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

IF 8.3 1区 医学 Q1 MEDICINE, GENERAL & INTERNAL BMC Medicine Pub Date : 2025-02-06 DOI:10.1186/s12916-025-03846-0
Emilia Fló, Daniel Fraiman, Jacobo Diego Sitt
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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.

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在运动想象过程中评估脑肌肉网络以检测隐蔽的命令执行。
背景:在这项研究中,我们评估了肌电和脑电图记录的网络方法在健康参与者中检测隐蔽命令遵循的潜力。这项研究的动机是开发一种诊断工具,该工具可以应用于普通临床环境,以检测无法传达明确运动或语言反应的患者的意识,例如患有意识障碍(DoC)的患者。方法:在简单运动任务的运动和想象运动以及静息状态下,观察脑和肌肉的反应。对所有通道对的相干谱进行非负矩阵分解(NMF),得到脑肌网络。对于表现出运动意象的15/38名参与者,通过共同空间滤波和线性判别分析,我们在组水平上对比了想象运动和静息状态下网络的配置,并使用NMF的权重作为特征,结合试验方向的功率调制和心脏反应,实现了受试者水平的分类器,以区分静息状态和运动意象。结果:与静息状态相比,动觉运动意象产生的mu- β波段减少,并且发现mu- β功率与运动意象问卷修订第二版的动觉意象得分之间存在较小的相关性。全特征分类器成功地区分了所有参与者的运动意象和静息状态,而脑肌肉功能网络对整体分类没有贡献。然而,心脏活动和皮质能量对于检测参与者何时在心理上排练一个动作至关重要。结论:我们的工作强调了结合脑电图和外周测量来检测命令遵循的重要性,这对于提高对无反应患者与意志一致的隐蔽反应的检测很重要。
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来源期刊
BMC Medicine
BMC Medicine 医学-医学:内科
CiteScore
13.10
自引率
1.10%
发文量
435
审稿时长
4-8 weeks
期刊介绍: BMC Medicine is an open access, transparent peer-reviewed general medical journal. It is the flagship journal of the BMC series and publishes outstanding and influential research in various areas including clinical practice, translational medicine, medical and health advances, public health, global health, policy, and general topics of interest to the biomedical and sociomedical professional communities. In addition to research articles, the journal also publishes stimulating debates, reviews, unique forum articles, and concise tutorials. All articles published in BMC Medicine are included in various databases such as Biological Abstracts, BIOSIS, CAS, Citebase, Current contents, DOAJ, Embase, MEDLINE, PubMed, Science Citation Index Expanded, OAIster, SCImago, Scopus, SOCOLAR, and Zetoc.
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