Phillips J W, Leahy R M, Mosher J C
Signal and Image Processing Institute, University of Southern California, Los Angeles 90089, USA.
IEEE Trans Med Imaging. 1997 Jun;16(3):338-48. doi: 10.1109/42.585768.
We describe a new approach to imaging neural current sources from measurements of the magnetoencephalogram (MEG) associated with sensory, motor, or cognitive brain activation. Many previous approaches to this problem have concentrated on the use of weighted minimum norm (WMN) inverse methods. While these methods ensure a unique solution, they do not introduce information specific to the MEG inverse problem, often producing overly smoothed solutions and exhibiting severe sensitivity to noise. We describe a Bayesian formulation of the inverse problem in which a Gibbs prior is constructed to reflect the sparse focal nature of neural current sources associated with evoked response data. We demonstrate the method with simulated and experimental phantom data, comparing its performance with several WMN methods.
我们描述了一种新的方法，用于从与感觉、运动或认知脑激活相关的脑磁图（MEG）测量中对神经电流源进行成像。以前针对这个问题的许多方法都集中在使用加权最小范数（WMN）逆方法上。虽然这些方法确保了唯一解，但它们没有引入特定于MEG逆问题的信息，常常产生过度平滑的解，并且对噪声表现出严重的敏感性。我们描述了逆问题的贝叶斯公式，其中构建了一个吉布斯先验来反映与诱发反应数据相关的神经电流源的稀疏焦点性质。我们用模拟和实验虚拟数据演示了该方法，并将其性能与几种WMN方法进行了比较。
