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大规模脑网络 (查看源代码)

2022年4月4日 (一) 15:46的版本

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

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

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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。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，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.<ref name="Yeo" /><ref>{{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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各种网络活动的中断牵连到神经精神疾病，如抑郁症、老年痴呆症、自闭症光谱、精神分裂症、多动症和躁郁症。

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==Core networks ==

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==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]]

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.

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*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" />

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*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>

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* 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>

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

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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>{{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>

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= = = = = = = = = = = = = 这个网络处理视觉信息。

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==Other networks==

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==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" />

*Limbic<ref name="Bassett" /><ref name="Yeo" /><ref name="Bailey" />

*Auditory<ref name="Yuan" /><ref name="Heine" />

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

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

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

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

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

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

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

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*Temporal<ref name="Yeo" /><ref name="Shafiei" />

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* Temporal<ref name="Yeo" /><ref name="Shafiei" />

*Visual perception/imagery<ref name="Hutton" />

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*空间注意

*语言

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*外侧视觉

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* 外侧视觉

*颞视知觉/图像

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==See also==

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== See also==

*[[Complex network]]

*[[Neural network]]

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*

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== References==

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==References==

Shenky20

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大规模脑网络 (查看源代码)

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