich|first8=Mark|last9=Brookes|first9=Matthew J.|date=2011-10-04|title=Investigating the electrophysiological basis of resting state networks using magnetoencephalography|journal=Proceedings of the National Academy of Sciences|language=en|volume=108|issue=40|pages=16783–16788|doi=10.1073/pnas.1112685108|issn=0027-8424|pmid=21930901|pmc=3189080|bibcode=2011PNAS..10816783B|doi-access=free}}</ref> 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.<ref name="Petersen">{{cite journal|last1=Petersen|first1=Steven|last2=Sporns|first2=Olaf|title=Brain Networks and Cognitive Architectures|journal=Neuron|date=October 2015|volume=88|issue=1|pages=207–219|doi=10.1016/j.neuron.2015.09.027|pmid=26447582|pmc=4598639 }}</ref> Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.<ref name=Bressler>{{cite journal|last1=Bressler|first1=Steven L.|last2=Menon|first2=Vinod|s2cid=5967761|title=Large scale brain networks in cognition: emerging methods and principles|journal=Trends in Cognitive Sciences|date=June 2010|volume=14|issue=6|pages=233–290|doi=10.1016/j.tics.2010.04.004|url=http://www.cell.com/trends/cognitive-sciences/issue?pii=S1364-6613(10)X0005-5|accessdate=24 January 2016|pmid=20493761}}</ref> | '''Large-scale brain networks''' (also known as '''intrinsic brain networks''') are collections of widespread [[brain regions]] showing [[Resting state fMRI#Functional|functional connectivity]] by statistical analysis of the [[Functional magnetic resonance imaging|fMRI]] [[BOLD signal]]<ref name=Riedl>{{cite journal|last1=Riedl|first1=Valentin|last2=Utz|first2=Lukas|last3=Castrillón|first3=Gabriel|last4=Grimmer|first4=Timo|last5=Rauschecker|first5=Josef P.|last6=Ploner|first6=Markus|last7=Friston|first7=Karl J.|last8=Drzezga|first8=Alexander|last9=Sorg|first9=Christian|title=Metabolic connectivity mapping reveals effective connectivity in the resting human brain|journal=PNAS|date=January 12, 2016|volume=113|issue=2|pages=428–433|doi=10.1073/pnas.1513752113|pmid=26712010|pmc=4720331|bibcode=2016PNAS..113..428R|doi-access=free}}</ref> or other recording methods such as [[Electroencephalography|EEG]],<ref name=":1">{{Cite journal|last1=Foster|first1=Brett L.|last2=Parvizi|first2=Josef|date=2012-03-01|title=Resting oscillations and cross-frequency coupling in the human posteromedial cortex|journal=NeuroImage|volume=60|issue=1|pages=384–391|doi=10.1016/j.neuroimage.2011.12.019|pmid=22227048|issn=1053-8119|pmc=3596417}}</ref> [[Positron emission tomography|PET]]<ref name=":2">{{Cite journal|last1=Buckner|first1=Randy L.|last2=Andrews‐Hanna|first2=Jessica R.|last3=Schacter|first3=Daniel L.|date=2008|title=The Brain's Default Network|journal=Annals of the New York Academy of Sciences|language=en|volume=1124|issue=1|pages=1–38|doi=10.1196/annals.1440.011|pmid=18400922|issn=1749-6632|bibcode=2008NYASA1124....1B|s2cid=3167595}}</ref> and [[Magnetoencephalography|MEG]].<ref name=":3">{{Cite journal|last1=Morris|first1=Peter G.|last2=Smith|first2=Stephen M.|last3=Barnes|first3=Gareth R.|last4=Stephenson|first4=Mary C.|last5=Hale|first5=Joanne R.|last6=Price|first6=Darren|last7=Luckhoo|first7=Henry|last8=Woolrich|first8=Mark|last9=Brookes|first9=Matthew J.|date=2011-10-04|title=Investigating the electrophysiological basis of resting state networks using magnetoencephalography|journal=Proceedings of the National Academy of Sciences|language=en|volume=108|issue=40|pages=16783–16788|doi=10.1073/pnas.1112685108|issn=0027-8424|pmid=21930901|pmc=3189080|bibcode=2011PNAS..10816783B|doi-access=free}}</ref> 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.<ref name="Petersen">{{cite journal|last1=Petersen|first1=Steven|last2=Sporns|first2=Olaf|title=Brain Networks and Cognitive Architectures|journal=Neuron|date=October 2015|volume=88|issue=1|pages=207–219|doi=10.1016/j.neuron.2015.09.027|pmid=26447582|pmc=4598639 }}</ref> Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.<ref name=Bressler>{{cite journal|last1=Bressler|first1=Steven L.|last2=Menon|first2=Vinod|s2cid=5967761|title=Large scale brain networks in cognition: emerging methods and principles|journal=Trends in Cognitive Sciences|date=June 2010|volume=14|issue=6|pages=233–290|doi=10.1016/j.tics.2010.04.004|url=http://www.cell.com/trends/cognitive-sciences/issue?pii=S1364-6613(10)X0005-5|accessdate=24 January 2016|pmid=20493761}}</ref> | ||
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'''<font color="#ff8000">大规模脑网络Large-scale brain networks</font>'''(也称为'''<font color="#ff8000">内在大脑网络Intrinsic brain networks</font>''')是在对基于'''<font color="#ff8000">血氧水平依赖效应BOLD</font>'''的'''<font color="#ff8000">功能性磁共振成像fMRI</font>'''信号<ref name="Riedl" />的统计分析或其他记录方法(如'''<font color="#ff8000">脑电图EEG<ref name=":1" /></font>'''、'''<font color="#ff8000">正电子发射断层扫描技术PET<ref name=":2" /></font>'''和'''<font color="#ff8000">脑磁图MEG<ref name=":3" /></font>''')中，表现出'''<font color="#ff8000">功能连接Functional connectivity</font>'''的'''<font color="#ff8000">脑区Brain regions</font>'''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'''<font color="#ff8000">数据聚类Cluster analysis</font>'''、空间'''<font color="#ff8000">独立元素分析ICA</font>'''、种子点方法等算法来发现。<ref name="Petersen" />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。