Ding Lei
University of Oklahoma, Norman, Oklahoma, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1922-5. doi: 10.1109/IEMBS.2009.5333925.
We have previously reported a new sparse neuroimaging method (i.e. VB-SCCD) using the L1-norm optimization technology to solve EEG inverse problems. The new method distinguishes itself from other reported L1-norm methods since it explores the sparseness in a transform domain rather than in the original source domain. In the present study, we conducted a Monte Carlo simulation study to compare the performance of VB-SCCD and other two popular L2-norm neuroimaging methods (i.e. wMNE and cLORETA) in reconstructing extended cortical neural electrical activations. Our simulation data suggests that the VB-SCCD method is able to reconstruct extended cortical sources with the overall high accuracy. It has significantly higher accuracy, less number of false alarms and less number of missing sources when studying complex brain activations (up to 5 simultaneous sources). This new sparse neuroimaging method is thus promising to have many valuable applications in neuroscience and neurology problems. It is also applicable to MEG neuroimaging.
我们之前报道了一种新的稀疏神经成像方法（即VB-SCCD），该方法使用L1范数优化技术来解决脑电图逆问题。这种新方法与其他已报道的L1范数方法不同，因为它是在变换域而非原始源域中探索稀疏性。在本研究中，我们进行了一项蒙特卡罗模拟研究，以比较VB-SCCD与其他两种流行的L2范数神经成像方法（即wMNE和cLORETA）在重建扩展皮质神经电激活方面的性能。我们的模拟数据表明，VB-SCCD方法能够以总体较高的精度重建扩展皮质源。在研究复杂脑激活（多达5个同时激活源）时，它具有显著更高的精度、更少的误报数量和更少的缺失源数量。因此，这种新的稀疏神经成像方法有望在神经科学和神经病学问题中具有许多有价值的应用。它也适用于脑磁图神经成像。
