Dukic Stefan, Govaarts Rosanne, Hillebrand Arjan, de Visser Marianne, Seeck Margitta, McMackin Roisin
Department of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, the Netherlands.
Academic Unit of Neurology, School of Medicine, Trinity College, The University of Dublin, Dublin, Ireland.
Clin Neurophysiol Pract. 2025 Jul 11;10:301-315. doi: 10.1016/j.cnp.2025.07.001. eCollection 2025.
Motor neurone diseases (MNDs) are increasingly being acknowledged as network disorders, with cortical dysfunction and degeneration extending beyond the motor cortex. Measures of this broader cortical pathophysiology are providing promising candidates in the search for diagnostic and prognostic biomarkers of the MNDs. Electroencephalography (EEG) and magnetoencephalography (MEG) offer a direct view of neural network activity by detecting changes in electromagnetic fields of the brain. Measurements based on EEG/MEG have often been overlooked in the search for MND biomarkers, largely due to their limited spatial resolution and the perceived challenges associated with noise in these signals. However, with recent developments in sensor technology and source reconstruction algorithms, alongside substantial improvement in pipelines that address noise, EEG/MEG-based measures can now be readily employed for spatiotemporally-precise, economical and non-invasive characterisation of cortical network pathophysiology in MNDs. Here, we provide an overview of how EEG/MEG signals have been employed to quantify neural network function in MND. We outline the advantages and limitations of these measurements, discuss the most clinically promising EEG/MEG studies of MNDs to date, and highlight future directions warranted to harness the full potential of these technologies for understanding and assessing MNDs.
运动神经元病(MNDs)越来越被认为是网络障碍性疾病,其皮质功能障碍和退化超出了运动皮质的范围。对这种更广泛的皮质病理生理学的测量为寻找MNDs的诊断和预后生物标志物提供了有前景的候选指标。脑电图(EEG)和脑磁图(MEG)通过检测大脑电磁场的变化,直接观察神经网络活动。在寻找MND生物标志物的过程中,基于EEG/MEG的测量常常被忽视,这主要是由于其空间分辨率有限以及这些信号中与噪声相关的明显挑战。然而,随着传感器技术和源重建算法的最新发展,以及处理噪声的流程有了显著改进,基于EEG/MEG的测量现在可以很容易地用于对MNDs皮质网络病理生理学进行时空精确、经济且无创的特征描述。在此,我们概述了EEG/MEG信号如何被用于量化MND中的神经网络功能。我们概述了这些测量的优点和局限性,讨论了迄今为止最具临床前景的MNDs的EEG/MEG研究,并强调了为充分利用这些技术来理解和评估MNDs所需的未来方向。