<ref name="Bressler" /> | '''<font color="#ff8000">大规模脑网络Large-scale brain networks</font>'''(也称为'''<font color="#ff8000">内在大脑网络Intrinsic brain networks</font>''')是在对基于'''<font color="#ff8000">血氧水平依赖效应BOLD</font>'''的'''<font color="#ff8000">功能性磁共振成像fMRI</font>'''信号<ref name="Riedl" />的统计分析或其他记录方法(如'''<font color="#ff8000">脑电图EEG<ref name=":1" /></font>'''、'''<font color="#ff8000">正电子发射断层扫描技术PET<ref name=":2" /></font>'''和'''<font color="#ff8000">脑磁图MEG<ref name=":3" /></font>''')中，表现出'''<font color="#ff8000">功能连接Functional connectivity</font>'''的'''<font color="#ff8000">脑区Brain regions</font>'''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'''<font color="#ff8000">数据聚类Cluster analysis</font>'''、空间'''<font color="#ff8000">独立元素分析ICA</font>'''、种子点方法等算法来发现。<ref name="Petersen" />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。<ref name="Bressler" /> | ||
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The set of identified brain areas that are linked together in a large-scale network varies with cognitive function.<ref name="Bressler2">{{cite journal|last1=Bressler|first1=Steven L.|title=Neurocognitive networks|journal=Scholarpedia|volume=3|issue=2|pages=1567|doi=10.4249/scholarpedia.1567|year=2008|bibcode=2008SchpJ...3.1567B|doi-access=free}}</ref> When the cognitive state is not explicit (i.e., the subject is at "rest"), the large-scale brain network is a [[Resting state fMRI|resting state]] network (RSN). As a physical system with graph-like properties,<ref name="Bressler" /> 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 theory|dynamical systems]]. | The set of identified brain areas that are linked together in a large-scale network varies with cognitive function.<ref name="Bressler2">{{cite journal|last1=Bressler|first1=Steven L.|title=Neurocognitive networks|journal=Scholarpedia|volume=3|issue=2|pages=1567|doi=10.4249/scholarpedia.1567|year=2008|bibcode=2008SchpJ...3.1567B|doi-access=free}}</ref> When the cognitive state is not explicit (i.e., the subject is at "rest"), the large-scale brain network is a [[Resting state fMRI|resting state]] network (RSN). As a physical system with graph-like properties,<ref name="Bressler" /> 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 theory|dynamical systems]]. | ||
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大规模脑网络中,连接在一起的脑区集合因认知功能的不同而不同。<ref name="Bressler2" />当认知状态不明确(即主体处于“静止”状态)时，大规模脑网络是一个'''<font color="#ff8000">静息状态Resting state</font>'''网络(RSN)。作为一个具有图形特征的物理系统，<ref name="Bressler" />大规模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，成像技术不断进步，同时'''<font color="#ff8000">图论Graph theory</font>'''、'''<font color="#ff8000">动力学系统Dynamical systems</font>'''领域出现了新的技术手段，这使得脑网络分析变得可行。 | 大规模脑网络中,连接在一起的脑区集合因认知功能的不同而不同。<ref name="Bressler2" />当认知状态不明确(即主体处于“静止”状态)时，大规模脑网络是一个'''<font color="#ff8000">静息状态Resting state</font>'''网络(RSN)。作为一个具有图形特征的物理系统，<ref name="Bressler" />大规模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，成像技术不断进步，同时'''<font color="#ff8000">图论Graph theory</font>'''、'''<font color="#ff8000">动力学系统Dynamical systems</font>'''领域出现了新的技术手段，这使得脑网络分析变得可行。 | ||
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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.<ref name="Yeo" /><ref name=":4">{{cite journal|last1=Abou Elseoud|first1=Ahmed|last2=Littow|first2=Harri|last3=Remes|first3=Jukka|last4=Starck|first4=Tuomo|last5=Nikkinen|first5=Juha|last6=Nissilä|first6=Juuso|last7=Timonen|first7=Markku|last8=Tervonen|first8=Osmo|last9=Kiviniemi1|first9=Vesa|title=Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity|journal= Frontiers in Systems Neuroscience|date=2011-06-03|volume=5|pages=37|doi=10.3389/fnsys.2011.00037|pmid=21687724|pmc=3109774 |doi-access=free}}</ref> 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.<ref name="Yeo" /> The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.<ref name="Petersen" /><ref name="Bassett">{{cite journal|last1=Bassett|first1=Daniella|last2=Bertolero|first2=Max|title=How Matter Becomes Mind|journal=Scientific American|date=July 2019|volume=321|issue=1|page=32|url=https://www.scientificamerican.com/|accessdate=23 June 2019 }}</ref> | 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.<ref name="Yeo" /><ref name=":4">{{cite journal|last1=Abou Elseoud|first1=Ahmed|last2=Littow|first2=Harri|last3=Remes|first3=Jukka|last4=Starck|first4=Tuomo|last5=Nikkinen|first5=Juha|last6=Nissilä|first6=Juuso|last7=Timonen|first7=Markku|last8=Tervonen|first8=Osmo|last9=Kiviniemi1|first9=Vesa|title=Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity|journal= Frontiers in Systems Neuroscience|date=2011-06-03|volume=5|pages=37|doi=10.3389/fnsys.2011.00037|pmid=21687724|pmc=3109774 |doi-access=free}}</ref> 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.<ref name="Yeo" /> The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.<ref name="Petersen" /><ref name="Bassett">{{cite journal|last1=Bassett|first1=Daniella|last2=Bertolero|first2=Max|title=How Matter Becomes Mind|journal=Scientific American|date=July 2019|volume=321|issue=1|page=32|url=https://www.scientificamerican.com/|accessdate=23 June 2019 }}</ref> | ||
