43
个编辑
更改
跳到导航
跳到搜索
第2行：
第2行： − + − + 第13行：
第13行： − + − + − + − + − +
→Core networks
{{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>{{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>{{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>{{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>
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 or other recording methods such as EEG, PET and MEG. 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. Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.
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 or other recording methods such as EEG, PET and MEG. 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. Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.
'''<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>'''信号的统计分析或其他记录方法(如'''<font color="#ff8000">脑电图EEG</font>'''、'''<font color="#ff8000">正电子发射断层扫描技术PET</font>'''和'''<font color="#ff8000">脑磁图MEG</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。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。
'''<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" />
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]].
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.
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.
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'''<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，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。
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>
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. 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. The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.
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. 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. The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.
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。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。</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。
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" />
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>{{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>{{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>
Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia, ADHD and bipolar disorder.
Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia, ADHD and bipolar disorder.
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</font>'''和'''<font color="#ff8000">躁郁症Bipolar disorder</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==