Finger stability in precision grips

Stable precision grips using the fingertips are a cornerstone of human hand dexterity. Occasionally, however, our fingers become unstable and snap into a hyper-extended posture. This is because multi-link mechanisms, like our fingers, can buckle under tip forces. Suppressing this instability is crucial for hand dexterity, but how the neuromuscular system does so is unknown. Here we show that finger stability is due to the stiffness from muscle contraction and likely not feedback control. We recorded maximal force application with the index finger and found that most buckling events lasted less than 50ms, too fast for sensorimotor feedback to act. However, a biomechanical model of the finger predicted that muscle-induced stiffness is also insufficient for stability at maximal force unless we add springs to stiffen the joints. We tested this prediction in 39 volunteers. Upon adding stiffness, maximal force increased by 34±3%, and muscle electromyography readings were 21±3% higher for the finger flexors (mean±standard error). Hence, people refrain from applying truly maximal force unless an external stabilizing stiffness allows their muscles to apply higher force without losing stability. Muscle recordings and mathematical modeling show that the splint offloads the demand for muscle co-contraction and this reduced co-contraction with the splint underlies the increase in force. But more stiffness is not always better. Stiff fingers would interfere the ability to passively adapt to complex object geometries and precisely regulate force. Thus, our results show how hand function arises from neurally tuned muscle stiffness that balances finger stability with compliance.

Sensorimotor feedback loops are selectively sensitive to reward

While it is well established that motivational factors such as earning more money for performing well improve motor performance, how the motor system implements this improvement remains unclear. For instance, feedback-based control, which uses sensory feedback from the body to correct for errors in movement, improves with greater reward. But feedback control encompasses many feedback loops with diverse characteristics such as the brain regions involved and their response time. Which specific loops drive these performance improvements with reward is unknown, even though their diversity makes it unlikely that they are contributing uniformly. This lack of mechanistic insight leads to practical limitations in applications using reward, such as clinical rehabilitation, athletic coaching, and brain-inspired robotics. We systematically tested the effect of reward on the latency (how long for a corrective response to arise?) and gain (how large is the corrective response?) of eight distinct sensorimotor feedback loops in humans. Only the feedback responses known to rely on prefrontal associative cortices showed sensitivity to reward, while feedback responses that relied mainly on premotor and sensorimotor cortex did not show sensitivity to reward. Our results may have implications regarding feedback control performance in pathologies showing a cognitive decline, or on athletic coaching. For instance, coaching methodologies that rely on reinforcement or "reward shaping" may need to specifically target aspects of movement that rely on reward-sensitive feedback responses.

Abnormal center of mass feedback responses during balance: A potential biomarker of falls in Parkinson’s disease

Although Parkinson disease (PD) causes profound balance impairments, we know very little about how PD impacts the sensorimotor networks we rely on for automatically maintaining balance control. In young healthy people and animals, muscles are activated in a precise temporal and spatial organization when the center of body mass (CoM) is unexpectedly moved that is largely automatic and determined by feedback of CoM motion. Here, we show that PD alters the sensitivity of the sensorimotor feedback transformation. Importantly, sensorimotor feedback transformations for balance in PD remain temporally precise, but become spatially diffuse by recruiting additional muscle activity in antagonist muscles during balance responses. The abnormal antagonist muscle activity remains precisely time-locked to sensorimotor feedback signals encoding undesirable motion of the body in space. Further, among people with PD, the sensitivity of abnormal antagonist muscle activity to CoM motion varies directly with the number of recent falls. Our work shows that in people with PD, sensorimotor feedback transformations for balance are intact but disinhibited in antagonist muscles, likely contributing to balance deficits and falls.

Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback

How humans visually select where to grasp objects is determined by the physical object properties (e.g., size, shape, weight), the degrees of freedom of the arm and hand, as well as the task to be performed. We recently demonstrated that human grasps are near-optimal with respect to a weighted combination of different cost functions that make grasps uncomfortable, unstable, or impossible, e.g., due to unnatural grasp apertures or large torques. Here, we ask whether humans can consciously access these rules. We test if humans can explicitly judge grasp quality derived from rules regarding grasp size, orientation, torque, and visibility. More specifically, we test if grasp quality can be inferred (i) by using visual cues and motor imagery alone, (ii) from watching grasps executed by others, and (iii) through performing grasps, i.e., receiving visual, proprioceptive and haptic feedback. Stimuli were novel objects made of 10 cubes of brass and wood (side length 2.5 cm) in various configurations. On each object, one near-optimal and one sub-optimal grasp were selected based on one cost function (e.g., torque), while the other constraints (grasp size, orientation, and visibility) were kept approximately constant or counterbalanced. Participants were visually cued to the location of the selected grasps on each object and verbally reported which of the two grasps was best. Across three experiments, participants were required to either (i) passively view the static objects and imagine executing the two competing grasps, (ii) passively view videos of other participants grasping the objects, or (iii) actively grasp the objects themselves. Our results show that, for a majority of tested objects, participants could already judge grasp optimality from simply viewing the objects and imagining to grasp them, but were significantly better in the video and grasping session. These findings suggest that humans can determine grasp quality even without performing the grasp—perhaps through motor imagery—and can further refine their understanding of how to correctly grasp an object through sensorimotor feedback but also by passively viewing others grasp objects.