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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。<ref name="Yeo" /><ref name=":4" />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。<ref name="Yeo" /></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。<ref name="Petersen" /><ref name="Bassett" /> | 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。<ref name="Yeo" /><ref name=":4" />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。<ref name="Yeo" /></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。<ref name="Petersen" /><ref name="Bassett" /> | ||
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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 name=":5">{{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 name=":6">{{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 name=":5">{{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 name=":6">{{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> | ||
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脑网络活动的中断与诸多神经精神疾病密切相关，如'''<font color="#ff8000">抑郁症Depression</font>'''、'''<font color="#ff8000">老年痴呆症Alzheimer's</font>'''、'''<font color="#ff8000">自闭症谱系障碍Autism spectrum disorder</font>'''、'''<font color="#ff8000">精神分裂症Schizophrenia</font>'''、'''<font color="#ff8000">多动症ADHD<ref name=":5" /></font>'''和'''<font color="#ff8000">躁郁症Bipolar disorder<ref name=":6" /></font>'''。 | 脑网络活动的中断与诸多神经精神疾病密切相关，如'''<font color="#ff8000">抑郁症Depression</font>'''、'''<font color="#ff8000">老年痴呆症Alzheimer's</font>'''、'''<font color="#ff8000">自闭症谱系障碍Autism spectrum disorder</font>'''、'''<font color="#ff8000">精神分裂症Schizophrenia</font>'''、'''<font color="#ff8000">多动症ADHD<ref name=":5" /></font>'''和'''<font color="#ff8000">躁郁症Bipolar disorder<ref name=":6" /></font>'''。 | ||
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【图1：An example that identified 10 large-scale brain networks from resting state fMRI activity through independent component analysis.<ref name="Heine" />这是一个通过独立元素分析方法从静息状态脑功能性磁共振成像信号中分辨出10个大规模脑网络的例子。<ref name="Heine" />】 | 【图1：An example that identified 10 large-scale brain networks from resting state fMRI activity through independent component analysis.<ref name="Heine" />这是一个通过独立元素分析方法从静息状态脑功能性磁共振成像信号中分辨出10个大规模脑网络的例子。<ref name="Heine" />】 | ||
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因为脑网络可以在不同分辨率条件下、使用不同的神经生物学特性来识别，所以没有能够适用于所有情况的通用脑网络图谱。<ref name=":7" />在承认这个问题的同时，Uddin，Yeo，和Spreng在2019年提出<ref name="Uddin2019" />，综合考虑来自多个研究的证据<ref name=":8" /><ref name="Yeo" /><ref name=":9" />，以下六个网络应该被定义为核心网络，以促进研究人员之间的交流。 | 因为脑网络可以在不同分辨率条件下、使用不同的神经生物学特性来识别，所以没有能够适用于所有情况的通用脑网络图谱。<ref name=":7" />在承认这个问题的同时，Uddin，Yeo，和Spreng在2019年提出<ref name="Uddin2019" />，综合考虑来自多个研究的证据<ref name=":8" /><ref name="Yeo" /><ref name=":9" />，以下六个网络应该被定义为核心网络，以促进研究人员之间的交流。 | ||
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===Default Mode (Medial frontoparietal)=== | ===Default Mode (Medial frontoparietal)=== | ||
{{Main|Default mode network}} | {{Main|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.<ref name="Bressler" /><ref name="Bassett" /><ref>{{Cite journal|date=2012-08-15|title=The serendipitous discovery of the brain's default network|journal=NeuroImage|language=en|volume=62|issue=2|pages=1137–1145|doi=10.1016/j.neuroimage.2011.10.035|pmid=22037421|issn=1053-8119|last1=Buckner|first1=Randy L.|s2cid=9880586}}</ref><ref name="Riedl" /><ref name="Yuan"> | + | *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.<ref name="Bressler" /><ref name="Bassett" /><ref name=":10">{{Cite journal|date=2012-08-15|title=The serendipitous discovery of the brain's default network|journal=NeuroImage|language=en|volume=62|issue=2|pages=1137–1145|doi=10.1016/j.neuroimage.2011.10.035|pmid=22037421|issn=1053-8119|last1=Buckner|first1=Randy L.|s2cid=9880586}}</ref><ref name="Riedl" /><ref name="Yuan"> |
{{cite journal|last1=Yuan|first1=Rui|last2=Di|first2=Xin|last3=Taylor|first3=Paul A.|last4=Gohel|first4=Suril|last5=Tsai|first5=Yuan-Hsiung|last6=Biswal|first6=Bharat B.|title=Functional topography of the thalamocortical system in human|journal=Brain Structure and Function|date=30 April 2015|doi=10.1007/s00429-015-1018-7|pmid=25924563|pmc=6363530|volume=221|issue=4|pages=1971–1984}}</ref><ref name="Bell">{{cite journal|last1=Bell|first1=Peter T.|last2=Shine|first2=James M.|title=Estimating Large-Scale Network Convergence in the Human Functional Connectome|journal=Brain Connectivity|date=2015-11-09|volume=5|issue=9|doi=10.1089/brain.2015.0348|pmid=26005099|pages=565–74}}</ref><ref name="Heine">{{cite journal|last1=Heine|first1=Lizette|last2=Soddu|first2=Andrea|last3=Gomez|first3=Francisco|last4=Vanhaudenhuyse|first4=Audrey|last5=Tshibanda|first5=Luaba|last6=Thonnard|first6=Marie|last7=Charland-Verville|first7=Vanessa|last8=Kirsch|first8=Murielle|last9=Laureys|first9=Steven|last10=Demertzi|first10=Athena|title=Resting state networks and consciousness. Alterations of multiple resting state network connectivity in physiological, pharmacological and pathological consciousness states.|journal=Frontiers in Psychology|date=2012|volume=3|pages=295|doi=10.3389/fpsyg.2012.00295|pmid=22969735|pmc=3427917|doi-access=free}}</ref><ref name="Yeo">{{cite journal|last1=Yeo|first1=B. T. Thomas|last2=Krienen|first2=Fenna M.|last3=Sepulcre|first3=Jorge|last4=Sabuncu|first4=Mert R.