文献合集 | 静息态功能连接和脑网络分析方法

2024-06-14 17:32

本文主要是介绍文献合集 | 静息态功能连接和脑网络分析方法,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

文章来源于微信公众号(茗创科技),欢迎有兴趣的朋友搜索关注。

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。

以下就静息态功能磁共振成像,及其脑网络分析方法基于种子点方法(Seed-based)、图论(Graph theory)、独立成分分析(ICA以及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。

静息态功能磁共振成像(rs-fMRI)

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:10.1016/j.neuroimage.2012.01.090

5. Brain work and brain imaging.

doi:10.1146/annurev.neuro.29.051605.112819

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-based 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: 10.1016/j.compmedimag.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.

doi: 10.3389/fnins.2013.00137

7. Regional homogeneity approach to fMRI data analysis.

doi:10.1016/j.neuroimage.2003.12.030

8. Competition between functional brain networks mediates behavioralvariability.

doi: 10.1016/j.neuroimage.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

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.

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-based 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

独立成分分析(Independent component analysisICA):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 separation and blinddeconvolution.

doi: 10.1162/neco.1995.7.6.1129

4. Analysis of fMRI data by blind separation 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?

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: 10.1016/j.neuroimage.2008.10.057

8. A unified framework for group independent component analysis formulti-subject fMRI data.

doi: 10.1016/j.neuroimage.2008.05.008

9. Independent component analysis of fMRI group studies by self-organizingclustering.

doi: 10.1016/j.neuroimage.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

以下是关于不同的脑静息态网络,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&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.007648

基底神经节网络

1. Aberrant functional connectivity within the basal ganglia of patientswith Parkinson’s disease.

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 Parkinson's Disease Patients.

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’sdisease 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.

doi: 10.1073/pnas.0135058100

3. Investigations into Resting-State Connectivity Using IndependentComponent Analysis.

doi: 10.1098/rsbt.2005.1634

4. Searching for a baseline: 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 baseline: 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

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 increased 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.

doi: 10.1073/pnas.0504136102

楔前叶网络

1. Posterior Cingulate Cortex Activation by EmotionalWords: fMRI Evidence From a Valence Decision Task.

doi: 10.1002/hbm.10075

2. Posterior Cingulate Cortex Mediates Outcome-Contingent Allocation ofBehavior.

doi: 10.1016/j.neuron.2008.09.012

3. Precuneus Is a Functional Core of the Default-Mode Network.

doi: 10.1523/JNEUROSCI.4227-13.2014

4. Remembering familiar people: the posterior cingulate cortex andautobiographical memory retrieval.

doi: 10.1016/S0306-4522(01)00108-7

5. The precuneus/posterior cingulate cortex plays a pivotal role in thedefault mode network: Evidence from a partial correlation network analysis.

doi: 10.1016/j.neuroimage.2008.05.059

6. The precuneus: a review of its functional anatomy and behaviouralcorrelates.

doi: 10.1093/brain/awl004

感觉运动网络

1. A small number of abnormal brain connections predictsadult autism spectrum disorder.

doi: 10.1038/ncomms11254

2. Functional Connectivity in the Motor Cortex of Resting Human Brain UsingEcho-Planar MRI.

doi: 10.1002/mrm.1910340409

3. Identifying patients with Alzheimer’s disease using resting-state fMRI andgraph theory.

doi: 10.1016/j.clinph.2015.02.060

4. Recovery of resting brain connectivity ensuing mild traumatic braininjury.

doi: 10.3389/fnhum.2015.00513

这篇关于文献合集 | 静息态功能连接和脑网络分析方法的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/1061056

相关文章

VScode连接远程Linux服务器环境配置图文教程

《VScode连接远程Linux服务器环境配置图文教程》:本文主要介绍如何安装和配置VSCode,包括安装步骤、环境配置(如汉化包、远程SSH连接)、语言包安装(如C/C++插件)等,文中给出了详... 目录一、安装vscode二、环境配置1.中文汉化包2.安装remote-ssh,用于远程连接2.1安装2

C语言小项目实战之通讯录功能

《C语言小项目实战之通讯录功能》:本文主要介绍如何设计和实现一个简单的通讯录管理系统,包括联系人信息的存储、增加、删除、查找、修改和排序等功能,文中通过代码介绍的非常详细,需要的朋友可以参考下... 目录功能介绍:添加联系人模块显示联系人模块删除联系人模块查找联系人模块修改联系人模块排序联系人模块源代码如下

Java中使用Java Mail实现邮件服务功能示例

《Java中使用JavaMail实现邮件服务功能示例》:本文主要介绍Java中使用JavaMail实现邮件服务功能的相关资料,文章还提供了一个发送邮件的示例代码,包括创建参数类、邮件类和执行结... 目录前言一、历史背景二编程、pom依赖三、API说明(一)Session (会话)(二)Message编程客

Python判断for循环最后一次的6种方法

《Python判断for循环最后一次的6种方法》在Python中,通常我们不会直接判断for循环是否正在执行最后一次迭代,因为Python的for循环是基于可迭代对象的,它不知道也不关心迭代的内部状态... 目录1.使用enuhttp://www.chinasem.cnmerate()和len()来判断for

Java循环创建对象内存溢出的解决方法

《Java循环创建对象内存溢出的解决方法》在Java中,如果在循环中不当地创建大量对象而不及时释放内存,很容易导致内存溢出(OutOfMemoryError),所以本文给大家介绍了Java循环创建对象... 目录问题1. 解决方案2. 示例代码2.1 原始版本(可能导致内存溢出)2.2 修改后的版本问题在

关于rpc长连接与短连接的思考记录

《关于rpc长连接与短连接的思考记录》文章总结了RPC项目中长连接和短连接的处理方式,包括RPC和HTTP的长连接与短连接的区别、TCP的保活机制、客户端与服务器的连接模式及其利弊分析,文章强调了在实... 目录rpc项目中的长连接与短连接的思考什么是rpc项目中的长连接和短连接与tcp和http的长连接短

Java CompletableFuture如何实现超时功能

《JavaCompletableFuture如何实现超时功能》:本文主要介绍实现超时功能的基本思路以及CompletableFuture(之后简称CF)是如何通过代码实现超时功能的,需要的... 目录基本思路CompletableFuture 的实现1. 基本实现流程2. 静态条件分析3. 内存泄露 bug

四种Flutter子页面向父组件传递数据的方法介绍

《四种Flutter子页面向父组件传递数据的方法介绍》在Flutter中,如果父组件需要调用子组件的方法,可以通过常用的四种方式实现,文中的示例代码讲解详细,感兴趣的小伙伴可以跟随小编一起学习一下... 目录方法 1:使用 GlobalKey 和 State 调用子组件方法方法 2:通过回调函数(Callb

一文详解Python中数据清洗与处理的常用方法

《一文详解Python中数据清洗与处理的常用方法》在数据处理与分析过程中,缺失值、重复值、异常值等问题是常见的挑战,本文总结了多种数据清洗与处理方法,文中的示例代码简洁易懂,有需要的小伙伴可以参考下... 目录缺失值处理重复值处理异常值处理数据类型转换文本清洗数据分组统计数据分箱数据标准化在数据处理与分析过

Java中Object类的常用方法小结

《Java中Object类的常用方法小结》JavaObject类是所有类的父类,位于java.lang包中,本文为大家整理了一些Object类的常用方法,感兴趣的小伙伴可以跟随小编一起学习一下... 目录1. public boolean equals(Object obj)2. public int ha