Reliability of dynamic causal modelling of resting state magnetoencephalography
Reliability of dynamic causal modelling of resting state magnetoencephalography
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静息态脑磁图动态因果模型的可靠性
DOI：
10.1101/2023.10.16.562379
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Jafarian A
中科院分区：
文献类型：
--
作者：
Jafarian A
本研究从后验参数估计和模型证据两个方面评估了脑磁图(MEG)静息状态动态因果模型(DCM)在基于电导的正则微电路模型下的可靠性。我们使用了两次会议的静息状态脑磁图数据，两次会议相隔2 周，来自阿尔茨海默病患者之间差异较大的队列。我们的重点不是疾病的影响，而是方法的可靠性(如受试者之间的会议间协议)，这对未来疾病进展和药物干预的研究至关重要。为了评估一级DCMS的可靠性，我们比较了与特定受试者自由能之间的协方差(即模型的质量)相关的模型证据与不具有类间相关性的模型证据。然后，我们使用参数经验贝叶斯(PEB)来研究在不同受试者水平上推断的DCM参数概率分布之间的差异。具体地说，我们考察了支持或反对参数差异的证据(I)受试者内、会话内和纪元间；(Ii)受试者内会话间；以及(Iii)受试者内受试者之间适应参数估计之间的条件依赖。我们表明，对于接近时间获得的数据，在类似的情况下，超过95%的推断DCM参数不太可能不同，谈到会议上的相互可预测性。利用PEB，我们证明了“可靠性”的传统定义与推断的模型参数之间的条件相关性之间的倒数关系。我们的分析证实了基于电导的DCMS用于静息状态神经生理学数据的可靠性和重复性。在这方面，内隐生成模型适用于神经和精神障碍的干预性和纵向研究。
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.
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DOI：
10.1162/netn_a_00215
发表时间：
2022-03
期刊：
Network neuroscience (Cambridge, Mass.)
影响因子：
--
作者：
Frässle S;Stephan KE
通讯作者：
Stephan KE
影响因子：
2.9
作者：
Lanskey, Juliette Helene;Kocagoncu, Ece;Quinn, Andrew J.;Cheng, Yun-Ju;Karadag, Melek;Pitt, Jemma;Lowe, Stephen;Perkinton, Michael;Raymont, Vanessa;Singh, Krish D.;Woolrich, Mark;Nobre, Anna C.;Henson, Richard N.;Rowe, James B.
通讯作者：
Rowe, James B.
缓解措施
DOI：
10.22269/180619
发表时间：
2018
期刊：
Places Journal
影响因子：
--
作者：
Elizabeth S. Dodd
通讯作者：
Elizabeth S. Dodd
关于因果建模
DOI：
10.1017/cbo9780511488801.006
发表时间：
2005
期刊：
Places Journal
影响因子：
--
作者：
P. Hedstrom
通讯作者：
P. Hedstrom
影响因子：
5.7
作者：
Shaw AD;Moran RJ;Muthukumaraswamy SD;Brealy J;Linden DE;Friston KJ;Singh KD
通讯作者：
Singh KD