|last5=Lashkari|first5=Danial|last6=Hollinshead|first6=Marisa|last7=Roffman|first7=Joshua L.|last8=Smoller|first8=Jordan W.|last9=Zöllei|first9=Lilla|last10=Polimeni|first10=Jonathan R.|last11=Fischl|first11=Bruce|last12=Liu|first12=Hesheng|last13=Buckner|first13=Randy L.|title=The organization of the human cerebral cortex estimated by intrinsic functional connectivity|journal=Journal of Neurophysiology|date=2011-09-01|volume=106|issue=3|pages=1125–1165|doi=10.1152/jn.00338.2011|pmid=21653723|pmc=3174820|bibcode=2011NatSD...2E0031H }}</ref><ref name="Shafiei">{{cite journal|last1=Shafiei|first1=Golia|last2=Zeighami|first2=Yashar|last3=Clark|first3=Crystal A.|last4=Coull|first4=Jennifer T.|last5=Nagano-Saito|first5=Atsuko|last6=Leyton|first6=Marco|last7=Dagher|first7=Alain|last8=Mišić|first8=Bratislav|title=Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks|journal=Cerebral Cortex|date=2018-10-01|volume=29|issue=1|pages=397–409|doi=10.1093/cercor/bhy264|pmid=30357316|pmc=6294404 }}</ref><ref name="Bailey">{{cite journal|last1=Bailey|first1=Stephen K.|last2=Aboud|first2=Katherine S.|last3=Nguyen|first3=Tin Q.|last4=Cutting|first4=Laurie E.|title=Applying a network framework to the neurobiology of reading and dyslexia|journal=Journal of Neurodevelopmental Disorders|date=13 December 2018|volume=10|issue=1|page=37|doi=10.1186/s11689-018-9251-z|pmid=30541433|pmc=6291929 }}</ref> | {{cite journal|last1=Yuan|first1=Rui|last2=Di|first2=Xin|last3=Taylor|first3=Paul A.|last4=Gohel|first4=Suril|last5=Tsai|first5=Yuan-Hsiung|last6=Biswal|first6=Bharat B.|title=Functional topography of the thalamocortical system in human|journal=Brain Structure and Function|date=30 April 2015|doi=10.1007/s00429-015-1018-7|pmid=25924563|pmc=6363530|volume=221|issue=4|pages=1971–1984}}</ref><ref name="Bell">{{cite journal|last1=Bell|first1=Peter T.|last2=Shine|first2=James M.|title=Estimating Large-Scale Network Convergence in the Human Functional Connectome|journal=Brain Connectivity|date=2015-11-09|volume=5|issue=9|doi=10.1089/brain.2015.0348|pmid=26005099|pages=565–74}}</ref><ref name="Heine">{{cite journal|last1=Heine|first1=Lizette|last2=Soddu|first2=Andrea|last3=Gomez|first3=Francisco|last4=Vanhaudenhuyse|first4=Audrey|last5=Tshibanda|first5=Luaba|last6=Thonnard|first6=Marie|last7=Charland-Verville|first7=Vanessa|last8=Kirsch|first8=Murielle|last9=Laureys|first9=Steven|last10=Demertzi|first10=Athena|title=Resting state networks and consciousness. Alterations of multiple resting state network connectivity in physiological, pharmacological and pathological consciousness states.|journal=Frontiers in Psychology|date=2012|volume=3|pages=295|doi=10.3389/fpsyg.2012.00295|pmid=22969735|pmc=3427917|doi-access=free}}</ref><ref name="Yeo">{{cite journal|last1=Yeo|first1=B. T. Thomas|last2=Krienen|first2=Fenna M.|last3=Sepulcre|first3=Jorge|last4=Sabuncu|first4=Mert R.|last5=Lashkari|first5=Danial|last6=Hollinshead|first6=Marisa|last7=Roffman|first7=Joshua L.|last8=Smoller|first8=Jordan W.|last9=Zöllei|first9=Lilla|last10=Polimeni|first10=Jonathan R.|last11=Fischl|first11=Bruce|last12=Liu|first12=Hesheng|last13=Buckner|first13=Randy L.|title=The organization of the human cerebral cortex estimated by intrinsic functional connectivity|journal=Journal of Neurophysiology|date=2011-09-01|volume=106|issue=3|pages=1125–1165|doi=10.1152/jn.00338.2011|pmid=21653723|pmc=3174820|bibcode=2011NatSD...2E0031H }}</ref><ref name="Shafiei">{{cite journal|last1=Shafiei|first1=Golia|last2=Zeighami|first2=Yashar|last3=Clark|first3=Crystal A.|last4=Coull|first4=Jennifer T.|last5=Nagano-Saito|first5=Atsuko|last6=Leyton|first6=Marco|last7=Dagher|first7=Alain|last8=Mišić|first8=Bratislav|title=Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks|journal=Cerebral Cortex|date=2018-10-01|volume=29|issue=1|pages=397–409|doi=10.1093/cercor/bhy264|pmid=30357316|pmc=6294404 }}</ref><ref name="Bailey">{{cite journal|last1=Bailey|first1=Stephen K.|last2=Aboud|first2=Katherine S.|last3=Nguyen|first3=Tin Q.|last4=Cutting|first4=Laurie E.|title=Applying a network framework to the neurobiology of reading and dyslexia|journal=Journal of Neurodevelopmental Disorders|date=13 December 2018|volume=10|issue=1|page=37|doi=10.1186/s11689-018-9251-z|pmid=30541433|pmc=6291929 }}</ref> | ||
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=<nowiki>= = 默认模式网络（内侧额顶叶） = =</nowiki>= | =<nowiki>= = 默认模式网络（内侧额顶叶） = =</nowiki>= | ||
| − | *默认模式网络在个体清醒和休息时都处于活跃状态。当个体专注于内部导向任务，比如做白日梦、展望未来、提取回忆和'''<font color="#ff8000">心智理论Theory of mind</font>'''任务时，默认模式网络会被优先激活。它与专注于外部视觉信号的大脑系统成负相关。对默认模式网络的研究目前是所谓网络之中最为广泛的。 | + | *默认模式网络在个体清醒和休息时都处于活跃状态。当个体专注于内部导向任务，比如做白日梦、展望未来、提取回忆和'''<font color="#ff8000">心智理论Theory of mind</font>'''任务时，默认模式网络会被优先激活。它与专注于外部视觉信号的大脑系统成负相关。对默认模式网络的研究目前是所谓网络之中最为广泛的。<ref name="Bressler" /><ref name="Bassett" /><ref name=":10" /><ref name="Riedl" /><ref name="Yuan" /><ref name="Bell" /><ref name="Heine" /><ref name="Yeo" /><ref name="Shafiei" /><ref name="Bailey" /> |
===Salience (Midcingulo-Insular)=== | ===Salience (Midcingulo-Insular)=== | ||
