Barnes Gareth R, Furlong Paul L, Singh Krish D, Hillebrand Arjan
The Wellcome Trust Laboratory for MEG studies, Neurosciences Research Institute, School of Life and Health Sciences, Aston University, Birmingham B4 7ET, UK.
Neuroimage. 2006 Jun;31(2):623-6. doi: 10.1016/j.neuroimage.2005.12.036. Epub 2006 Feb 9.
Magnetoencephalography (MEG) is a non-invasive brain imaging technique with the potential for very high temporal and spatial resolution of neuronal activity. The main stumbling block for the technique has been that the estimation of a neuronal current distribution, based on sensor data outside the head, is an inverse problem with an infinity of possible solutions. Many inversion techniques exist, all using different a-priori assumptions in order to reduce the number of possible solutions. Although all techniques can be thoroughly tested in simulation, implicit in the simulations are the experimenter's own assumptions about realistic brain function. To date, the only way to test the validity of inversions based on real MEG data has been through direct surgical validation, or through comparison with invasive primate data. In this work, we constructed a null hypothesis that the reconstruction of neuronal activity contains no information on the distribution of the cortical grey matter. To test this, we repeatedly compared rotated sections of grey matter with a beamformer estimate of neuronal activity to generate a distribution of mutual information values. The significance of the comparison between the un-rotated anatomical information and the electrical estimate was subsequently assessed against this distribution. We found that there was significant (P < 0.05) anatomical information contained in the beamformer images across a number of frequency bands. Based on the limited data presented here, we can say that the assumptions behind the beamformer algorithm are not unreasonable for the visual-motor task investigated.
脑磁图（MEG）是一种非侵入性脑成像技术，具有对神经元活动进行非常高的时间和空间分辨率成像的潜力。该技术的主要障碍在于，基于头部外部的传感器数据来估计神经元电流分布是一个具有无穷多个可能解的逆问题。存在许多反演技术，它们都使用不同的先验假设来减少可能解的数量。尽管所有技术都可以在模拟中进行全面测试，但模拟中隐含着实验者自己对现实脑功能的假设。迄今为止，基于真实MEG数据测试反演有效性的唯一方法是通过直接手术验证，或与侵入性灵长类动物数据进行比较。在这项工作中，我们构建了一个零假设，即神经元活动的重建不包含关于皮质灰质分布的信息。为了验证这一点，我们反复将灰质的旋转切片与神经元活动的波束形成器估计值进行比较，以生成互信息值的分布。随后，根据这个分布评估未旋转的解剖信息与电估计值之间比较的显著性。我们发现在多个频带的波束形成器图像中包含显著的（P < 0.05）解剖信息。基于这里给出的有限数据，我们可以说，对于所研究的视觉运动任务，波束形成器算法背后的假设并非不合理。
