“大规模脑网络”的版本间的差异

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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 Functional magnetic resonance imaging(fMRI)信号<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>的统计分析或其他记录方法(如'''脑电图 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>'''、'''正电子发射断层扫描技术 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>'''和'''脑磁图 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>''')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">{{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>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>{{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 Functional magnetic resonance imaging(fMRI)信号<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>的统计分析或其他记录方法(如'''脑电图 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>'''、'''正电子发射断层扫描技术 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}}</ref>'''和'''脑磁图 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>''')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">{{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>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>{{cite journal|last1=Bressler|first1=Steven L.|last2=Menon|first2=Vinod|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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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、老年痴呆症、自闭症谱系障碍、精神分裂症、多动症<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>'''和'''躁郁症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,如抑郁症、老年痴呆症、自闭症谱系障碍、精神分裂症、多动症<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>'''和'''躁郁症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|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>'''。
  
  
 
==核心网络==
 
==核心网络==
 
[[File:Heine2012x3010.png|thumb|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" />]]
 
[[File:Heine2012x3010.png|thumb|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" />]]
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">{{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>在承认这个问题的同时,Uddin,Yeo,和Spreng在2019年提出<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>,综合考虑来自多个研究的证据<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>,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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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">{{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|url=http://juser.fz-juelich.de/record/856633/files/Eickhoff_Yeo_Genon_NRN_MainManuscriptInclFigures.pdf}}</ref>在承认这个问题的同时,Uddin,Yeo,和Spreng在2019年提出<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>,综合考虑来自多个研究的证据<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>,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。
  
  
 
===默认模式网络(内侧额顶骨)===
 
===默认模式网络(内侧额顶骨)===
*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">{{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">
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*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">{{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.}}</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>
  

2022年4月10日 (日) 15:52的版本


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 Functional magnetic resonance imaging(fMRI)信号[1]的统计分析或其他记录方法(如脑电图 Electroencephalography(EEG)[2]正电子发射断层扫描技术 Positron emission tomography(PET)[3]脑磁图 Magnetoencephalography(MEG)[4])中,表现出功能连接 Functional connectivity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]


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,同时图论动力学系统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]


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、老年痴呆症、自闭症谱系障碍、精神分裂症、多动症[11]躁郁症Bipolar disorder[12]


核心网络

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。


默认模式网络(内侧额顶骨)

  • 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]


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) the anterior (bilateral) insula, dorsal anterior cingulate cortex, and three subcortical structures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]


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]


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]


感觉运动网络(中央区域)

  • 这个网络参与了躯体感觉信息的加工和运动的协调。[13][8][21][10][19]听觉皮层 Auditory cortex可能也包括在内。[15][8]


视觉网络(枕部)

  • 这个网络处理视觉信息。[31]


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]


另见


参考文献

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