{{Main|Salience network}} | {{Main|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.<ref>{{Cite journal|last1=Steimke|first1=Rosa|last2=Nomi|first2=Jason S.|last3=Calhoun|first3=Vince D.|last4=Stelzel|first4=Christine|last5=Paschke|first5=Lena M.|last6=Gaschler|first6=Robert|last7=Goschke|first7=Thomas|last8=Walter|first8=Henrik|last9=Uddin|first9=Lucina Q.|date=2017-12-01|title=Salience network dynamics underlying successful resistance of temptation|journal=Social Cognitive and Affective Neuroscience|language=en|volume=12|issue=12|pages=1928–1939|doi=10.1093/scan/nsx123|pmid=29048582|pmc=5716209|issn=1749-5016|doi-access=free}}</ref><ref name=":0">{{Citation|last=Menon|first=V.|title=Brain Mapping|chapter=Salience Network|date=2015-01-01|chapter-url=http://www.sciencedirect.com/science/article/pii/B978012397025100052X|pages=597–611|editor-last=Toga|editor-first=Arthur W.|publisher=Academic Press|isbn=978-0-12-397316-0|access-date=2019-12-08|doi=10.1016/B978-0-12-397025-1.00052-X}}</ref> It plays the key role of monitoring the [[Salience (neuroscience)|salience]] of external inputs and internal brain events.<ref name="Riedl" /><ref name="Bressler" /><ref name="Bassett" /><ref name="Yuan" /><ref name="Heine" /><ref name="Yeo" /><ref name="Shafiei" /> Specifically, it aids in directing attention by identifying important biological and cognitive events.<ref name=":0" /><ref name="Bailey" /> | + | *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.<ref name=":11">{{Cite journal|last1=Steimke|first1=Rosa|last2=Nomi|first2=Jason S.|last3=Calhoun|first3=Vince D.|last4=Stelzel|first4=Christine|last5=Paschke|first5=Lena M.|last6=Gaschler|first6=Robert|last7=Goschke|first7=Thomas|last8=Walter|first8=Henrik|last9=Uddin|first9=Lucina Q.|date=2017-12-01|title=Salience network dynamics underlying successful resistance of temptation|journal=Social Cognitive and Affective Neuroscience|language=en|volume=12|issue=12|pages=1928–1939|doi=10.1093/scan/nsx123|pmid=29048582|pmc=5716209|issn=1749-5016|doi-access=free}}</ref><ref name=":0">{{Citation|last=Menon|first=V.|title=Brain Mapping|chapter=Salience Network|date=2015-01-01|chapter-url=http://www.sciencedirect.com/science/article/pii/B978012397025100052X|pages=597–611|editor-last=Toga|editor-first=Arthur W.|publisher=Academic Press|isbn=978-0-12-397316-0|access-date=2019-12-08|doi=10.1016/B978-0-12-397025-1.00052-X}}</ref> It plays the key role of monitoring the [[Salience (neuroscience)|salience]] of external inputs and internal brain events.<ref name="Riedl" /><ref name="Bressler" /><ref name="Bassett" /><ref name="Yuan" /><ref name="Heine" /><ref name="Yeo" /><ref name="Shafiei" /> Specifically, it aids in directing attention by identifying important biological and cognitive events.<ref name=":0" /><ref name="Bailey" /> |
| − | *This network includes the ventral attention network, which primarily includes the [[temporoparietal junction]] and the ventral [[frontal cortex]] of the right hemisphere.<ref name="Uddin2019" /><ref name="Vossel" /> These areas respond when behaviorally relevant stimuli occur unexpectedly.<ref name="Vossel" /> 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.<ref name="Vossel" /><ref>{{Cite journal|last1=Shulman|first1=Gordon L.|last2=McAvoy|first2=Mark P.|last3=Cowan|first3=Melanie C.|last4=Astafiev|first4=Serguei V.|last5=Tansy|first5=Aaron P.|last6=d'Avossa|first6=Giovanni|last7=Corbetta|first7=Maurizio|date=2003-11-01|title=Quantitative Analysis of Attention and Detection Signals During Visual Search|journal=Journal of Neurophysiology|volume=90|issue=5|pages=3384–3397|doi=10.1152/jn.00343.2003|pmid=12917383|issn=0022-3077}}</ref> | + | *This network includes the ventral attention network, which primarily includes the [[temporoparietal junction]] and the ventral [[frontal cortex]] of the right hemisphere.<ref name="Uddin2019" /><ref name="Vossel" /> These areas respond when behaviorally relevant stimuli occur unexpectedly.<ref name="Vossel" /> 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.<ref name="Vossel" /><ref name=":12">{{Cite journal|last1=Shulman|first1=Gordon L.|last2=McAvoy|first2=Mark P.|last3=Cowan|first3=Melanie C.|last4=Astafiev|first4=Serguei V.|last5=Tansy|first5=Aaron P.|last6=d'Avossa|first6=Giovanni|last7=Corbetta|first7=Maurizio|date=2003-11-01|title=Quantitative Analysis of Attention and Detection Signals During Visual Search|journal=Journal of Neurophysiology|volume=90|issue=5|pages=3384–3397|doi=10.1152/jn.00343.2003|pmid=12917383|issn=0022-3077}}</ref> |
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=<nowiki>= = 突显网络（扣带回-岛叶）= =</nowiki>= | =<nowiki>= = 突显网络（扣带回-岛叶）= =</nowiki>= | ||
| − | *突显网络由'''<font color="#32CD32"> 前(双)岛、前扣带回皮层和三个皮层下结构（腹侧纹状体、黑质/腹侧被盖区）</font>''' | + | *突显网络由'''<font color="#32CD32"> 前(双)岛、前扣带回皮层和三个皮层下结构（腹侧纹状体、黑质/腹侧被盖区）</font>'''组成，<ref name=":11" /><ref name=":0" />它在监测外部输入和内部脑活动的'''<font color="#ff8000">凸显程度Salience</font>'''中发挥着关键作用。<ref name="Riedl" /><ref name="Bressler" /><ref name="Bassett" /><ref name="Yuan" /><ref name="Heine" /><ref name="Yeo" /><ref name="Shafiei" />具体来说，突显网络通过识别重要的生理、认知活动来帮助引导注意力。<ref name=":0" /><ref name="Bailey" /> |
| − | *突显网络包括主要由右半球的'''<font color="#ff8000">颞顶联合区Temporoparietal junction</font>'''和腹侧'''<font color="#ff8000">额叶Frontal cortex</font>''' | + | *突显网络包括主要由右半球的'''<font color="#ff8000">颞顶联合区Temporoparietal junction</font>'''和腹侧'''<font color="#ff8000">额叶Frontal cortex</font>'''皮层组成的腹侧注意网络。<ref name="Uddin2019" /><ref name="Vossel" />当行为相关的刺激意外发生时，这些区域会对此做出反应。在自上而下加工注意焦点的过程中（例如使用视觉搜索某件物品时），腹侧注意网络会受到抑制。这种抑制反应可以防止目标驱动的注意力被不相关的刺激分散。当找到目标或相关信息时，突显网络会被再次激活。<ref name="Vossel" /><ref name=":12" /> |