How using brain-machine interfaces influences the human sense of agency

Brain-machine interfaces (BMI) allows individuals to control an external device by controlling their own brain activity, without requiring bodily or muscle movements. Performing voluntary movements is associated with the experience of agency (“sense of agency”) over those movements and their outcomes. When people voluntarily control a BMI, they should likewise experience a sense of agency. However, using a BMI to act presents several differences compared to normal movements. In particular, BMIs lack sensorimotor feedback, afford lower controllability and are associated with increased cognitive fatigue. Here, we explored how these different factors influence the sense of agency across two studies in which participants learned to control a robotic hand through motor imagery decoded online through electroencephalography. We observed that the lack of sensorimotor information when using a BMI did not appear to influence the sense of agency. We further observed that experiencing lower control over the BMI reduced the sense of agency. Finally, we observed that the better participants controlled the BMI, the greater was the appropriation of the robotic hand, as measured by body-ownership and agency scores. Results are discussed based on existing theories on the sense of agency in light of the importance of BMI technology for patients using prosthetic limbs.

Humans can visually judge grasp quality and refine their judgments through visual and haptic feedback

How humans visually select where to grasp objects is determined by the physical object properties (e.g., size, shape, weight), the degrees of freedom of the arm and hand, as well as the task to be performed. We recently demonstrated that human grasps are near-optimal with respect to a weighted combination of different cost functions that make grasps uncomfortable, unstable or impossible e.g., due to unnatural grasp apertures or large torques. Here, we ask whether humans can consciously access these rules. We test if humans can explicitly judge grasp quality derived from rules regarding grasp size, orientation, torque, and visibility. More specifically, we test if grasp quality can be inferred (i) by using motor imagery alone, (ii) from watching grasps executed by others, and (iii) through performing grasps, i.e. receiving visual, proprioceptive and haptic feedback. Stimuli were novel objects made of 10 cubes of brass and wood (side length 2.5 cm) in various configurations. On each object, one near-optimal and one sub-optimal grasp were selected based on one cost function (e.g. torque), while the other constraints (grasp size, orientation, and visibility) were kept approximately constant or counterbalanced. Participants were visually cued to the location of the selected grasps on each object and verbally reported which of the two grasps was best. Across three experiments, participants could either (i) passively view the static objects, (ii) passively view videos of other participants grasping the objects, or (iii) actively grasp the objects themselves. Our results show that participants could already judge grasp optimality from simply viewing the objects, but were significantly better in the video and grasping session. These findings suggest that humans can determine grasp quality even without performing the grasp—perhaps through motor imagery—and can further refine their understanding of how to correctly grasp an object through sensorimotor feedback but also by passively viewing others grasp objects.

Abnormal center of mass control during balance is associated with falls in Parkinson’s disease

Although Parkinson disease (PD) causes profound balance impairments, we know very little about how PD impacts the sensorimotor networks we rely on for automatically maintaining balance control. In young healthy people and animals, muscles are activated in a precise temporal and spatial organization when the center of body mass (CoM) is unexpectedly moved. This organization is largely automatic and determined by feedback of CoM motion. Here, we show that PD alters the sensitivity of the sensorimotor feedback transformation. Importantly, sensorimotor feedback transformations for balance in PD remain temporally precise, but become spatially diffuse by recruiting additional muscle activity in antagonist muscles during balance responses. The abnormal antagonist muscle activity remains precisely time-locked to sensorimotor feedback signals encoding undesirable motion of the body in space. Further, among people with PD, the sensitivity of abnormal antagonist muscle activity to CoM motion varies directly with the number of recent falls. Our work shows that in people with PD, sensorimotor feedback transformations for balance are intact but disinhibited in antagonist muscles, likely contributing to balance deficits and falls.

The Role of Motor System in Mental Rotation: New Insights from Myotonic Dystrophy Type 1

Objective:This study explored mental rotation (MR) performance in patients with myotonic dystrophy 1 (DM1), an inherited neuromuscular disorder dominated by muscular symptoms, including muscle weakness and myotonia. The aim of the study was twofold: to gain new insights into the neurocognitive mechanisms of MR and to better clarify the cognitive profile of DM1 patients. To address these aims, we used MR tasks involving kinds of stimuli that varied for the extent to which they emphasized motor simulation and activation of body representations (body parts) versus visuospatial imagery (abstract objects). We hypothesized that, if peripheral sensorimotor feedback system plays a pivotal role in modulating MR performance, then DM1 patients would exhibit more difficulties in mentally rotating hand stimuli than abstract objects.Method:Twenty-four DM1 patients and twenty-four age- and education-matched control subjects were enrolled in the study and were required to perform two computerized MR tasks involving pictures of hands and abstract objects.Results:The analysis of accuracy showed that patients had impaired MR performance when the angular disparities between the stimuli were higher. Notably, as compared to controls, patients showed slower responses when the stimuli were hands, whereas no significant differences when stimuli were objects.Conclusion:The findings are coherent with the embodied cognition view, indicating a tight relation between body- and motor-related processes and MR. They suggest that peripheral, muscular, abnormalities in DM1 lead to alterations in manipulation of motor representations, which in turn affect MR, especially when body parts are to mentally rotate.