随机多分辨率扫描在变深度脑活动的局灶和快速E/MEG感知中的应用
Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth
摘要：
我们专注于脑磁电成像的神经活动，特别是，通过层次贝叶斯模型（HBM）找到一个强大的估计的主要电流分布。我们的目标是开发一个合理的快速最大后验（MAP）估计技术，这将是适用于表面和深层地区没有具体的先验知识的数量或位置的活动。为了使任何深度的源可扩展性，我们引入了一种随机多分辨率扫描（RAMUS）的方法，其中的MAP估计的大脑活动是不同的重建过程中。RAMUS旨在为整个大脑提供强大而准确的成像结果，同时将计算成本保持在适当的水平。逆伽马（IG）分布被应用作为主要的超先验，以实现最佳的性能为大脑的深部。在这个概念验证研究中，我们考虑通过模拟14-20 ms刺激后体感诱发电位和电手腕刺激的场响应的数值模拟数据来检测同时的丘脑和体感活动。球模型和真实模型都被用来分析源重建的差异。在数值研究的情况下，RAMUS被观察到，以提高深组件的可见性，也边缘化的离散化和优化的随机效应，而没有显着的计算成本。一个强大的和准确的MAP估计的初级电流密度在大脑的浅表和深部。
英文摘要：
We focus on electromagnetoencephalography imaging of the neural activity and, in particular, finding a robust estimate for the primary current distribution via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably fast maximum a posteriori (MAP) estimation technique which would be applicable for both superficial and deep areas without specific a priori knowledge of the number or location of the activity. To enable source distinguishability for any depth, we introduce a randomized multiresolution scanning (RAMUS) approach in which the MAP estimate of the brain activity is varied during the reconstruction process. RAMUS aims to provide a robust and accurate imaging outcome for the whole brain, while maintaining the computational cost on an appropriate level. The inverse gamma (IG) distribution is applied as the primary hyperprior in order to achieve an optimal performance for the deep part of the brain. In this proof-of-the-concept study, we consider the detection of simultaneous thalamic and somatosensory activity via numerically simulated data modeling the 14-20 ms post-stimulus somatosensory evoked potential and field response to electrical wrist stimulation. Both a spherical and realistic model are utilized to analyze the source reconstruction discrepancies. In the numerically examined case, RAMUS was observed to enhance the visibility of deep components and also marginalizing the random effects of the discretization and optimization without a remarkable computation cost. A robust and accurate MAP estimate for the primary current density was obtained in both superficial and deep parts of the brain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