===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 name=":13">{{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" /> |
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=<nowiki>= = 注意网络(背侧额顶骨) = =</nowiki>= | =<nowiki>= = 注意网络(背侧额顶骨) = =</nowiki>= | ||
| − | * | + | *这个网络参与了自发的、自上而下的注意力分配。<ref name="Riedl" /><ref name="Yuan" /><ref name="Bell" /><ref name="Yeo" /><ref name="Shafiei" /><ref name="Vossel" /><ref name="Hutton" />在背侧注意网络中，顶内沟和额眼影响着大脑的视觉区域。这些影响因素决定了注意力的方向。<ref name=":13" /><ref name="Vossel" /><ref name="Bailey" /> |
===Control (Lateral frontoparietal)=== | ===Control (Lateral frontoparietal)=== | ||
{{Main|Frontoparietal network}} | {{Main|Frontoparietal network}} | ||
| − | *This network initiates and modulates cognitive control and comprises 18 sub-regions of the brain.<ref>{{Cite journal|last1=Scolari|first1=Miranda|last2=Seidl-Rathkopf|first2=Katharina N|last3=Kastner|first3=Sabine|date=2015-02-01|title=Functions of the human frontoparietal attention network: Evidence from neuroimaging|journal=Current Opinion in Behavioral Sciences|series=Cognitive control|volume=1|pages=32–39|doi=10.1016/j.cobeha.2014.08.003|issn=2352-1546|pmid=27398396|pmc=4936532}}</ref> There is a strong correlation between fluid intelligence and the involvement of the fronto-parietal network with other networks.<ref>{{Cite journal|last1=Marek|first1=Scott|last2=Dosenbach|first2=Nico U. F.|date=June 2018|title=The frontoparietal network: function, electrophysiology, and importance of individual precision mapping|journal=Dialogues in Clinical Neuroscience|volume=20|issue=2|pages=133–140|doi=10.31887/DCNS.2018.20.2/smarek|issn=1294-8322|pmc=6136121|pmid=30250390}}</ref> | + | *This network initiates and modulates cognitive control and comprises 18 sub-regions of the brain.<ref name=":14">{{Cite journal|last1=Scolari|first1=Miranda|last2=Seidl-Rathkopf|first2=Katharina N|last3=Kastner|first3=Sabine|date=2015-02-01|title=Functions of the human frontoparietal attention network: Evidence from neuroimaging|journal=Current Opinion in Behavioral Sciences|series=Cognitive control|volume=1|pages=32–39|doi=10.1016/j.cobeha.2014.08.003|issn=2352-1546|pmid=27398396|pmc=4936532}}</ref> There is a strong correlation between fluid intelligence and the involvement of the fronto-parietal network with other networks.<ref name=":15">{{Cite journal|last1=Marek|first1=Scott|last2=Dosenbach|first2=Nico U. F.|date=June 2018|title=The frontoparietal network: function, electrophysiology, and importance of individual precision mapping|journal=Dialogues in Clinical Neuroscience|volume=20|issue=2|pages=133–140|doi=10.31887/DCNS.2018.20.2/smarek|issn=1294-8322|pmc=6136121|pmid=30250390}}</ref> |
*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" /> | ||
| − | |||
| − | |||
=<nowiki>= = 控制网络(侧额顶骨) = =</nowiki>= | =<nowiki>= = 控制网络(侧额顶骨) = =</nowiki>= | ||
| − | * | + | *这个网络参与了认知控制的启动与调节，它包括了大脑的18个亚区。<ref name=":14" />额顶网络与其他网络的参与程度和流体智力之间存在着有很强的相关性。<ref name=":15" /> |
| − | + | *在其它版本中，这种网络也被称为中央执行(或执行控制)网络和认知控制网络。<ref name="Uddin2019" /> | |
| − | |||
| − | |||
| − | |||
| − | *在其它版本中，这种网络也被称为中央执行(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" /> | ||
| − | |||
| − | |||
=<nowiki>= = 感觉运动网络(中央区域) = =</nowiki>= | =<nowiki>= = 感觉运动网络(中央区域) = =</nowiki>= | ||
| − | * | + | * 这个网络参与了躯体感觉信息的加工和运动的协调。<ref name="Heine" /><ref name="Yeo" /><ref name="Shafiei" /><ref name="Bassett" /><ref name="Yuan" />'''<font color="#ff8000">听觉皮层Auditory cortex</font>'''可能也包括在内。<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> | ||
| − | |||
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| − | |||
=<nowiki>= = 视觉网络(枕部) = =</nowiki>= | =<nowiki>= = 视觉网络(枕部) = =</nowiki>= | ||
| − | *这个网络处理视觉信息。 | + | *这个网络处理视觉信息。<ref name="Yang" /> |
| − | == Other networks== | + | ==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.<ref name="Uddin2019" /> | Different methods and data have identified several other brain networks, many of which greatly overlap or are subsets of more well-characterized core networks.<ref name="Uddin2019" /> | ||
*Limbic<ref name="Bassett" /><ref name="Yeo" /><ref name="Bailey" /> | *Limbic<ref name="Bassett" /><ref name="Yeo" /><ref name="Bailey" /> | ||
| − | *Auditory<ref name="Yuan" /><ref name="Heine" /> | + | * Auditory<ref name="Yuan" /><ref name="Heine" /> |
*Right/left executive<ref name="Yuan" /><ref name="Heine" /> | *Right/left executive<ref name="Yuan" /><ref name="Heine" /> | ||
*Cerebellar<ref name="Bell" /><ref name="Heine" /> | *Cerebellar<ref name="Bell" /><ref name="Heine" /> | ||
*Spatial attention<ref name="Riedl" /><ref name="Bressler" /> | *Spatial attention<ref name="Riedl" /><ref name="Bressler" /> | ||
*Language<ref name="Bressler" /><ref name="Hutton" /> | *Language<ref name="Bressler" /><ref name="Hutton" /> | ||
| − | * Lateral visual<ref name="Yuan" /><ref name="Bell" /><ref name="Heine" /> | + | *Lateral visual<ref name="Yuan" /><ref name="Bell" /><ref name="Heine" /> |
