Wavelet coherence analysis of muscle coupling during reaching movement in stroke

Inter-muscular coherence in speed skaters with skater's cramp

INTRODUCTION

Skater's cramp is a career-ending movement disorder in expert speed skaters noted to be a likely task-specific dystonia. In other movement disorders, including task-specific dystonia, studies have found evidence of central dysregulation expressed as higher inter-muscular coherence. We looked at whether inter-muscular coherence was higher in affected skaters as a possible indicator that it is centrally driven, and by extension further evidence it is a task-specific dystonia.

METHODS

In 14 affected and 14 control skaters we calculated inter-muscular coherence in the theta-band in a stationary task where tonic muscle activation was measured at 10%, 20% and 50% of maximum voluntary contraction. Additionally, we calculated wavelet coherence while skating at key moments in the stroke cycle.

RESULTS

Coherence did not differ in the stationary activation task. While skating, coherence was higher in the impacted leg of affected skaters compared to their non-impacted leg, p = .05, η² = 0.031, and amplitude of electromyography correlated with coherence in the impacted leg, p = .009, R²_adjusted = 0.41. A sub-group of severely affected skaters (n = 6) had higher coherence in the impacted leg compared to the left and right leg of controls, p = .02, Cohen's d = 1.59 and p = .01, Cohen's d = 1.63 respectively. Results were less clear across the entire affected cohort probably due to a diverse case-mix.

CONCLUSION

Our results of higher coherence in certain severe cases of skater's cramp is preliminary evidence of a central dysregulation, making the likelihood it is a task-specific dystonia higher.

Changes in intermuscular connectivity during active elbow extension reveal a functional simplification of motor control after stroke

Background

Stroke alters muscle co-activation and notably leads to exaggerated antagonist co-contraction responsible for impaired motor function. However, the mechanisms underlying this exaggerated antagonist co-contraction remain unclear. To fill this gap, the analysis of oscillatory synchronicity in electromyographic signals from synergistic muscles, also called intermuscular coherence, was a relevant tool.

Objective

This study compares functional intermuscular connectivity between muscle pairs of the paretic and non-paretic upper limbs of stroke subjects and the dominant limb of control subjects, concomitantly between two muscle pairs with a different functional role, through an intermuscular coherence analysis.

Methods

Twenty-four chronic stroke subjects and twenty-four healthy control subjects were included. Subjects performed twenty elbow extensions while kinematic data and electromyographic activity of both flexor and extensor elbow muscles were recorded. Intermuscular coherence was analyzed in the beta frequency band compared to the assessment of antagonist co-contraction.

Results

Intermuscular coherence was higher in the stroke subjects’ paretic limbs compared to control subjects. For stroke subjects, the intermuscular coherence of the antagonist-antagonist muscle pair (biceps brachii—brachioradialis) was higher than that of the agonist-antagonist muscle pair (triceps brachii—brachioradialis). For the paretic limb, intermuscular coherence of the antagonist-antagonist muscle pair presented a negative relationship with antagonist co-contraction.

Conclusion

Differences in intermuscular coherence between the paretic limbs of stroke subjects and control subjects suggest a higher common central drive during movement. Furthermore, results highlight the association between stroke-related alteration of intermuscular functional connectivity and the alteration of motor function.

Analysis of wearable time series data in endocrine and metabolic research

Many hormones in the body oscillate with different frequencies and amplitudes, creating a dynamic environment that is essential to maintain health. In humans, disruptions to these rhythms are strongly associated with increased morbidity and mortality. While mathematical models can help us understand rhythm misalignment, translating this insight into personalised healthcare technologies requires solving additional challenges. Here, we discuss how combining minimally invasive, high-frequency biosampling technologies with wearable devices can assist the development of hormonal surrogates. We review bespoke algorithms that can help analyse multidimensional, noisy, time series data and identify wearable signals that could constitute clinical proxies of endocrine rhythms. These techniques can support the development of computational biomarkers to support the diagnosis and management of endocrine and metabolic conditions.