Hincapié Ana-Sofía, Kujala Jan, Mattout Jérémie, Daligault Sebastien, Delpuech Claude, Mery Domingo, Cosmelli Diego, Jerbi Karim
Psychology Department, University of Montreal, Montreal, QC, Canada H2V 2S9; Department of Computer Science, Pontificia Universidad Católica de Chile, 7820436 Santiago de Chile, Chile; School of Psychology and Interdisciplinary Center for Neurosciences, Pontificia Universidad Católica de Chile, 7820436 Santiago de Chile, Chile; Lyon Neuroscience Research Center, DyCog Team, Inserm U1028, CNRS UMR5292, 69675 Bron Cedex, France.
Lyon Neuroscience Research Center, DyCog Team, Inserm U1028, CNRS UMR5292, 69675 Bron Cedex, France; Department of Neuroscience and Biomedical Engineering, Aalto University, 02150 Espoo, Finland.
Comput Intell Neurosci. 2016;2016:3979547. doi: 10.1155/2016/3979547. Epub 2016 Mar 22.
Minimum Norm Estimation (MNE) is an inverse solution method widely used to reconstruct the source time series that underlie magnetoencephalography (MEG) data. MNE addresses the ill-posed nature of MEG source estimation through regularization (e.g., Tikhonov regularization). Selecting the best regularization parameter is a critical step. Generally, once set, it is common practice to keep the same coefficient throughout a study. However, it is yet to be known whether the optimal lambda for spectral power analysis of MEG source data coincides with the optimal regularization for source-level oscillatory coupling analysis. We addressed this question via extensive Monte-Carlo simulations of MEG data, where we generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths. Then, we searched for the Tikhonov regularization coefficients (lambda) that maximize detection performance for (a) power and (b) coherence. For coherence, the optimal lambda was two orders of magnitude smaller than the best lambda for power. Moreover, we found that the spatial extent of the interacting sources and SNR, but not the extent of coupling, were the main parameters affecting the best choice for lambda. Our findings suggest using less regularization when measuring oscillatory coupling compared to power estimation.
最小范数估计（MNE）是一种广泛用于重建脑磁图（MEG）数据基础源时间序列的逆解方法。MNE通过正则化（例如，蒂霍诺夫正则化）解决MEG源估计的不适定性问题。选择最佳正则化参数是关键步骤。一般来说，一旦设定，在整个研究中保持相同系数是常见做法。然而，MEG源数据频谱功率分析的最佳λ是否与源级振荡耦合分析的最佳正则化一致尚不清楚。我们通过对MEG数据进行广泛的蒙特卡罗模拟来解决这个问题，在模拟中我们生成了21600对具有不同大小、信噪比（SNR）和耦合强度的耦合源配置。然后，我们寻找使（a）功率和（b）相干性的检测性能最大化的蒂霍诺夫正则化系数（λ）。对于相干性，最佳λ比功率的最佳λ小两个数量级。此外，我们发现相互作用源的空间范围和SNR，而非耦合程度，是影响λ最佳选择的主要参数。我们的研究结果表明，与功率估计相比，在测量振荡耦合时应使用较少的正则化。