*Temporal<ref name="Yeo" /><ref name="Shafiei" /> | *Temporal<ref name="Yeo" /><ref name="Shafiei" /> | ||
| − | *Visual perception/imagery<ref name="Hutton" /> | + | * Visual perception/imagery<ref name="Hutton" /> |
| − | + | =<nowiki>= = 其他网络 = =</nowiki>= | |
| − | + | 不同的方法和数据已经能够确定上述核心网络以外的几个大脑网络，其中许多网络之间存在极大的重叠，部分网络实际上是核心网络更具特点的子集。<ref name="Uddin2019" /> | |
| − | * | + | *边缘网络<ref name="Bassett" /><ref name="Yeo" /><ref name="Bailey" /> |
| − | * | + | *听觉网络<ref name="Yuan" /><ref name="Heine" /> |
| − | * | + | *右/左执行网络<ref name="Yuan" /><ref name="Heine" /> |
| − | * | + | *小脑网络<ref name="Bell" /><ref name="Heine" /> |
| − | * | + | *空间注意网络<ref name="Riedl" /><ref name="Bressler" /> |
| − | * | + | *语言网络<ref name="Bressler" /><ref name="Hutton" /> |
| − | * | + | *外侧视觉网络<ref name="Yuan" /><ref name="Bell" /><ref name="Heine" /> |
| − | * | + | *颞骨网络<ref name="Yeo" /><ref name="Shafiei" /> |
| − | + | *颞视知觉/图像网络<ref name="Hutton" /> | |
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | *颞视知觉/ | ||
==See also== | ==See also== | ||
| 第145行： | 第111行： | ||
*[[Neural network]] | *[[Neural network]] | ||
| − | + | =<nowiki>= = 相关词条 = =</nowiki>= | |
| − | |||
| − | |||
| − | =<nowiki>= = =</nowiki>= | ||
*复杂网络 | *复杂网络 | ||
| − | |||
*神经网络 | *神经网络 | ||
| − | |||
==References== | ==References== | ||
2022年4月4日 (一) 17:34的版本
此词条由神经动力学模型读书会词条梳理志愿者Shenky20翻译审校，翻译字数共800，未经专家审核，带来阅读不便，请见谅。
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]
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[1]的统计分析或其他记录方法(如脑电图EEG[2]、正电子发射断层扫描技术PET[3]和脑磁图MEG[4])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。[5]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。[6]
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.
大规模脑网络中,连接在一起的脑区集合因认知功能的不同而不同。[7]当认知状态不明确(即主体处于“静止”状态)时，大规模脑网络是一个静息状态Resting state网络(RSN)。作为一个具有图形特征的物理系统，[6]大规模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，成像技术不断进步，同时图论Graph theory、动力学系统Dynamical systems领域出现了新的技术手段，这使得脑网络分析变得可行。
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]
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认知Cognition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。[8][9]一个模型理论认为，符合上述条件的神经模型只包含默认模式网络Default mode network和任务激活网络Task-positive network，但目前大多数分析理论都包括从几个到17个不等的网络。[8]下面列举了最常见且稳定的网络。 人脑可以动态地重新配置参与功能网络的脑区。[5][10]
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]
脑网络活动的中断与诸多神经精神疾病密切相关，如抑郁症Depression、老年痴呆症Alzheimer's、自闭症谱系障碍Autism spectrum disorder、精神分裂症Schizophrenia、多动症ADHD[11]和躁郁症Bipolar disorder[12]。
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.
【图1：An example that identified 10 large-scale brain networks from resting state fMRI activity through independent component analysis.[13]这是一个通过独立元素分析方法从静息状态脑功能性磁共振成像信号中分辨出10个大规模脑网络的例子。[13]】
因为脑网络可以在不同分辨率条件下、使用不同的神经生物学特性来识别，所以没有能够适用于所有情况的通用脑网络图谱。[14]在承认这个问题的同时，Uddin，Yeo，和Spreng在2019年提出[15]，综合考虑来自多个研究的证据[16][8][17]，以下六个网络应该被定义为核心网络，以促进研究人员之间的交流。
Default Mode (Medial frontoparietal)
- 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]
= = 默认模式网络（内侧额顶叶） = =
- 默认模式网络在个体清醒和休息时都处于活跃状态。当个体专注于内部导向任务，比如做白日梦、展望未来、提取回忆和心智理论Theory of mind任务时，默认模式网络会被优先激活。它与专注于外部视觉信号的大脑系统成负相关。对默认模式网络的研究目前是所谓网络之中最为广泛的。[6][10][18][1][19][20][13][8][21][22]
Salience (Midcingulo-Insular)
- 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]
= = 突显网络（扣带回-岛叶）= =
- 突显网络由 前(双)岛、前扣带回皮层和三个皮层下结构（腹侧纹状体、黑质/腹侧被盖区）组成，[23][24]它在监测外部输入和内部脑活动的凸显程度Salience中发挥着关键作用。[1][6][10][19][13][8][21]具体来说，突显网络通过识别重要的生理、认知活动来帮助引导注意力。[24][22]
- 突显网络包括主要由右半球的颞顶联合区Temporoparietal junction和腹侧额叶Frontal cortex皮层组成的腹侧注意网络。[15][25]当行为相关的刺激意外发生时，这些区域会对此做出反应。在自上而下加工注意焦点的过程中（例如使用视觉搜索某件物品时），腹侧注意网络会受到抑制。这种抑制反应可以防止目标驱动的注意力被不相关的刺激分散。当找到目标或相关信息时，突显网络会被再次激活。[25][26]
Attention (Dorsal frontoparietal)
- 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]
= = 注意网络(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。[1][19][20][8][21][25][27]在背侧注意网络中，顶内沟和额眼影响着大脑的视觉区域。这些影响因素决定了注意力的方向。[28][25][22]
Control (Lateral frontoparietal)
- 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]
- Versions of this network have also been called the central executive (or executive control) network and the cognitive control network.[15]
= = 控制网络(侧额顶骨) = =
- 这个网络参与了认知控制的启动与调节，它包括了大脑的18个亚区。[29]额顶网络与其他网络的参与程度和流体智力之间存在着有很强的相关性。[30]
- 在其它版本中，这种网络也被称为中央执行(或执行控制)网络和认知控制网络。[15]
Sensorimotor or Somatomotor (Pericentral)
- This network processes somatosensory information and coordinates motion.[13][8][21][10][19] The auditory cortex may be included.[15][8]
= = 感觉运动网络(中央区域) = =
Visual (Occipital)
- This network handles visual information processing.[31]
= = 视觉网络(枕部) = =
- 这个网络处理视觉信息。[31]
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]
- Limbic[10][8][22]
- Auditory[19][13]
- Right/left executive[19][13]
- Cerebellar[20][13]
- Spatial attention[1][6]
- Language[6][27]
- Lateral visual[19][20][13]
- Temporal[8][21]
- Visual perception/imagery[27]
= = 其他网络 = =
不同的方法和数据已经能够确定上述核心网络以外的几个大脑网络，其中许多网络之间存在极大的重叠，部分网络实际上是核心网络更具特点的子集。[15]
- 边缘网络[10][8][22]
- 听觉网络[19][13]
- 右/左执行网络[19][13]
- 小脑网络[20][13]
- 空间注意网络[1][6]
- 语言网络[6][27]
- 外侧视觉网络[19][20][13]
- 颞骨网络[8][21]
- 颞视知觉/图像网络[27]
See also
= = 相关词条 = =
- 复杂网络
- 神经网络
References
- ↑ 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 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.
