⽂献合集静息态功能连接和脑⽹络分析⽅法
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⽂章来源于微信公众号（茗创科技
静息态脑功能成像是脑功能磁共振成像⽅法的⼀种。正常⼈脑在静息态下依然存在有规律的功能活动⽹络，且病理状态下的脑功能活动⽹络与正常⼈脑存在差异及重塑，被检者处于静息状态下应⽤⾎氧⽔平依赖脑功能成像获得脑活动功能图的成像技术。⽆须进⾏复杂的任务设计，可操作性好，可避免基于任务的研究由于任务设计的不同及被检者执⾏情况的差异性导致的实验结果的不可⽐性。
Seed-bad）、图论（
Graph theory）、独⽴成分分析
）、图论（Graph theory
基于种⼦点⽅法（Seed-bad
以下就静息态功能磁共振成像
静息态功能磁共振成像，及其脑⽹络分析⽅法
脑⽹络分析⽅法：基于种⼦点⽅法（
不同的脑静息态⽹络列举相关⽂献，以供该领域的学者参考。
（ICA）以及不同的脑静息态⽹络
(rs-fMRI)
静息态功能磁共振成像(rs-fMRI
静息态功能磁共振成像
1. Resting statefunctional magnetic resonance imaging:an emerging clinical tool.
doi:10.4103/0028-3886.111107
2. Clinical applicationsof resting state functional connectivity.
doi:10.3389/fnsys.2010.00019
3. Resting state activityin patients with disorders of consciousness.
doi:10.1016/j.yfrne.2010.11.002
4. Resting state fMRI: apersonal history.
doi:10.uroimage.2012.01.090
5. Brain work and brain imaging.
doi:10.uro.29.051605.112819
包括：基于种⼦点⽅法、图论、独⽴成分分析。
这⾥主要介绍⼏种处理静息态fMRI数据，检查脑区之间功能连接的存在和程度的⽅法，包括：基于种⼦点⽅法、图论、独⽴成分分析。
基于种⼦点的分析（Seed-bad analysis）：种⼦点可以是先验定义的区域，或者可以从任务态fMRI实验中获得的激活图中选择，从⽽确基于种⼦点的分析（Seed-bad analysis）：
