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{{Short description|Collections of brain regions}}
{{Short description|Collections of brain regions}}
'''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>
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>'''。
== 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 name=":7">{{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 name=":8">{{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 name=":9">{{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 name=":7">{{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 name=":8">{{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 name=":9">{{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.
=<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" />】
【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" />】
=<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" />
*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)===
==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" />
*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>=
=<nowiki>= = 其他网络 = =</nowiki>=
*听觉网络<ref name="Yuan" /><ref name="Heine" />
*听觉网络<ref name="Yuan" /><ref name="Heine" />
*右/左执行网络<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" />
* 小脑网络<ref name="Bell" /><ref name="Heine" />
*空间注意网络<ref name="Riedl" /><ref name="Bressler" />
*空间注意网络<ref name="Riedl" /><ref name="Bressler" />
*语言网络<ref name="Bressler" /><ref name="Hutton" />
*语言网络<ref name="Bressler" /><ref name="Hutton" />
=<nowiki>= = 相关词条 = =</nowiki>=
=<nowiki>= = 相关词条 = =</nowiki>=
*复杂网络
*[[复杂网络]]
*神经网络
*[[神经网络]]
==References==
==References==