Astolfi Laura, Cincotti Febo, Mattia Donatella, Marciani M Grazia, Baccala Luiz A, de Vico Fallani Fabrizio, Salinari Serenella, Ursino Mauro, Zavaglia Melissa, Ding Lei, Edgar J Christopher, Miller Gregory A, He Bin, Babiloni Fabio
Dipartimento Informatica e Sistemistica, Universita La Sapienza, Rome, Italy.
Hum Brain Mapp. 2007 Feb;28(2):143-57. doi: 10.1002/hbm.20263.
The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high-resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal-to-noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high-resolution EEG data recorded during the well-known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high-resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF.
这项工作的目的是定量表征一组频域技术在从实际中常见的不同手术条件下的高分辨率脑电图记录估计皮质连接性方面的性能。所研究的连接性模式估计器包括定向传递函数（DTF）、其被称为直接DTF（dDTF）的变体以及部分定向相干性（PDC）。模拟了预定义的皮质连接性模式，然后通过应用DTF、dDTF和PDC方法进行恢复。研究了脑电图时段的信噪比（SNR）和长度（LENGTH）作为影响所施加连接性模式重建的因素。通过一些重建连接性模式的质量指标来评估估计连接性模式的重建质量和错误率。使用方差分析（ANOVA）对误差函数进行统计分析。然后将整个方法应用于在著名的斯特鲁普范式期间记录的高分辨率脑电图数据。模拟表明，在合理条件下，所有三种方法都能正确估计模拟的连接性模式。然而，这些方法的性能随SNR和LENGTH因素而有所不同。当应用于斯特鲁普数据时，这些方法通常是等效的。一般来说，可用脑电图的量会影响连接性模式估计的准确性。对27秒不连续记录且SNR为3或更高的分析确保了对于PDC，连接性模式能够以低于7%的误差准确恢复，对于DTF则为5%。总之，通过结合高分辨率脑电图技术、皮质活动的线性逆估计以及诸如PDC、DTF和dDTF等频域多变量方法，可以在大多数脑电图记录所满足的一般条件下有效地估计皮质活动的功能连接性模式。
