Puthanmadam Subramaniyam Narayan, Tronarp Filip, Särkkä Simo, Parkkonen Lauri
Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
Netw Neurosci. 2025 Jul 17;9(3):842-868. doi: 10.1162/netn_a_00453. eCollection 2025.
Current techniques to estimate directed functional connectivity from magnetoencephalography (MEG) signals involve two sequential steps: (a) estimation of the sources and their amplitude time series from the MEG data and (b) estimation of directed interactions between the source time series. However, such a sequential approach is not optimal as it leads to spurious connectivity due to spatial leakage. Here, we present an algorithm to jointly estimate the source and connectivity parameters using Bayesian filtering. We refer to this new algorithm as JEDI-MEG (Joint Estimation of source Dynamics and Interactions from MEG data). By formulating a state-space model for the locations and amplitudes of a given number of sources, we show that estimation of their connections can be reduced to a system identification problem. Using simulated MEG data, we show that the joint approach provides a more accurate reconstruction of connectivity parameters than the conventional two-step approach. Using real MEG responses to visually presented faces in 16 subjects, we also demonstrate that our method gives source and connectivity estimates that are both physiologically plausible and largely consistent across subjects. In conclusion, the proposed joint estimation approach outperforms the traditional two-step approach in determining functional connectivity in MEG data.
目前从脑磁图（MEG）信号估计定向功能连接性的技术涉及两个连续步骤：（a）从MEG数据估计源及其幅度时间序列，以及（b）估计源时间序列之间的定向相互作用。然而，这种顺序方法并非最优，因为由于空间泄漏会导致虚假连接性。在此，我们提出一种使用贝叶斯滤波联合估计源和连接性参数的算法。我们将这种新算法称为JEDI-MEG（从MEG数据联合估计源动态和相互作用）。通过为给定数量的源的位置和幅度制定状态空间模型，我们表明对其连接的估计可以简化为一个系统识别问题。使用模拟的MEG数据，我们表明联合方法比传统的两步法能更准确地重建连接性参数。使用16名受试者对视觉呈现面孔的真实MEG反应，我们还证明我们的方法给出的源和连接性估计在生理上是合理的，并且在受试者之间基本一致。总之，在确定MEG数据中的功能连接性方面，所提出的联合估计方法优于传统的两步法。
