Dynamic causal modelling of evoked potentials: a reproducibility study.
Dynamic causal modelling of evoked potentials: a reproducibility study.
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DOI：
10.1016/j.neuroimage.2007.03.014
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发表时间：
2007-07-01
期刊：
影响因子：
5.7
通讯作者：
Friston KJ
中科院分区：
文献类型：
--
作者：
Garrido MI;Kilner JM;Kiebel SJ;Stephan KE;Friston KJ
动态因果模型（DCM）最近已被应用于事件相关反应（ERP）与EEG/MEG测量。DCM试图解释ERPs使用网络的相互作用的皮质来源和波形差异的耦合变化之间的来源。这项工作的目的是通过评估DCM在受试者中的重现性来确定DCM的有效性。我们使用了一个古怪的范例来引出不匹配的反应。皮质活动的来源被建模为等效的电流偶极子，使用生物物理信息的时空前向模型，包括在每个源中的神经元亚群之间的连接。贝叶斯反演提供了源之间耦合变化的估计值和每个模型的边际可能性。通过指定不同的连接模型，我们能够评估三种不同的假设：在ERP的罕见和频繁的事件的差异是由前向连接（F模型），后向连接（B模型）或两者（FB模型）的变化介导的。研究结果在不同的受试者中具有显著的一致性。除了一个主题外，在所有主题中，前向模型都优于后向模型。这是一个重要的结果，因为这些模型具有相同数量的参数（即，的复杂性）。此外，FB模型在11名受试者中的7名中明显优于两者。这是另一个重要的结果，因为它表明更复杂的模型（可以更准确地拟合数据）不一定是最可能的模型。在组水平上，FB模型随之而来。我们讨论了DCM的有效性和有用性方面的这些发现在表征EEG/MEG数据和它的能力，以机械的方式建模ERP。
Dynamic causal modelling (DCM) has been applied recently to event-related responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the validity of DCM by assessing its reproducibility across subjects. We used an oddball paradigm to elicit mismatch responses. Sources of cortical activity were modelled as equivalent current dipoles, using a biophysical informed spatiotemporal forward model that included connections among neuronal subpopulations in each source. Bayesian inversion provided estimates of changes in coupling among sources and the marginal likelihood of each model. By specifying different connectivity models we were able to evaluate three different hypotheses: differences in the ERPs to rare and frequent events are mediated by changes in forward connections (F-model), backward connections (B-model) or both (FB-model). The results were remarkably consistent over subjects. In all but one subject, the forward model was better than the backward model. This is an important result because these models have the same number of parameters (i.e., the complexity). Furthermore, the FB-model was significantly better than both, in 7 out of 11 subjects. This is another important result because it shows that a more complex model (that can fit the data more accurately) is not necessarily the most likely model. At the group level the FB-model supervened. We discuss these findings in terms of the validity and usefulness of DCM in characterising EEG/MEG data and its ability to model ERPs in a mechanistic fashion.
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影响因子：
5.7
作者：
Opitz, B;Rinne, T;Schröger, E
通讯作者：
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DOI：
10.1098/rstb.2005.1622
发表时间：
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作者：
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通讯作者：
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影响因子：
5.7
作者：
Garrido MI;Kilner JM;Kiebel SJ;Stephan KE;Friston KJ
通讯作者：
Friston KJ