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在运动想象过程中评估脑-肌网络以检测隐蔽的指令跟随情况。

Assessing brain-muscle networks during motor imagery to detect covert command-following.

作者信息

Fló Emilia, Fraiman Daniel, Sitt Jacobo Diego

机构信息

Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Paris, France.

Departamento de Matemática y Ciencias, Universidad de San Andrés, Buenos Aires, Argentina.

出版信息

BMC Med. 2025 Feb 6;23(1):68. doi: 10.1186/s12916-025-03846-0.

Abstract

BACKGROUND

In this study, we evaluated the potential of a network approach to electromyography and electroencephalography recordings to detect covert command-following in healthy participants. The motivation underlying this study was the development of a diagnostic tool that can be applied in common clinical settings to detect awareness in patients that are unable to convey explicit motor or verbal responses, such as patients that suffer from disorders of consciousness (DoC).

METHODS

We examined the brain and muscle response during movement and imagined movement of simple motor tasks, as well as during resting state. Brain-muscle networks were obtained using non-negative matrix factorization (NMF) of the coherence spectra for all the channel pairs. For the 15/38 participants who showed motor imagery, as indexed by common spatial filters and linear discriminant analysis, we contrasted the configuration of the networks during imagined movement and resting state at the group level, and subject-level classifiers were implemented using as features the weights of the NMF together with trial-wise power modulations and heart response to classify resting state from motor imagery.

RESULTS

Kinesthetic motor imagery produced decreases in the mu-beta band compared to resting state, and a small correlation was found between mu-beta power and the kinesthetic imagery scores of the Movement Imagery Questionnaire-Revised Second version. The full-feature classifiers successfully distinguished between motor imagery and resting state for all participants, and brain-muscle functional networks did not contribute to the overall classification. Nevertheless, heart activity and cortical power were crucial to detect when a participant was mentally rehearsing a movement.

CONCLUSIONS

Our work highlights the importance of combining EEG and peripheral measurements to detect command-following, which could be important for improving the detection of covert responses consistent with volition in unresponsive patients.

摘要

背景

在本研究中,我们评估了一种基于网络方法的肌电图和脑电图记录技术,用于检测健康参与者的隐蔽指令遵循情况。本研究的动机是开发一种诊断工具,该工具可应用于普通临床环境,以检测无法做出明确运动或言语反应的患者的意识,例如患有意识障碍(DoC)的患者。

方法

我们检查了简单运动任务的运动和想象运动期间以及静息状态下的大脑和肌肉反应。使用所有通道对相干谱的非负矩阵分解(NMF)获得脑-肌网络。对于15/38名表现出运动想象的参与者(通过共同空间滤波器和线性判别分析确定),我们在组水平上对比了想象运动和静息状态期间网络的配置,并使用NMF的权重以及逐次试验的功率调制和心脏反应作为特征,实施个体水平分类器,以将静息状态与运动想象区分开来。

结果

与静息状态相比,动觉运动想象使μ-β频段功率降低,并且在μ-β功率与修订版第二版运动想象问卷的动觉想象得分之间发现了小的相关性。全特征分类器成功区分了所有参与者的运动想象和静息状态,并且脑-肌功能网络对整体分类没有贡献。然而,心脏活动和皮层功率对于检测参与者何时在脑海中演练运动至关重要。

结论

我们的工作强调了结合脑电图和外周测量来检测指令遵循的重要性,这对于改善对无反应患者中与意志一致的隐蔽反应的检测可能很重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f751/11803995/e958ca088b12/12916_2025_3846_Fig2_HTML.jpg

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