论文标题
光谱复杂性在连通性估计中的作用
The role of spectral complexity in connectivity estimation
论文作者
论文摘要
对磁性磁造影（MEG）数据的功能连通性的研究包括量化时间序列之间描述来自头皮外记录的磁场的不同神经源活性的统计依赖性。可以通过利用频域中计算的连接度量来解决此问题，通常依赖于通过解决MEG逆问题估计的神经时间序列的跨功率谱的评估。最近的研究集中在针对逆问题的正则化理论框架中对跨功率谱的最佳确定，这提供了迹象，这是令人惊讶的是，正则化过程导致神经活动的最佳估计不会导致相应功能连接的最佳估计。沿着这些线条，本文利用合成时间序列模拟了MEG设备记录的神经活动，以表明跨功率频谱的正则化与测量的信噪比显着相关，因此，该正则化相应地取决于神经活动的光谱复杂性。
The study of functional connectivity from magnetoecenphalographic (MEG) data consists in quantifying the statistical dependencies among time series describing the activity of different neural sources from the magnetic field recorded outside the scalp. This problem can be addressed by utilizing connectivity measures whose computation in the frequency domain often relies on the evaluation of the cross-power spectrum of the neural time-series estimated by solving the MEG inverse problem. Recent studies have focused on the optimal determination of the cross-power spectrum in the framework of regularization theory for ill-posed inverse problems, providing indications that, rather surprisingly, the regularization process that leads to the optimal estimate of the neural activity does not lead to the optimal estimate of the corresponding functional connectivity. Along these lines, the present paper utilizes synthetic time series simulating the neural activity recorded by an MEG device to show that the regularization of the cross-power spectrum is significantly correlated with the signal-to-noise ratio of the measurements and that, as a consequence, this regularization correspondingly depends on the spectral complexity of the neural activity.
