Owen Julia P, Wipf David P, Attias Hagai T, Sekihara Kensuke, Nagarajan Srikantan S
Biomagnetic Imaging Laboratory, Dept. Radiology and Biomedical Imaging, UCSF San Francisco, CA, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:65-8. doi: 10.1109/IEMBS.2009.5335005.
The synchronous brain activity measured via magnetoencephalography (MEG) arises from current dipoles located throughout the cortex. Estimating the number, location, time-course, and orientation of these dipoles, called sources, remains a challenging task, one that is significantly compounded by the effects of source correlations and interference from spontaneous brain activity and sensor noise. Likewise, assessing the interactions between the individual sources, known as functional connectivity, is also confounded by noise and correlations in the sensor recordings. Computational complexity has been an obstacle to computing functional connectivity. This paper demonstrates the application of an empirical Bayesian method to perform source localization with MEG data in order to estimate measures of functional connectivity. We demonstrate that brain source activity inferred from this algorithm is better suited to uncover the interactions between brain areas as compared to other commonly used source localization algorithms.
通过脑磁图（MEG）测量的同步脑活动源自遍布整个皮层的电流偶极子。估计这些偶极子（即源）的数量、位置、时间进程和方向仍然是一项具有挑战性的任务，而源相关性以及自发脑活动和传感器噪声的干扰会使这一任务变得更加复杂。同样，评估各个源之间的相互作用（即功能连接性）也会因传感器记录中的噪声和相关性而变得复杂。计算复杂性一直是计算功能连接性的障碍。本文展示了一种经验贝叶斯方法在利用MEG数据进行源定位以估计功能连接性指标方面的应用。我们证明，与其他常用的源定位算法相比，从该算法推断出的脑源活动更适合揭示脑区之间的相互作用。
