Multi-subject analyses with dynamic causal modeling.
Multi-subject analyses with dynamic causal modeling.
复制标题
DOI：
10.1016/j.neuroimage.2009.11.037
复制
发表时间：
2010-02-15
期刊：
影响因子：
5.7
通讯作者：
Windischberger, Christian
中科院分区：
文献类型：
--
作者：
Kasess, Christian Herbert;Stephan, Klaas Enno;Weissenbacher, Andreas;Pezawas, Lukas;Moser, Ewald;Windischberger, Christian
目前，大多数采用动态因果模型（DCM）的研究使用随机效应（RFX）分析来进行群体推论，对被试的参数估计应用二级频率检验。然而，在某些情况下，固定效应（FFX）分析可能更合适。这种分析可以通过根据贝叶斯定理在多变量（贝叶斯参数平均或BPA）或单变量基础（后验方差加权平均或PVWA）上结合受试者的后验密度来实现，或者通过将DCM应用于受试者事先的时间序列平均（时间平均或TA）来实现。虽然所有这些FFX方法都具有允许对参数进行贝叶斯推断的优势，但迄今为止还缺乏对其统计特性的系统比较。基于双区域网络生成的模拟数据，我们研究了信噪比（SNR）和种群异质性对群体水平参数估计的影响。数据集的模拟假设是一个均匀的大群体（N=60），具有恒定的跨主题连接，或者是一个具有不同参数的异质群体。TA在较低信噪比下表现出优势，但适用性有限。由于BPA和PVWA考虑了后验（co）方差结构，仅考虑后验均值时所得结果不直观。这个问题与高信噪比数据、明显的参数相互依赖性以及FFX假设被违反（即非同质组）有关。它随着信噪比的降低而减少，对于具有独立参数的模型或FFX假设适当时不存在。因此，使用这些FFX方法获得的组结果应该通过考虑模型参数之间的依赖估计来仔细解释。
Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis to make group inferences, applying a second-level frequentist test to subjects’ parameter estimates. In some instances, however, fixed-effects (FFX) analysis can be more appropriate. Such analyses can be implemented by combining the subjects’ posterior densities according to Bayes’ theorem either on a multivariate (Bayesian parameter averaging or BPA) or univariate basis (posterior variance weighted averaging or PVWA), or by applying DCM to time-series averaged across subjects beforehand (temporal averaging or TA). While all these FFX approaches have the advantage of allowing for Bayesian inferences on parameters a systematic comparison of their statistical properties has been lacking so far. Based on simulated data generated from a two-region network we examined the effects of signal-to-noise ratio (SNR) and population heterogeneity on group-level parameter estimates. Data sets were simulated assuming either a homogeneous large population (N=60) with constant connectivities across subjects or a heterogeneous population with varying parameters. TA showed advantages at lower SNR but is limited in its applicability. Because BPA and PVWA take into account posterior (co)variance structure, they can yield non-intuitive results when only considering posterior means. This problem is relevant for high SNR data, pronounced parameter interdependencies and when FFX assumptions are violated (i.e. inhomogeneous groups). It diminishes with decreasing SNR and is absent for models with independent parameters or when FFX assumptions are appropriate. Group results obtained with these FFX approaches should therefore be interpreted carefully by considering estimates of dependencies among model parameters.
登录
查看更多内容
影响因子：
5.7
作者：
Li, Junning;Wang, Z. Jane;McKeown, Martin J.
通讯作者：
McKeown, Martin J.
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
DOI：
10.1097/00004647-199701000-00009
发表时间：
1997-01-01
影响因子：
6.3
作者：
Buxton, RB;Frank, LR
通讯作者：
Frank, LR
影响因子：
1.9
作者：
GRANGER, CWJ
通讯作者：
GRANGER, CWJ
影响因子：
5.7
作者：
Friston, KJ;Stephan, KE;Kiebel, S
通讯作者：
Kiebel, S