Real-time slacking as a default mode of grip force control: implications for force minimization and personal grip force variation

Entropy in Electroencephalographic Signals Modulates with Force Magnitude During Grasping - A Preliminary Report

The ability to hold objects relies on neural processes underlying grip force control during grasping. Brain activity lateralized to contralateral hemisphere averaged over trials is associated with grip force applied on an object. However, the involvement of neural variability within-trial during grip force control remains unclear. We examined dependence of neural variability over frontal, central, and parietal regions of interest (ROI) on grip force magnitude using noninvasive electroencephalography (EEG). We utilized our existing EEG dataset comprised of healthy young adults performing an isometric force control task, cued to exert 5, 10, or 15% of their maximum voluntary contraction (MVC) across trials and received visual feedback of their grip force. We quantified variability in EEG signal via sample entropy (sequence-dependent) and standard deviation (sequence-independent measure) over ROI. We found lateralized modulation in EEG sample entropy with force magnitude over central electrodes but not over frontal or parietal electrodes. However, modulation was not observed for standard deviation in the EEG activity. These findings highlight lateralized and spatially constrained modulation in sequence-dependent, but not sequence-independent component of EEG variability. We contextualize these findings in applications requiring finer precision (e.g., prosthesis), and propose directions for future studies investigating role of neural entropy in behavior.

Lateralized Neural Entropy modulates with Grip Force during Precision Grasping

When holding a coffee mug filled to the brim, we strive to avoid spilling the coffee. This ability relies on the neural processes underlying the control of finger forces on a moment-to-moment basis. The brain activity lateralized to the contralateral hemisphere averaged over a trial and across the trials is known to be associated with the magnitude of grip force applied on an object. However, the mechanistic involvement of the variability in neural signals during grip force control remains unclear. In this study, we examined the dependence of neural variability over the frontal, central, and parietal regions assessed using noninvasive electroencephalography (EEG) on grip force magnitude during an isometric force control task. We hypothesized laterally specific modulation in EEG variability with higher magnitude of the grip force exerted during grip force control. We utilized an existing EEG dataset (64 channel) comprised of healthy young adults, who performed an isometric force control task while receiving visual feedback of the force applied. The force magnitude to be exerted on the instrumented object was cued to participants during the task, and varied pseudorandomly among 5, 10, and 15% of their maximum voluntary contraction (MVC) across the trials. We quantified neural variability via sample entropy (sequence-dependent measure) and standard deviation (sequence-independent measure) of the temporal EEG signal over the frontal, central, and parietal electrodes. The EEG sample entropy over the central electrodes showed lateralized, nonlinear, localized, modulation with force magnitude. Similar modulation was not observed over frontal or parietal EEG activity, nor for standard deviation in the EEG activity. Our findings highlight specificity in neural control of grip forces by demonstrating the modulation in sequence-dependent but not sequence-independent component of EEG variability. This modulation appeared to be lateralized, spatially constrained, and functionally dependent on the grip force magnitude. We discuss the relevance of these findings in scenarios where a finer precision is essential to enable grasp application, such as prosthesis and associated neural signal integration, and propose directions for future studies investigating the mechanistic role of neural entropy in grip force control.

Providing Real-Time Wearable Feedback to Increase Hand Use after Stroke: A Randomized, Controlled Trial

After stroke, many people substantially reduce use of their impaired hand in daily life, even if they retain even a moderate level of functional hand ability. Here, we tested whether providing real-time, wearable feedback on the number of achieved hand movements, along with a daily goal, can help people increase hand use intensity. Twenty participants with chronic stroke wore the Manumeter, a novel magnetic wristwatch/ring system that counts finger and wrist movements. We randomized them to wear the device for three weeks with (feedback group) or without (control group) real-time hand count feedback and a daily goal. Participants in the control group used the device as a wristwatch, but it still counted hand movements. We found that the feedback group wore the Manumeter significantly longer (11.2 ± 1.3 h/day) compared to the control group (10.1 ± 1.1 h/day). The feedback group also significantly increased their hand counts over time (p = 0.012, slope = 9.0 hand counts/hour per day, which amounted to ~2000 additional counts per day by study end), while the control group did not (p-value = 0.059; slope = 4.87 hand counts/hour per day). There were no significant differences between groups in any clinical measures of hand movement ability that we measured before and after the feedback period, although several of these measures improved over time. Finally, we confirmed that the previously reported threshold relationship between hand functional capacity and daily use was stable over three weeks, even in the presence of feedback, and established the minimal detectable change for hand count intensity, which is about 30% of average daily intensity. These results suggest that disuse of the hand after stroke is temporarily modifiable with wearable feedback, but do not support that a 3-week intervention of wearable hand count feedback provides enduring therapeutic gains.

Expectation of volitional arm movement has prolonged effects on the grip force exerted on a pinched object

Humans closely coordinate the grip force exerted on a hand-held object with changes in the load arising from the object's dynamics. Recent work suggests the grip force is responsive to the predictability of the load forces as well. The well-known grip-force-load-force coupling is intermittent when the load arising from volitional movements fluctuates predictably, whereas grip force increases when loads are unpredictable. Here, we studied the influence of expected but uncertain volitional movements on the digit forces during a static grasp. Young, healthy participants used a pinch grasp to hold an instrumented object and track visual targets by moving the object. We quantified the mean grip force, the temporal decline in grip force (slacking), and the coupling between the pressing digit forces that yield the grip force during static prehension with no expectation of movement, and during the static phase of a choice reaction time task, when the participant expected to move the object after a variable duration. Simply expecting to move the object led to sustained (for at least 5 s) higher magnitude and lower slacking in the grip force, and weaker coupling between the pressing digit forces. These effects were modulated by the direction of the expected movement and the object's mass. The changes helped to maintain the safety margin for the current grasp and likely facilitated the transition from static to dynamic object manipulation. Influence of expected actions on the current grasp may have implications for manual dexterity and its well-known loss with age.

Force variability is mostly not motor noise: Theoretical implications for motor control

Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume 'motor noise' leads to force variability and its 'signal dependence' (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by 'motor noise.' Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.