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Bio-signal analysis in fatigue and cancer related fatigue: Weakening of corticomuscular functional coupling.

机译:疲劳和癌症相关疲劳中的生物信号分析:皮质神经功能耦合减弱。

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摘要

Fatigue is a common experience that reduces productivity and increases chance of injury, and has been reported as one of most common symptoms with greatest impact on quality-of-life parameters in cancer patients. Neural mechanisms behind fatigue and cancer related fatigue (CRF) are not well known. Recent research has shown dissociation between changes in brain and muscle signals during voluntary muscle fatigue, which may suggest weakening of functional corticomuscular coupling (fCMC). However, this weakening of brain-muscle coupling has never been directly evaluated. More important information could be gained if fCMC is directly detected during fatigue because a voluntary muscle contraction depends on integration of the entire chain of events and is a complex interaction of different components from the central nervous system to peripheral systems. This research, first, evaluated the effect of muscle fatigue on fCMC in healthy people by determining electroencephalography (EEG)-electromyography (EMG) coherence during two stages of a sustained voluntary muscle contraction, one with minimal fatigue and the other with severer fatigue. The obtained results suggest that despite an elevation of the power for both the EEG and EMG activities with muscle fatigue, the fatigue weakens strength of fCMC between the two signals. Secondly, given the fact that there is larger discrepancy between central and peripheral fatigue in CRF, the effect of cancer related fatigue on fCMC was evaluated by comparing EEG-EMG coherence during a muscle fatigue task in CRF patients with healthy controls. CRF patients showed significantly lower fCMC compared to healthy controls during minimal fatigue stage which may be caused by possible pathophysiological impairments in the patients. Finally, to better understand dynamic fatigue effect on fCMC, a single trial coherence estimation based on Morlet wavelet was developed and applied to investigate fatigue effect on fCMC in single trial during repetitive maximal muscle contractions. It was revealed that the decreasing pattern of the fCMC varied among the subjects but the overall decreasing trend was consistent across subjects. The results from the single-trial study suggest it is possible to detect more dynamic fCMC adaptations under acute neuromuscular instability conditions, such as muscle fatigue.;This research reveals that muscle fatigue impairs normal coupling between the central and peripheral neuromuscular systems, which could be a major factor contributing to worsened performance under fatigue influence. In general, cancer patients with fatigue symptom exhibit substantially weakened fCMC, even without influence of muscle fatigue. The findings are potentially important in understanding neural mechanisms of muscle fatigue and cancer related fatigue, and in guiding development of new methodologies to improve diagnosis and treatment of fatigue symptoms in clinical populations.
机译:疲劳是降低生产率并增加受伤机会的常见经验,并且据报道是疲劳的最常见症状之一,对癌症患者的生活质量参数影响最大。疲劳和癌症相关疲劳(CRF)背后的神经机制尚不清楚。最近的研究表明,在自愿性肌肉疲劳期间,大脑和肌肉信号的变化之间存在分离关系,这可能表明功能性皮层-皮质耦合(fCMC)减弱。但是,这种脑-肌肉耦合的减弱从未被直接评估过。如果在疲劳期间直接检测到fCMC,则可以获得更重要的信息,因为自愿性肌肉收缩取决于整个事件链的整合,并且是中枢神经系统与周围系统的不同组成部分之间的复杂相互作用。这项研究首先通过确定持续自愿性肌肉收缩的两个阶段中的脑电图(EEG)-肌电图(EMG)相干性来评估健康人中肌肉疲劳对fCMC的影响,一个阶段疲劳最小,另一个阶段疲劳严重。所获得的结果表明,尽管伴随肌肉疲劳的EEG和EMG活动的功率都升高了,但疲劳削弱了这两个信号之间的fCMC强度。其次,鉴于CRF的中枢和周围疲劳之间存在较大差异,因此,通过比较具有健康对照的CRF患者在肌肉疲劳任务期间的脑电图-心电图相关性,评估了癌症相关疲劳对fCMC的影响。在最小的疲劳阶段,CRF患者的fCMC显着低于健康对照组,这可能是由于患者可能的病理生理损伤所致。最后,为了更好地了解动态疲劳对fCMC的影响,开发了基于Morlet小波的单项试验相关性估计,并将其用于调查单项试验在重复最大肌肉收缩过程中对fCMC的疲劳影响。揭示了fCMC的下降模式在受试者之间变化,但是总体下降趋势在受试者之间是一致的。单项试验的结果表明,在急性神经肌肉不稳定的情况下,例如肌肉疲劳,可以检测到更多的动态fCMC适应性;该研究表明,肌肉疲劳会损害中枢神经系统和外周神经肌肉系统之间的正常耦合,这可能是由于在疲劳影响下导致性能下降的主要因素。通常,即使没有肌肉疲劳的影响,具有疲劳症状的癌症患者也会表现出明显的fCMC减弱。该发现对理解肌肉疲劳和癌症相关疲劳的神经机制,以及指导开发新方法以改善临床人群中疲劳症状的诊断和治疗具有潜在的重要意义。

著录项

  • 作者

    Yang, Qi.;

  • 作者单位

    Cleveland State University.;

  • 授予单位 Cleveland State University.;
  • 学科 Engineering Biomedical.
  • 学位 D.Eng.
  • 年度 2008
  • 页码 113 p.
  • 总页数 113
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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