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Abstract：
This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting-state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between-subject variance arising from Alzheimer\'s disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within-subject between-session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first-level DCMs, we compare model evidence associated with the covariance among subject-specific free energies (i.e., the \'quality\' of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, and between-epochs; (ii) within-subject between-session; and (iii) within-site between-subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of \'reliability\' and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance-based DCMs for resting-state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders.
摘要：
这项研究评估了在基于电导的规范微电路模型下，脑磁图（MEG）的静息状态动态因果模型（DCM）的可靠性，在后验参数估计和模型证据方面。我们使用来自两个会话的静息状态MEG数据，相隔两周，来自一个由阿尔茨海默病引起的受试者间高差异的队列。我们的重点不是疾病的影响，但在方法的可靠性(如主体内会话协议)上，这对未来的疾病进展和药物干预研究至关重要。为了评估一级DCM的可靠性，我们比较与受试者特定自由能之间的协方差相关的模型证据(即，模型的“质量”)与没有类间相关性的对比。然后，我们使用参数经验贝叶斯（PEB）来研究受试者之间推断的DCM参数概率分布之间的差异。具体来说,我们检查了支持或反对参数差异的证据(I)受试者内部，会内,和纪元之间；(Ii)受试者内部会话之间；和(Iii)受试者之间的现场内，适应参数估计之间的条件依赖性。我们表明，对于时间接近的数据，在类似的情况下，超过95%的推断DCM参数不太可能不同，在会话中谈到相互的可预测性。使用PEB，我们显示了“可靠性”的常规定义与推断模型参数之间的条件依赖性之间的相互关系。我们的分析证实了基于电导的DCM对静息状态神经生理数据的可靠性和可重复性。在这方面,内隐生成模型适用于神经和精神疾病的介入和纵向研究。
