Evidence map›Paper›PMID 41764120›Full record

ArticleAnnals of biomedical engineering2026

Network Reconfiguration Underlies Compensatory Muscle Control Across Force Gradients: Parallel Functional Network Evidence from EEG and sEMG.

Xiaoguang Liu, Pengyuan Lin, Yutong Wang, Tie Liang, Xiaodong Wang, Jun Li, Peng Xiong, Xiuling Liu

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Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Xiaoguang LiuCollege of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei, China. lxg_hbu@163.com.ORCID http://orcid.org/0000-0002-0026-3092
Pengyuan Lin *College of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei, China.
Yutong Wang *College of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei, China.
Tie Liang *College of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei, China.
Xiaodong Wang *College of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei, China.
Jun Li *College of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei, China.
Peng Xiong *College of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei, China.
Xiuling Liu *College of Electronic and Information Engineering, Hebei University, Baoding, 071000, Hebei, China.

Funding

Key Program of Hebei Provincial Natural Science Foundation F2022201037Multi-disciplinary Interdisciplinary Research Fund of Hebei University DXK202312National Natural Science Foundation of China No. 62276087Original Exploration Program of the National Natural Science Foundation of China 62450100Science Research Project of Hebei Education Department CXY2024006S&T Program of Hebei 252G7601D
6 · The paper itself

Abstract

purposeUnderstanding how the neuromuscular system adapts to increasing force demands is essential for characterizing compensatory motor control. This study investigated force-dependent reconfiguration of muscle and cortical functional networks during isometric upper-limb tasks.

methodsTwelve healthy participants performed isometric elbow flexion at 30%, 50%, and 70% of maximal voluntary contraction (MVC). Surface electromyography (sEMG) from eight upper-limb muscles and electroencephalography (EEG) from 21 scalp electrodes were recorded concurrently. Directed functional connectivity was estimated using generalized partial directed coherence (GPDC), and graph-theoretical metrics-average global efficiency (AGE), average clustering coefficient (ACC), and average path length (APL)-were computed separately for muscle and cortical networks.

resultsIn the muscle network, a significant main effect of force level was observed. Compared with 30% MVC, AGE increased by 12.24% (

conclusionThese results demonstrate systematic, force-dependent reconfiguration of both muscle and cortical functional networks during isometric force production. Rather than indicating improved performance or neural plasticity, the observed network changes suggest shifts in coordination strategies as force demands increase. The present framework provides quantitative network metrics that can be extended to clinical and longitudinal studies in future work.

Indexed as

ElectroencephalographyElectromyographyIsometric ContractionMuscle, SkeletalNerve NetAdultFemaleHumansMaleYoung AdultBrain function networksMuscle compensationMuscle functional networksNeural oscillatory mechanismsStrength training

Identifiers

PMID41764120

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.