- ↑ 2.0 2.1 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.
- ↑ 3.0 3.1 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.
- ↑ 4.0 4.1 Morris, Peter G.; Smith, Stephen M.; Barnes, Gareth R.; Stephenson, Mary C.; Hale, Joanne R.; Price, Darren; Luckhoo, Henry; Woolrich, Mark; Brookes, Matthew J. (2011-10-04). "Investigating the electrophysiological basis of resting state networks using magnetoencephalography". Proceedings of the National Academy of Sciences (in English). 108 (40): 16783–16788. Bibcode:2011PNAS..10816783B. doi:10.1073/pnas.1112685108. ISSN 0027-8424. PMC 3189080. PMID 21930901.
- ↑ 5.0 5.1 5.2 5.3 Petersen, Steven; Sporns, Olaf (October 2015). "Brain Networks and Cognitive Architectures". Neuron. 88 (1): 207–219. doi:10.1016/j.neuron.2015.09.027. PMC 4598639. PMID 26447582.
- ↑ 6.00 6.01 6.02 6.03 6.04 6.05 6.06 6.07 6.08 6.09 6.10 6.11 Bressler, Steven L.; Menon, Vinod (June 2010). "Large scale brain networks in cognition: emerging methods and principles". Trends in Cognitive Sciences. 14 (6): 233–290. doi:10.1016/j.tics.2010.04.004. PMID 20493761. S2CID 5967761. Retrieved 24 January 2016.
- ↑ 7.0 7.1 Bressler, Steven L. (2008). "Neurocognitive networks". Scholarpedia. 3 (2): 1567. Bibcode:2008SchpJ...3.1567B. doi:10.4249/scholarpedia.1567.
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- ↑ 16.0 16.1 Doucet, GE; Lee, WH; Frangou, S (2019-10-15). "Evaluation of the spatial variability in the major resting-state networks across human brain functional atlases". Human Brain Mapping. 40 (15): 4577–4587. doi:10.1002/hbm.24722. PMC 6771873. PMID 31322303.
- ↑ 17.0 17.1 Smith, SM; Fox, PT; Miller, KL; Glahn, DC; Fox, PM; Mackay, CE; Filippini, N; Watkins, KE; Toro, R; Laird, AR; Beckmann, CF (2009-08-04). "Correspondence of the brain's functional architecture during activation and rest". Proceedings of the National Academy of Sciences of the United States of America. 106 (31): 13040–5. Bibcode:2009PNAS..10613040S. doi:10.1073/pnas.0905267106. PMC 2722273. PMID 19620724.
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- ↑ 19.00 19.01 19.02 19.03 19.04 19.05 19.06 19.07 19.08 19.09 19.10 19.11 19.12 19.13 Yuan, Rui; Di, Xin; Taylor, Paul A.; Gohel, Suril; Tsai, Yuan-Hsiung; Biswal, Bharat B. (30 April 2015). "Functional topography of the thalamocortical system in human". Brain Structure and Function. 221 (4): 1971–1984. doi:10.1007/s00429-015-1018-7. PMC 6363530. PMID 25924563.
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- ↑ 21.0 21.1 21.2 21.3 21.4 21.5 21.6 21.7 21.8 21.9 Shafiei, Golia; Zeighami, Yashar; Clark, Crystal A.; Coull, Jennifer T.; Nagano-Saito, Atsuko; Leyton, Marco; Dagher, Alain; Mišić, Bratislav (2018-10-01). "Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks". Cerebral Cortex. 29 (1): 397–409. doi:10.1093/cercor/bhy264. PMC 6294404. PMID 30357316.
- ↑ 22.0 22.1 22.2 22.3 22.4 22.5 22.6 22.7 Bailey, Stephen K.; Aboud, Katherine S.; Nguyen, Tin Q.; Cutting, Laurie E. (13 December 2018). "Applying a network framework to the neurobiology of reading and dyslexia". Journal of Neurodevelopmental Disorders. 10 (1): 37. doi:10.1186/s11689-018-9251-z. PMC 6291929. PMID 30541433.
- ↑ 23.0 23.1 Steimke, Rosa; Nomi, Jason S.; Calhoun, Vince D.; Stelzel, Christine; Paschke, Lena M.; Gaschler, Robert; Goschke, Thomas; Walter, Henrik; Uddin, Lucina Q. (2017-12-01). "Salience network dynamics underlying successful resistance of temptation". Social Cognitive and Affective Neuroscience (in English). 12 (12): 1928–1939. doi:10.1093/scan/nsx123. ISSN 1749-5016. PMC 5716209. PMID 29048582.
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- ↑ 25.0 25.1 25.2 25.3 25.4 25.5 25.6 25.7 25.8 Vossel, Simone; Geng, Joy J.; Fink, Gereon R. (2014). "Dorsal and Ventral Attention Systems: Distinct Neural Circuits but Collaborative Roles". The Neuroscientist. 20 (2): 150–159. doi:10.1177/1073858413494269. PMC 4107817. PMID 23835449.
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- ↑ 29.0 29.1 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.
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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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