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基于SPM动态因果模型操作练习.ppt 25页
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基于SPM动态因果模型操作练习.ppt
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Dynamic Causal Modeling (DCM) A Practical Perspective
Ollie Hulme Barrie Roulston Zeki lab Structure 1. Quick recap on what DCM can do for you. 2. What to think about when designing a DCM experiment 3. How to do DCM. What buttons to press etc. * Disclaimer
The following speakers have never used DCM. Any impression of expertise or experience is entirely accidental.
A Re-cap for Dummies
You can ask different types of questions about brain processing.
Questions of Where
Questions of How
Functional Specialization is a question of Where? Where in the brain is a certain cognitive/perceptual attribute processed? What are the Regionally specific effects ?your normal SPM analysis (GLM)
Functional Integration is a question of
Experimentally designed input How does the system work?
What are the inter-regional effects? How do the components of the system interact with each other? MODEL-FREE MODEL-DEPENDENT Hypothesis driven
DCM! Functional connectivity = the temporal correlation between
spatially remote areas Effective connectivity = the influence one area exerts over another 2 Categories of Functional integration analysis PPI
DCM overview DCM allows you model brain activity at the neuronal level (which is not directly accessible in fMRI) taking into account the anatomical architecture of the system and the interactions within that architecture under different conditions of stimulus input and context. The modelled neuronal dynamics (z) are transformed into area-specific BOLD signals (y) by a hemodynamic forward model (λ). The aim of DCM is to estimate parameters at the neuronal level so that the modelled BOLD signals are most similar to the experimentally measured BOLD signals. Planning a DCM-compatible study Experimental design: preferably multi-factorial (e.g. at least 2 x 2)
Static Moving No attent Attent.
1.Sensory input factor
At least one factor that varies the sensory input… changing the stimulus… a perturbation to the system
正在加载中，请稍后...数据的动态因果模型
dynamic causal modeling
罗劲博士课题组利用基于功能性核磁共振成像（fMRI）数据的动态因果模型（dynamic causal modeling, DCM）分析技术，研究了人们在做顿悟性拆字难题（如从“学..
基于2个网页-
罗劲博士课题组利用基于功能性核磁共振成像（fMRI）数据的动态因果模型（dynamic causal modeling, DCM）分析技术，研究了人们在做顿悟性拆字难题（如从“学..
基于1个网页-
在实证研究中，本文对相关数据进行了协整和格兰杰因果检验，并通过误差修正模型研究了纺织品服装出口影响要素的短期动态关系。
In the empirical study, the synergistic integration and Granger causality test, and error correction model by textile clothing effect factor of the short term dynamics.
提出一种结构方程模型的动态预测建模方法，从而可以在无须未来样本数据的情况下，预测系统要素之间未来的因果关系。
Based on the historical data, a forecast modeling method for structural equation model was discussed, where the future relationship between the system factors was described without future sample.
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感谢您的反馈，我们会尽快进行适当修改！基于动态因果模型的视觉工作记忆任务脑效应网络研究
Study on Brain Effective Network during Visual Working Memory Task Based on Dynamic Causal Model
目的 从多通道脑电(electroencephalographys,EEGs)的效应连接角度,探究工作记忆的脑网络机制.方法 收集15名健康志愿者的32通道脑电数据,通过源定位确定视觉工作记忆任务的激活脑区,在此基础上进行动态因果模型(DCM)分析,计算各连接的超越概率,构建工作记忆脑效应网络.结果 双侧顶叶(PAR)到同侧背外侧前额叶(DLPFC)和双侧顶叶(PAR)到同侧额中线皮层(MFC)连接的超越概率大于其它连接.模型中正向连接簇的超越概率EP_forward大于反向连接簇的超越概率EP_backward和横向连接簇的超越概率EP_lateral(P＜ 0.01).结论 视觉工作记忆过程中,双侧顶叶与同侧背外侧前额叶、额中线皮层之间有信息交流,顶叶皮层的信息流向前额叶皮层.
Abstract：
Objective To explore the brain network mechanism of visual working memory from the perspective of effectivity connectivity of multi-channel electroencephalogram (EEGs).Methods 32-channel EEGs were recorded in 15 healthy subjects and the activated brain regions of visual working memory were determined by source localization.On this basis,the dynamic causal modeling (DCM) analysis was performed.The effective network of working memory was constructed by calculating the exceedance probability of each connection.Results Compared to other connections,greater exceedance probability of connections from bilateral parietal cortex (PAR) to the ipsilateral dorsolateral prefrontal cortex (DLPFC) and from bilateral parietal cortex (PAR)to the ipsilateral middleline-frontal cortex (MFC) were observed.In the DCM,the exceedance probability of the forward family(EP_forward) was greater than that of the backward family (EP_backward) and the lateral family (EP_lateral,P ＜ 0.01).Conclusion During visual working memory,information exchange of the bilateral parietal cortex with the ipsilateral dorsolateral prefrontal cortex and the middleline-frontal cortex existed,and the information inflowed from the parietal cortex to the prefrontal cortex.
Zhang Ting
Zheng Xuyuan
天津医科大学生物医学工程与技术学院,天津,300070
& ISTICPKU
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