定特定的感兴趣区域。
1. Functional connectivity in the motor cortex of resting human brain usingecho-planar MRI.
doi: 10.1002/mrm.1910340409
2. Exploring the brain network: a review on resting-state fMRI functionalconnectivity.
doi: 10.1016/j.euroneuro.2010.03.008
3. Review of methods for functional brain connectivity detection using fMRI.
doi: 10.pmedimag.2008.10.011
4. DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-statefMRI.
doi: 10.3389/fnsys.2010.00013
5. Abnormal spontaneous brain activity in minimal hepatic encephalopathy:resting-state fMRI study.
doi: 10.5152/dir.2015.15208
6. A multisite resting state fMRI study on the amplitude of low frequencyfluctuations in schizophrenia.
道德小故事doi: 10.3389/fnins.2013.00137
7. Regional homogeneity approach to fMRI data analysis.
doi:10.uroimage.2003.12.030
8. Competition between functional brain networks mediates behavioralvariability.
doi: 10.uroimage.2007.08.008
9. REST: a toolkit for resting-state functional magnetic resonance imagingdata processing.
doi: 10.1371/journal.pone.0025031
图论（
Graph theory）：
）：⼈脑形成⼀个集成的复杂⽹络，将所有脑区和⼦⽹络连接到⼀个复杂的系统中。使⽤图论分析⽅法可以检查⼤脑⽹络图论（Graph theory
的整体结构，图论提供了⼀个理论框架，其中可以检查复杂⽹络的拓扑，并且可以揭⽰有关功能脑⽹络局部和全局的信息。
1. Social network analysis: a methodological introduction.
doi: 10.1111/j.1467-839X.2007.00241.x
2. A computational study of whole-brain connectivity in resting state andtask fMRI.
doi: 10.12659/MSM.891142和平英语
3. Brain connectivity in autism.
doi:10.3389/fnhum.2014.00349
4. Development of large-scale functional brain networks in children.
doi: 10.1371/journal.pbio.1000157
5. Complex brain networks: graph theoretical analysis of structural andfunctional systems.
我后悔了作文
doi: 10.1038/nrn2618
6. Efficiency and cost of economical brain functional networks.
doi: 10.1371/journal.pcbi.0030017
7. Efficient behavior of smallworld networks.
doi: 10.17877/DE290R-11359
8. Graph-bad network analysis of resting-state functional MRI.
doi: 10.3389/fnsys.2010.00016
9. The ubiquity of small-world networks.
doi: 10.1089/brain.2011.0038
）：静息态fMRI的ICA是⼀种盲源分离⽅法，主要是从静息态中分离出相互独Independent component analysis，ICA）：
独⽴成分分析（Independent component analysis
独⽴成分分析（
⽴的源。这个⽅法可以应⽤于全脑功能连接，将fMRI分离出⼤尺度脑⽹络。
1. Exploring the brain network: a review on resting-state fMRI functionalconnectivity.
doi: 10.1016/j.euroneuro.2010.03.008
2. Advances and pitfalls in the analysis and interpretation of restingstatefMRI data.
doi: 10.3389/fnsys.2010.00008
3. An information-maximization approach to blind paration and blinddeconvolution.
doi: 10.1162/neco.1995.7.6.1129
4. Analysis of fMRI data by blind paration into independent spatialcomponents.
doi: 10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1
5. Intrinsic brain activity in altered states of consciousness: howconscious is the default mode of brain function?
管理制度范本
doi: 10.1196/annals.1417.015
6. Group comparison of resting-state FMRI data using multi-subject ICA anddual regression.
doi: 10.1016/S1053-8119(09)71511-3
7. A review of group ICA for fMRI data and ICA for joint inference ofimaging, genetic, and ERP data.
doi: 10.uroimage.2008.10.057
8. A unified framework for group independent component analysis formulti-subject fMRI data.
doi: 10.uroimage.2008.05.008
9. Independent component analysis of fMRI group studies by lf-organizingclustering.
梦见去厕所
doi: 10.uroimage.2004.10.042
10. Comparison of three methods for generating group statistical inferencesfrom independent component analysis of functional magnetic resonance imagingdata.
doi: 10.1002/jmri.20009
脑静息态⽹络，如突显⽹络、听觉⽹络、基底神经节⽹络、视觉⽹络、视觉空间⽹络、默认模式⽹络、语⾔⽹络、执⾏⽹络&以下是关于不同的脑静息态⽹络
执⾏控制⽹络、楔前叶⽹络、感觉运动⽹络等相关⽂献合集。
突显⽹络
1. Cognitive Control and the Salience Network: An Investigation of ErrorProcessing and Effective Connectivity.
doi: 10.1523/JNEUROSCI.4692-12.2013
2. Salience processing and insular cortical function and dysfunction.
doi: 10.1038/nrn3857
3. Saliency, switching, attention and control: a network model of insulafunction.
doi: 10.1007/s00429-010-0262-0
听觉⽹络
1. Asymmetric Interhemispheric Transfer in the Auditory Network: Evidencefrom TMS, Resting-State fMRI, and Diffusion Imaging.
doi: 10.1523/JNEUROSCI.2333-15.2015
2. Default Mode, Dorsal Attention and Auditory Resting State NetworksExhibit Differential Functional Connectivity in Tinnitus and Hearing Loss.
doi: 10.1371/journal.pone.0076488
基底神经节⽹络
1. Aberrant functional connectivity within the basal ganglia of patientswith Parkinson’s dia.
doi: 10.1016/j.nicl.2015.04.003
2. Functional connectivity in the basal ganglia network differentiates PDpatients from controls.
doi: 10.1212/wnl.0000000000000592
3. Identifying the Basal Ganglia Network Model Markers forMedication-Induced Impulsivity in Parkins
on's Dia Patients.宣汉
doi: 10.1371/journal.pone.0127542
4. The basal ganglia: A neural network with more than motor function.
doi: 10.1016/S1071-9091(02)00003-7
视觉⽹络
1. Consistent resting-state networks across healthysubjects.
doi: 10.1073/pnas.0601417103
2. Investigations into resting-stateconnectivity using independent component analysis.
doi: 10. 1098/rstb.2005.1634
3. Spontaneous Activity Associated with PrimaryVisual Cortex: A Resting-State fMRI Study.
doi: 10.1093/cercor/bhm105
视觉空间⽹络
1. Default-mode network activity distinguishes Alzheimer’sdia from healthy aging: Evidence from functional MRI.
doi: 10.1073/pnas.0308627101
2. Functional connectivity in the resting brain: A network analysis of thedefault mode hypothesis.
聪明丈夫doi: 10.1073/pnas.0135058100
3. Investigations into Resting-State Connectivity Using IndependentComponent Analysis.
doi: 10.1098/rsbt.2005.1634
4.Searching for a baline: functional imaging andthe resting human brain.
doi: 10.1038/35094500
默认模式⽹络
1. Development of the Default Mode and CentralExecutive Networks across early adolescence: A longitudinal study.
doi: 10.1016/j.dcn.2014.08.002
2. Searching for a baline: functional imaging and the resting human brain.
doi: 10.1038/35094500
语⾔⽹络
1. Evidenceof Mirror Neurons in Human Inferior Frontal Gyrus.
doi: 10.1523/JNEUROSCI.2668-09.2009
2. How Localized are Language Brain Areas? A Review of Brodmann Areas Involvementin Oral Language.
doi: 10.1093/arclin/acv081
3. Mirror Neurons and the Lateralization of Human Language.
doi: 10.1523/JNEUROSCI.1452-06.2006
4. Speech-associated gestures, Broca’s area, and the human mirror system.
doi: 10.1016/j.bandl.2007.02.008
执⾏⽹络&&执⾏控制⽹络
执⾏⽹络
1. ConceptualProcessing during the Conscious Resting State: A Functional MRI Study.
doi: 10.1162/089892999563265
2. Dissociable Intrinsic Connectivity Networks for Salience Processing andExecutive Control.
doi: 10.1523/JNEUROSCI.5587-06.2007
3. Resting-state activity in the left executive control network isassociated with behavioral approach and is incread in substance dependence.
doi: 10.1016/j.drugalcdep.2014.02.320
4. Searching for Activations That Generalize Over Tasks.
doi: 10.1002/(SICI)1097-0193(1997)5:4<317::AID-HBM19>3.0.CO;2-A
5. The Human Brain Is Intrinsically Organized into Dynamic, AnticorrelatedFunctional Networks.
打狼是什么意思doi: 10.1073/pnas.0504136102
楔前叶⽹络
