DCM for complex-valued data: cross-spectra, coherence and phase-delays.
DCM for complex-valued data: cross-spectra, coherence and phase-delays.
复制标题
DOI：
10.1016/j.neuroimage.2011.07.048
复制
发表时间：
2012-01-02
期刊：
影响因子：
5.7
通讯作者：
Moran RJ
中科院分区：
文献类型：
--
作者：
Friston KJ;Bastos A;Litvak V;Stephan KE;Fries P;Moran RJ
本文描述了贝叶斯模型反演过程的扩展，用于复值数据的动态因果建模（DCM）。对复杂数据建模在分析多变量遍历（平稳）时间序列时特别有用。我们说明了这一点与广义DCM的稳态响应模型的真实的和虚部的样品互谱。DCM允许人们推断产生数据的潜在生物物理参数（如突触时间常数、连接强度和传导延迟）。由于传递函数和复杂的互谱可以从这些参数中产生，因此也可以根据常规（线性系统）测量来描述隐式系统架构;如相干性、相位延迟或互相关函数。至关重要的是，这些措施可以在传感器和源空间。换句话说，可以使用非侵入性数据检查隐藏的神经元源之间的互相关或相位延迟函数，并将这些函数与突触参数和神经元传导延迟相关联。我们说明了这些点，使用当地的场电位记录从丘脑底核和苍白球，特别注重传导延迟和随后的相位关系和交叉相关的时间滞后之间的关系人口活动。我们使用DCM来预测时间序列记录中的相干性和相位差。我们将变分贝叶斯技术推广到复杂数据。传统的时间序列测量充满了合理的生物物理形式。我们强调了源和传感器级别描述符的差异。
This note describes an extension of Bayesian model inversion procedures for the Dynamic Causal Modeling (DCM) of complex-valued data. Modeling complex data can be particularly useful in the analysis of multivariate ergodic (stationary) time-series. We illustrate this with a generalization of DCM for steady-state responses that models both the real and imaginary parts of sample cross-spectra. DCM allows one to infer underlying biophysical parameters generating data (like synaptic time constants, connection strengths and conduction delays). Because transfer functions and complex cross-spectra can be generated from these parameters, one can also describe the implicit system architecture in terms of conventional (linear systems) measures; like coherence, phase-delay or cross-correlation functions. Crucially, these measures can be derived in both sensor and source-space. In other words, one can examine the cross-correlation or phase-delay functions between hidden neuronal sources using non-invasive data and relate these functions to synaptic parameters and neuronal conduction delays. We illustrate these points using local field potential recordings from the subthalamic nucleus and globus pallidus, with a special focus on the relationship between conduction delays and the ensuing phase relationships and cross-correlation time lags between population activities. ► We use DCM to predict coherence and phase-differences in time-series recordings. ► We generalize variational Bayesian techniques for application to complex data. ► Conventional time-series measures are imbued with plausible biophysical form. ► We highlight differences in source and sensor level descriptors.
登录
查看更多内容
DOI：
10.1073/pnas.0308538101
发表时间：
2004-06-29
影响因子：
11.1
作者：
Brovelli, A;Ding, MZ;Bressler, SL
通讯作者：
Bressler, SL
DOI：
10.1073/pnas.0901831106
发表时间：
2009-06-23
影响因子：
11.1
作者：
Deco, Gustavo;Jirsa, Viktor;Koetter, Rolf
通讯作者：
Koetter, Rolf
影响因子：
20.6
作者：
CARTER, GC
通讯作者：
CARTER, GC
影响因子：
3.7
作者：
Felleman, Daniel J.;Van Essen, David C.
通讯作者：
Van Essen, David C.
影响因子：
3.7
作者：
Breakspear, M.;Roberts, J. A.;Robinson, P. A.
通讯作者：
Robinson, P. A.