The Step Response in Isometric Grip Force Tracking: A Model to Characterize Aging- and Stroke-Induced Changes

The correlation between upper body grip strength and resting-state EEG network

Current research in the field of neuroscience primarily focuses on the analysis of electroencephalogram (EEG) activities associated with movement within the central nervous system. However, there is a dearth of studies investigating the impact of prolonged individual strength training on the resting state of the brain. Therefore, it is crucial to examine the correlation between upper body grip strength and resting-state EEG networks. In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p < 0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands, with correlation coefficients greater than 0.60 (p < 0.01). Additionally, predicted MVC positively correlated with actual MVC, with a coefficient of 0.70 and root mean square error of 5.67 (p < 0.01). The results show that the resting-state EEG network is closely related to upper body grip strength, which can indirectly reflect an individual's muscle strength through the resting brain network.

Decrease in force control among older adults under unpredictable conditions

OBJECTIVES

Falls in older adults generally occur during unpredictable situations. Controlling posture through fine-tuned muscle force before and after falls is necessary to avoid serious injuries. However, details regarding force control among older adults during unpredictable situations are unclear. This study determined the features of force control in a random force-tracking task among older adults.

METHODS

Ten healthy older adults (67-76 years) and eight healthy young adults (20-23 years) participated in three force-tracking tasks with ankle plantar flexion: low-range (LR), high-range (HR), and pseudo-random (PR) force tasks. Force control ability was assessed using the root mean square error (RMSE) between the target and muscle forces produced by the participants. Muscle activities from the lateral head of the gastrocnemius and the tibialis anterior during each task were measured using surface electromyography to calculate the co-contraction index (CCI).

RESULTS

In all tasks, older adults (RMSEs: 1.09-3.70, CCIs: 29.4-56.4) had a significantly greater RMSEs and CCIs than young adults (RMSEs: 0.49-1.83, CCIs: 11.7-20.6; all, p < 0.05). The RMSEs during force generation were significantly greater than those during force release (LR: p < 0.01, HR: p < 0.05), except for the random force-tracking task in older adults. CCIs during the force release phase in both groups (older adults: 27.8-56.4, young adults: 15.0-20.6) were consistently greater than those during force generation (older adults: 24.5-50.4, young adults: 11.7-17.2). CCIs in force-tracing tasks differed in older adults, whereas those in the random force-tracing task increased. RMSEs and CCIs in the random and LR force-tracing tasks were significantly negatively correlated with the functional reach test (all: r > 0.5, p < 0.05).

CONCLUSION

Force control in older adults declines in low-band and random muscle force output. Moreover, increased CCIs in older adults are particularly pronounced during unpredictable situations.

Detection of functional connectivity in the brain during visuo-guided grip force tracking tasks: A functional near-infrared spectroscopy study

The functional connectivity (FC) between multiple brain regions during tasks is currently gradually being explored with functional near-infrared spectroscopy (fNIRS). However, the FC present during grip force tracking tasks performed under visual feedback remains unclear. In the present study, we used fNIRS to measure brain activity during resting states and grip force tracking tasks at 25%, 50%, and 75% of maximum voluntary contraction (MVC) in 11 healthy subjects, and the activity was measured from four target brain regions: the left prefrontal cortex (lPFC), right prefrontal cortex (rPFC), left sensorimotor cortex (lSMC), and right sensorimotor cortex (rSMC). We determined the FC between these regions utilizing three different methods: Pearson's correlation method, partial correlation method, and a pairwise maximum entropy model (MEM). The results showed that the FC of lSMC-rSMC and lPFC-rPFC (interhemispheric homologous pairs) were significantly stronger than those of other brain region pairs. Moreover, FC of lPFC-rPFC was strengthened during the 75% MVC task compared to the other task states and the resting states. The FC of lSMC-lPFC and rSMC-rPFC (intrahemispheric region pairs) strengthened with a higher task load. The results provided new insights into the FC between brain regions during visuo-guided grip force tracking tasks.