Department of Mathematics, University of Genoa, Genoa, Italy.
Department of Neurophysiology and Diagnostic Epileptology, IRCCS Foundation Carlo Besta Neurological Institute, Milan, Italy.
J Neurosci Methods. 2019 Jan 15;312:27-36. doi: 10.1016/j.jneumeth.2018.11.007. Epub 2018 Nov 17.
Magneto- and Electro-encephalography record the electromagnetic field generated by neural currents with high temporal frequency and good spatial resolution, and are therefore well suited for source localization in the time and in the frequency domain. In particular, localization of the generators of neural oscillations is very important in the study of cognitive processes in the healthy and in the pathological brain.
We introduce the use of a Bayesian multi-dipole localization method in the frequency domain. Given the Fourier Transform of the data at one or multiple frequencies and/or trials, the algorithm approximates numerically the posterior distribution with Monte Carlo techniques.
We use synthetic data to show that the proposed method behaves well under a wide range of experimental conditions, including low signal-to-noise ratios and correlated sources. We use dipole clusters to mimic the effect of extended sources. In addition, we test the algorithm on real MEG data to confirm its feasibility.
COMPARISON WITH EXISTING METHOD(S): Throughout the whole study, DICS (Dynamic Imaging of Coherent Sources) is used systematically as a benchmark. The two methods provide similar general pictures; the posterior distributions of the Bayesian approach contain much richer information at the price of a higher computational cost.
The Bayesian method described in this paper represents a reliable approach for localization of multiple dipoles in the frequency domain.
脑磁图和脑电图以高时间频率和良好的空间分辨率记录神经电流产生的电磁场，因此非常适合在时域和频域进行源定位。特别是，神经振荡发生器的定位在健康和病理大脑的认知过程研究中非常重要。
我们引入了频域中贝叶斯多极子定位方法的使用。给定一个或多个频率和/或试验的数据的傅立叶变换，该算法使用蒙特卡罗技术数值逼近后验分布。
我们使用合成数据表明，该方法在广泛的实验条件下表现良好，包括低信噪比和相关源。我们使用偶极子簇来模拟扩展源的效果。此外，我们还在真实的 MEG 数据上测试了该算法，以确认其可行性。
在整个研究过程中，DICS（相干源的动态成像）被系统地用作基准。这两种方法提供了相似的总体图像；贝叶斯方法的后验分布以更高的计算成本为代价，包含了更丰富的信息。
本文描述的贝叶斯方法代表了一种在频域中定位多个偶极子的可靠方法。
