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大规模脑网络 (查看源代码)

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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>

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'''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<ref name="Riedl" />的统计分析或其他记录方法(如'''脑电图EEG<ref name=":1" />'''、'''正电子发射断层扫描技术PET<ref name=":2" />'''和'''脑磁图MEG<ref name=":3" />''')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。<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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==核心网络==

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~~=<nowiki>= = 核心网络 = =</nowiki>=~~

【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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===默认模式网络（内侧额顶骨）===

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~~=<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'''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。<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" />

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=== Salience (Midcingulo-Insular)===

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===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.<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" />

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===突显网络（扣带回-岛叶）===

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~~=<nowiki>= = 突显网络（扣带回-岛叶）= =</nowiki>=~~

*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，<ref name=":11" /><ref name=":0" />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。<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'''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。<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" />

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===注意网络（背侧额顶骨）===

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~~=<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" />

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===Control (Lateral frontoparietal)===

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=== Control (Lateral frontoparietal)===

*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>

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===控制网络（侧额顶骨）===

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~~=<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" />

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*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" />

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===感觉运动网络（中央区域）===

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===感觉运动网络（中央区域） ===

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~~=<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" />'''听觉皮层Auditory cortex'''可能也包括在内。<ref name="Uddin2019" /><ref name="Yeo" />

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===视觉网络（枕部）===

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~~=<nowiki>= = 视觉网络(枕部) = =</nowiki>=~~

*这个网络处理视觉信息。<ref name="Yang" />

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*Right/left executive<ref name="Yuan" /><ref name="Heine" />

*Cerebellar<ref name="Bell" /><ref name="Heine" />

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*Spatial attention<ref name="Riedl" /><ref name="Bressler" />

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* Spatial attention<ref name="Riedl" /><ref name="Bressler" />

*Language<ref name="Bressler" /><ref name="Hutton" />

*Lateral visual<ref name="Yuan" /><ref name="Bell" /><ref name="Heine" />

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==其他网络==

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~~=<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" />

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==相关词条==

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~~=<nowiki>= = 相关词条 = =</nowiki>=~~

*[[复杂网络]]

*[[神经网络]]

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大规模脑网络 (查看源代码)

2022年4月4日 (一) 17:52的版本

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