Rana Kunjan D, Hämäläinen Matti S, Vaina Lucia M
Brain and Vision Research Laboratory, Department of Biomedical Engineering, Boston University, Boston, MA, United States.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.
Front Comput Neurosci. 2018 Jun 12;12:35. doi: 10.3389/fncom.2018.00035. eCollection 2018.
Magnetoencephalography (MEG) captures the magnetic fields generated by neuronal current sources with sensors outside the head. In MEG analysis these current sources are estimated from the measured data to identify the locations and time courses of neural activity. Since there is no unique solution to this so-called inverse problem, multiple source estimation techniques have been developed. The nulling beamformer (NB), a modified form of the linearly constrained minimum variance (LCMV) beamformer, is specifically used in the process of inferring interregional interactions and is designed to eliminate shared signal contributions, or cross-talk, between regions of interest (ROIs) that would otherwise interfere with the connectivity analyses. The nulling beamformer applies the truncated singular value decomposition (TSVD) to remove small signal contributions from a ROI to the sensor signals. However, ROIs with strong crosstalk will have high separating power in the weaker components, which may be removed by the TSVD operation. To address this issue we propose a new method, the (NBSS). This method, controlled by a tuning parameter, reweights the singular values of the gain matrix mapping from source to sensor space such that components with high overlap are reduced. By doing so, we are able to measure signals between nearby source locations with limited cross-talk interference, allowing for reliable cortical connectivity analysis between them. In two simulations, we demonstrated that NBSS reduces cross-talk while retaining ROIs' signal power, and has higher separating power than both the minimum norm estimate (MNE) and the nulling beamformer without subspace suppression. We also showed that NBSS successfully localized the auditory M100 event-related field in primary auditory cortex, measured from a subject undergoing an auditory localizer task, and suppressed cross-talk in a nearby region in the superior temporal sulcus.
脑磁图（MEG）通过头部外部的传感器捕捉神经元电流源产生的磁场。在MEG分析中，这些电流源是根据测量数据估算出来的，以确定神经活动的位置和时间进程。由于这个所谓的逆问题没有唯一解，因此已经开发了多种源估计技术。归零波束形成器（NB）是线性约束最小方差（LCMV）波束形成器的一种改进形式，专门用于推断区域间相互作用的过程，旨在消除感兴趣区域（ROI）之间的共享信号贡献或串扰，否则这些串扰会干扰连通性分析。归零波束形成器应用截断奇异值分解（TSVD）来去除ROI对传感器信号的小信号贡献。然而，具有强串扰的ROI在较弱分量中会有较高的分离能力，这些较弱分量可能会被TSVD操作去除。为了解决这个问题，我们提出了一种新方法，即归一化归零波束形成器（NBSS）。该方法由一个调谐参数控制，对从源空间到传感器空间的增益矩阵的奇异值进行重新加权，从而减少具有高重叠的分量。通过这样做，我们能够在有限的串扰干扰下测量附近源位置之间的信号，从而实现它们之间可靠的皮质连通性分析。在两个模拟中，我们证明了NBSS在保留ROI信号功率的同时减少了串扰，并且比最小范数估计（MNE）和没有子空间抑制的归零波束形成器具有更高的分离能力。我们还表明，NBSS成功地定位了一名接受听觉定位任务的受试者初级听觉皮层中的听觉M100事件相关场，并抑制了颞上沟附近区域的串扰。
