Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School/Massachusetts Institute of Technology , Charlestown, MA , USA ; McGovern Institute, Massachusetts Institute of Technology , Cambridge, MA , USA ; Department of Neurology, Massachusetts General Hospital, Harvard Medical School , Boston, MA , USA.
Aix Marseille Université, CNRS, ENSAM, Université de Toulon, LSIS UMR 7296 , Marseille , France.
Front Hum Neurosci. 2014 May 23;8:338. doi: 10.3389/fnhum.2014.00338. eCollection 2014.
Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz-Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical MEG/EEG source imaging data.
脑磁图（MEG）和脑电图（EEG）的分布式皮质解译表现出复杂的时空动力学。从这些皮质信号中提取感兴趣的模式和动态特征，迄今为止一直依赖于研究人员的专业知识。在临床和神经科学研究中，都明确需要一种能够自动从高维神经影像学数据中提取关键特征的方法。我们之前已经证明了使用光流技术来评估投影到非平坦流形（如皮质表面）上的运动场的运动学特性。我们进一步扩展了这个框架，通过使用修改和扩展的 2-黎曼流形亥姆霍兹-霍奇分解（HHD）自动检测光流矢量场中的特征。在这里，我们将这些数学模型应用于模拟和 MEG 数据，这些数据是从一名健康个体在体感实验期间以及一名癫痫儿科患者在睡眠期间记录的。我们测试了我们的技术是否可以自动提取皮质活动的显著动态特征。模拟结果表明，我们可以使用 HHD 精确地再现模拟的皮质动力学；将其编码为稀疏特征，并表示不同皮质区域之间的大脑活动的传播。使用 HHD，我们将体感 N20 成分解码为两个 HHD 特征，并将大脑活动的动力学表示为两个主要体感区域之间的移动源。在癫痫患者中，我们显示了脑损伤边缘周围的癫痫活动的传播。我们的研究结果表明，从皮质动力学计算得出的 HHD 测量值可以：（i）以稀疏特征和不同皮质区域之间的动态大脑活动传播的形式，定量地评估健康和患病大脑的皮质动力学；（ii）促进对实验和临床 MEG/EEG 源成像数据的可重复、自动化分析。
