The conventional beamformers that reconstruct the cerebral origin of brain activity measured outside the head via electro- and magneto-encephalography (EEG/MEG) suffer from depth bias and smearing of nearby sources. Here, to meet these methodological challenges, we propose a depth-invariant and forward beamformer for magneto-encephalography (MEG) data. Based on the new proposal, we further develop a two-step approach for inferring functional connectivity in the brain. The proposed methodology is invariant with respect to source depths in the brain. It nulls smearing of nearby sources and allows for time-varying source orientations. We illustrate the new approach with MEG data derived from a face-perception experiment, revealing patterns of functional connectivity for face perception. We identify a set of brain regions where their responses and connectivity are significantly varying when stimuli alter between faces and scrambled faces. By simulation studies, we show that the proposed forward beamformer can outperform the forward methods based on conventional beamformers in terms of localization bias.

中文翻译：

---

用于与 MEG 数据的功能连接的深度不变波束成形

传统的波束形成器通过脑电图和脑磁图 (EEG/MEG) 在头部外测量的重建大脑活动的脑源，受到深度偏差和附近源的拖尾的影响。在这里，为了应对这些方法上的挑战，我们提出了一种用于脑磁图 (MEG) 数据的深度不变和前向波束形成器。基于新的提议，我们进一步开发了一种两步法来推断大脑中的功能连接。所提出的方法对于大脑中的源深度是不变的。它消除了附近源的拖尾，并允许随时间变化的源方向。我们使用源自面部感知实验的 MEG 数据来说明新方法，揭示面部感知的功能连接模式。我们确定了一组大脑区域，当刺激在面部和打乱的面部之间发生变化时，它们的反应和连通性会发生显着变化。通过仿真研究，我们表明所提出的前向波束形成器在定位偏差方面可以优于基于传统波束形成器的前向方法。