Key Laboratory of Clinical and Medical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China; Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France.
Univ Rennes, Inserm, LTSI, UMR 1099, Rennes F-35000, France; Univ Rennes, Inserm, SEU, LIA - Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Rennes F-35000, France.
Comput Methods Programs Biomed. 2022 Jun;221:106840. doi: 10.1016/j.cmpb.2022.106840. Epub 2022 Apr 28.
Recently, spectral Dynamic Causal Modelling (DCM) has been used increasingly to infer effective connectivity from epileptic intracranial electroencephalographic (iEEG) signals. In this context, the Physiology-Based Model (PBM), a neural mass model, is used as a generative model. However, previous studies have highlighted out the inability of PBM to properly describe iEEG signals with specific power spectral densities (PSDs). More precisely, PSDs that have multiple peaks around β and γ rhythms (i.e. spectral characteristics at seizure onset) are concerned.
To cope with this limitation, an alternative neural mass model, called the complete PBM (cPBM), is investigated. The spectral DCM and two recent variants are used to evaluate the relevance of cPBM over PBM.
The study is conducted on both simulated signals and real epileptic iEEG recordings. Our results confirm that, compared to PBM, cPBM shows (i) more ability to model the desired PSDs and (ii) lower numerical complexity whatever the method.
Thanks to its intrinsic and extrinsic connectivity parameters as well as the input coming into the fast inhibitory subpopulation, the cPBM provides a more expressive model of PSDs, leading to a better understanding of epileptic patterns and DCM-based effective connectivity inference.
最近，光谱动态因果建模（DCM）越来越多地被用于从癫痫颅内脑电图（iEEG）信号中推断有效连通性。在这种情况下，生理模型（PBM），一种神经群体模型，被用作生成模型。然而，以前的研究已经强调了 PBM 无法正确描述具有特定功率谱密度（PSD）的 iEEG 信号。更确切地说，关注的是具有多个β和γ节律（即癫痫发作开始时的光谱特征）附近峰值的 PSD。
为了应对这一限制，研究了一种替代的神经群体模型，称为完全 PBM（cPBM）。使用光谱 DCM 和最近的两种变体来评估 cPBM 相对于 PBM 的相关性。
该研究在模拟信号和真实的癫痫 iEEG 记录上进行。我们的结果证实，与 PBM 相比，cPBM 显示出（i）更能模拟所需的 PSD，以及（ii）无论方法如何，数值复杂性更低。
由于其内在和外在连接参数以及进入快速抑制亚群的输入，cPBM 提供了更具表现力的 PSD 模型，从而更好地理解癫痫模式和基于 DCM 的有效连通性推断。
