Abbasi Omid, Hirschmann Jan, Schmitz Georg, Schnitzler Alfons, Butz Markus
Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Germany; Department of Medical Engineering, Ruhr-Universität Bochum, Germany.
Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University Düsseldorf, Germany.
J Neurosci Methods. 2016 Aug 1;268:131-41. doi: 10.1016/j.jneumeth.2016.04.010. Epub 2016 May 17.
Recording brain activity during deep brain stimulation (DBS) using magnetoencephalography (MEG) can potentially help clarifying the neurophysiological mechanism of DBS. The DBS artefact, however, distorts MEG data significantly. We present an artefact rejection approach to remove the DBS artefact from MEG data.
We developed an approach consisting of four consecutive steps: (i) independent component analysis was used to decompose MEG data to independent components (ICs); (ii) mutual information (MI) between stimulation signal and all ICs was calculated; (iii) artefactual ICs were identified by means of an MI threshold; and (iv) the MEG signal was reconstructed using only non-artefactual ICs. This approach was applied to MEG data from five Parkinson's disease patients with implanted DBS stimulators. MEG was recorded with DBS ON (unilateral stimulation of the subthalamic nucleus) and DBS OFF during two experimental conditions: a visual attention task and alternating right and left median nerve stimulation.
With the presented approach most of the artefact could be removed. The signal of interest could be retrieved in both conditions.
In contrast to existing artefact rejection methods for MEG-DBS data (tSSS and S(3)P), the proposed method uses the actual artefact source, i.e. the stimulation signal, as reference signal.
Using the presented method, the DBS artefact can be significantly rejected and the physiological data can be restored. This will facilitate research addressing the impact of DBS on brain activity during rest and various tasks.
使用脑磁图（MEG）记录深部脑刺激（DBS）期间的脑活动可能有助于阐明DBS的神经生理机制。然而，DBS伪迹会严重扭曲MEG数据。我们提出一种伪迹去除方法，以从MEG数据中去除DBS伪迹。
我们开发了一种由四个连续步骤组成的方法：（i）使用独立成分分析将MEG数据分解为独立成分（ICs）；（ii）计算刺激信号与所有ICs之间的互信息（MI）；（iii）通过MI阈值识别伪迹ICs；（iv）仅使用非伪迹ICs重建MEG信号。该方法应用于五名植入DBS刺激器的帕金森病患者的MEG数据。在两种实验条件下记录了DBS开启（单侧刺激丘脑底核）和DBS关闭时的MEG：视觉注意力任务以及左右正中神经交替刺激。
使用所提出的方法，大部分伪迹可以被去除。在两种条件下都可以检索到感兴趣的信号。
与现有的用于MEG-DBS数据的伪迹去除方法（tSSS和S(3)P）相比，所提出的方法使用实际的伪迹源，即刺激信号，作为参考信号。
使用所提出的方法，可以显著去除DBS伪迹并恢复生理数据。这将有助于研究DBS对休息和各种任务期间脑活动的影响。
