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静息态脑电图地形图:单侧空间忽视的可靠且敏感特征

Resting-state EEG topographies: Reliable and sensitive signatures of unilateral spatial neglect.

作者信息

Pirondini Elvira, Goldshuv-Ezra Nurit, Zinger Nofya, Britz Juliane, Soroker Nachum, Deouell Leon Y, Ville Dimitri Van De

机构信息

Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.

Department of Neurological Rehabilitation, Loewenstein Rehabilitation Hospital, Raanana, Israel; Evoked Potentials Laboratory, Technion - Israel Institute of Technology, Haifa, Israel.

出版信息

Neuroimage Clin. 2020;26:102237. doi: 10.1016/j.nicl.2020.102237. Epub 2020 Mar 5.

Abstract

Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on the availability of reliable, simple, and sensitive biomarkers of impairment level and extent of recovery, to enable an informed clinical-decision process. However, the neuropsychological tests currently in use do not tap into the complex neural re-organization process that occurs after brain insult and its modulation by treatment. Here we show that topographical analysis of resting-state electroencephalography (rsEEG) patterns using singular value decomposition (SVD) could be used to capture these processes. In two groups of subacute stroke patients, we show reliable detection of deviant neurophysiological patterns over repeated measurement sessions on separate days. These patterns generalized across patients groups. Additionally, they maintained a significant association with ipsilesional attention bias, discriminating patients with spatial neglect of different severity levels. The sensitivity and reliability of these rsEEG topographical analyses support their use as a tool for monitoring natural and treatment-induced recovery in the rehabilitation process.

摘要

神经科学的理论进展促使越来越多旨在促进脑损伤后功能恢复的干预措施得以开发。将这些新方法整合到临床实践中,依赖于可靠、简单且灵敏的生物标志物,以反映损伤程度和恢复程度,从而实现明智的临床决策过程。然而,目前使用的神经心理学测试无法触及脑损伤后发生的复杂神经重组过程及其受治疗的调节情况。在此,我们表明,使用奇异值分解(SVD)对静息态脑电图(rsEEG)模式进行地形分析可用于捕捉这些过程。在两组亚急性中风患者中,我们展示了在不同日期的重复测量过程中对异常神经生理模式的可靠检测。这些模式在患者组间具有普遍性。此外,它们与患侧注意力偏差保持着显著关联,能够区分不同严重程度空间忽视的患者。这些rsEEG地形分析的敏感性和可靠性支持将其用作监测康复过程中自然恢复和治疗诱导恢复的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/824b/7083886/c5b45139b8be/gr1.jpg

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