MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Hum Brain Mapp. 2024 Feb 1;45(2):e26602. doi: 10.1002/hbm.26602.
Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large-scale cortical networks. In this technical report, we develop a novel denoising approach for suppressing the leakage of interference source activity into the activity representing a brain region of interest. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We apply this denoising approach to reconstruct simulated evoked response topographies to deep brain stimulation (DBS) in a phantom recording. We highlight the advantages of our approach compared to the benchmark-spatiotemporal signal space separation-and show that it can more accurately reveal brain stimulation-evoked response topographies. Finally, we apply our method to MEG recordings from a single patient with Parkinson's disease, to reveal early cortical-evoked responses to DBS of the subthalamic nucleus.
脑磁图（MEG）记录经常受到干扰的污染，这些干扰的幅度可能超过生理脑活动的几个数量级。此外，干扰源的活动可能会在空间上扩展（称为源泄漏）到感兴趣的脑信号活动中，从而导致源估计不准确。当使用 MEG 研究脑刺激对大规模皮质网络的影响时，这个问题尤为明显。在本技术报告中，我们开发了一种新的去噪方法，用于抑制干扰源活动泄漏到代表感兴趣脑区域的活动中。该方法利用了来自预定解剖学感兴趣区域的信号的空间和时域投影器。我们将这种去噪方法应用于模拟深部脑刺激（DBS）在幻影记录中的诱发反应地形图的重建。我们强调了与基准-时空信号空间分离相比，我们的方法的优势，并表明它可以更准确地揭示脑刺激诱发的反应地形图。最后，我们将我们的方法应用于单个帕金森病患者的 MEG 记录，以揭示丘脑底核 DBS 对早期皮层诱发反应。
