Brookings Ted, Ortigue Stephanie, Grafton Scott, Carlson Jean
Department of Biology, Brandeis University, Waltham, MA 02454, USA.
Neuroimage. 2009 Jan 15;44(2):411-20. doi: 10.1016/j.neuroimage.2008.08.043. Epub 2008 Sep 23.
We develop two techniques to solve for the spatio-temporal neural activity patterns using Electroencephalogram (EEG) and Functional Magnetic Resonance Imaging (fMRI) data. EEG-only source localization is an inherently underconstrained problem, whereas fMRI by itself suffers from poor temporal resolution. Combining the two modalities transforms source localization into an overconstrained problem, and produces a solution with the high temporal resolution of EEG and the high spatial resolution of fMRI. Our first method uses fMRI to regularize the EEG solution, while our second method uses Independent Components Analysis (ICA) and realistic models of Blood Oxygen-Level Dependent (BOLD) signal to relate the EEG and fMRI data. The second method allows us to treat the fMRI and EEG data on equal footing by fitting simultaneously a solution to both data types. Both techniques avoid the need for ad hoc assumptions about the distribution of neural activity, although ultimately the second method provides more accurate inverse solutions.
我们开发了两种技术，用于利用脑电图（EEG）和功能磁共振成像（fMRI）数据求解时空神经活动模式。仅使用EEG的源定位本质上是一个约束不足的问题，而fMRI本身存在时间分辨率差的问题。将这两种模态结合起来可将源定位转化为一个约束过度的问题，并产生一个具有EEG高时间分辨率和fMRI高空间分辨率的解决方案。我们的第一种方法使用fMRI来正则化EEG解决方案，而我们的第二种方法使用独立成分分析（ICA）和血氧水平依赖（BOLD）信号的现实模型来关联EEG和fMRI数据。第二种方法使我们能够通过同时拟合两种数据类型的解决方案，在平等的基础上处理fMRI和EEG数据。两种技术都避免了对神经活动分布进行临时假设的需要，尽管最终第二种方法提供了更准确的逆解。
