Stochastic dynamic causal modelling of fMRI data: should we care about neural noise?
Stochastic dynamic causal modelling of fMRI data: should we care about neural noise?
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DOI：
10.1016/j.neuroimage.2012.04.061
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发表时间：
2012-08-01
期刊：
影响因子：
5.7
通讯作者：
Friston, K. J.
中科院分区：
文献类型：
--
作者：
Daunizeau, J.;Stephan, K. E.;Friston, K. J.
动态因果模型（DCM）被引入使用神经成像数据来研究大脑区域之间的有效连接。直到最近，DCM依赖于分布式神经元对外部扰动的响应的确定性模型（例如，感官刺激或任务需求）。然而，占随机波动的神经元活动和它们的相互作用与特定任务的过程可能是特别重要的研究状态依赖的相互作用。此外，允许随机神经元波动可以使DCM对网络识别的模型错误指定和精细化问题更鲁棒。在这篇文章中，我们研究了随机动态因果模型（sDCM）与其确定性模型（dDCM）的关系，并强调了只能用sDCM解决的问题。我们还比较了确定性和随机DCM的网络识别性能，使用Monte Carlo模拟和失神癫痫的实证案例研究。例如，我们的研究结果表明，随机DCM可以利用神经噪声的建模来区分直接和介导的连接。最后，我们讨论了sDCM的附加值和局限性，在其确定性同系物。fMRI时间序列的随机DCM分析：失神发作的途径。►神经噪声对网络识别影响的理论和数值。使用DCM建模神经噪声的利弊。癫痫活动期间状态依赖可塑性的定量建模。
Dynamic causal modelling (DCM) was introduced to study the effective connectivity among brain regions using neuroimaging data. Until recently, DCM relied on deterministic models of distributed neuronal responses to external perturbation (e.g., sensory stimulation or task demands). However, accounting for stochastic fluctuations in neuronal activity and their interaction with task-specific processes may be of particular importance for studying state-dependent interactions. Furthermore, allowing for random neuronal fluctuations may render DCM more robust to model misspecification and finesse problems with network identification. In this article, we examine stochastic dynamic causal models (sDCM) in relation to their deterministic counterparts (dDCM) and highlight questions that can only be addressed with sDCM. We also compare the network identification performance of deterministic and stochastic DCM, using Monte Carlo simulations and an empirical case study of absence epilepsy. For example, our results demonstrate that stochastic DCM can exploit the modelling of neural noise to discriminate between direct and mediated connections. We conclude with a discussion of the added value and limitations of sDCM, in relation to its deterministic homologue. Stochastic DCM analysis of fMRI time series: the route to absence seizure. ► Theory and numerics of the impact of neural noise on network identification. ► Pros and cons of modelling neural noise with DCM. ► Quantitative modelling of state-dependant plasticity during epileptogenic activity.
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DOI：
10.1016/j.physd.2009.08.002
发表时间：
2009-11-01
期刊：
Physica D. Nonlinear phenomena
影响因子：
--
作者：
Daunizeau J;Friston KJ;Kiebel SJ
通讯作者：
Kiebel SJ
影响因子：
3.7
作者：
HOFBAUER, J;SO, JWH
通讯作者：
SO, JWH
影响因子：
5.7
作者：
Friston, Karl J.;Mattout, Jeremie;Penny, Will
通讯作者：
Penny, Will
影响因子：
5.7
作者：
Friston, Karl J.;Penny, Will
通讯作者：
Penny, Will
影响因子：
5.7
作者：
Hamandi, Khalid;Salek-Haddadi, Afraim;Lemieux, Louis
通讯作者：
Lemieux, Louis