ABSTRACT
Repeatedly performing a submaximal motor task for a prolonged period of time leads to muscle fatigue comprising a central and peripheral component, which demands a gradually increasing effort. However, the brain contribution to the enhancement of effort to cope with progressing fatigue lacks a complete understanding. The intermittent motor tasks (IMTs) closely resemble many activities of daily living (ADL), thus remaining physiologically relevant to study fatigue. The scope of this study is therefore to investigate the EEG-based brain activation patterns in healthy subjects performing IMT until self-perceived exhaustion. Fourteen participants (median age 51.5 years; age range 26-72 years; 5 males) repeated elbow flexion contractions at 40% maximum voluntary contraction by following visual cues displayed on an oscilloscope screen until subjective exhaustion. Each contraction lasted for approximately 5 s with a 2-s rest between trials. The force, EEG, and surface EMG (from elbow joint muscles) data were simultaneously collected. After preprocessing, we selected a subset of trials at the beginning, middle, and end of the study session representing brain activities germane to mild, moderate, and severe fatigue conditions, respectively, to compare and contrast the changes in the EEG time-frequency (TF) characteristics across the conditions. The outcome of channel- and source-level TF analyses reveals that the theta, alpha, and beta power spectral densities vary in proportion to fatigue levels in cortical motor areas. We observed a statistically significant change in the band-specific spectral power in relation to the graded fatigue from both the steady- and post-contraction EEG data. The findings would enhance our understanding on the etiology and physiology of voluntary motor-action-related fatigue and provide pointers to counteract the perception of muscle weakness and lack of motor endurance associated with ADL. The study outcome would help rationalize why certain patients experience exacerbated fatigue while carrying out mundane tasks, evaluate how clinical conditions such as neurological disorders and cancer treatment alter neural mechanisms underlying fatigue in future studies, and develop therapeutic strategies for restoring the patients’ ability to participate in ADL by mitigating the central and muscle fatigue.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
1. Section 5 is revised to clarify the revised statistical framework. 2. Figures 2 and 4 are removed 3. Figure 5 is revised (now Figure 3) 4. Due to the revised statistical framework, all the figures (Fig. 8-15 in version v2) corresponding to the PSD vs. Frequency plots are now updated. 5. The results and discussion related to the revised analysis are now updated in Sections 6.4 and 6.5. 6. Tables 1 and 2 are now revised to reflect the changes in the statistical framework. 7. Supplemental files are updated with the additional results related to: (a) Linear SVM-based classification of ERSP features corresponding to different fatigue conditions (b) Effects of Gender on the ERSP analysis (c) Inter-subject variability in the ERSP analysis
