Visual delay affects force scaling and weight perception during object lifting in virtual reality.

Lifting an object requires precise scaling of fingertip forces based on a prediction of object weight. At object contact, a series of tactile and visual events arise that need to be rapidly processed online to fine-tune the planned motor commands for lifting the object. The brain mechanisms underlying multisensory integration serially at transient sensorimotor events, a general feature of actions requiring hand-object interactions, are not yet understood. In this study we tested the relative weighting between haptic and visual signals when they are integrated online into the motor command. We used a new virtual reality setup to desynchronize visual feedback from haptics, which allowed us to probe the relative contribution of haptics and vision in driving participants' movements when they grasped virtual objects simulated by two force-feedback robots. We found that visual delay changed the profile of fingertip force generation and led participants to perceive objects as heavier than when lifts were performed without visual delay. We further modeled the effect of vision on motor output by manipulating the extent to which delayed visual events could bias the force profile, which allowed us to determine the specific weighting the brain assigns to haptics and vision. Our results show for the first time how visuo-haptic integration is processed at discrete sensorimotor events for controlling object-lifting dynamics and further highlight the organization of multisensory signals online for controlling action and perception. NEW & NOTEWORTHY Dexterous hand movements require rapid integration of information from different senses, in particular touch and vision, at different key time points as movement unfolds. The relative weighting between vision and haptics for object manipulation is unknown. We used object lifting in virtual reality to desynchronize visual and haptic feedback and find out their relative weightings. Our findings shed light on how rapid multisensory integration is processed over a series of discrete sensorimotor control points.

Prediction of muscle activity during loaded movements of the upper limb.

BACKGROUND: Accurate prediction of electromyographic (EMG) signals associated with a variety of motor behaviors could, in theory, serve as activity templates needed to evoke movements in paralyzed individuals using functional electrical stimulation. Such predictions should encompass complex multi-joint movements and include interactions with objects in the environment. METHODS: Here we tested the ability of different artificial neural networks (ANNs) to predict EMG activities of 12 arm muscles while human subjects made free movements of the arm or grasped and moved objects of different weights and dimensions. Inputs to the trained ANNs included hand position, hand orientation, and thumb grip force. RESULTS: The ability of ANNs to predict EMG was equally as good for tasks involving interactions with external loads as for unloaded movements. The ANN that yielded the best predictions was a feed-forward network consisting of a single hidden layer of 30 neural elements. For this network, the average coefficient of determination (R2 value) between predicted and actual EMG signals across all nine subjects and 12 muscles during movements that involved episodes of moving objects was 0.43. CONCLUSION: This reasonable accuracy suggests that ANNs could be used to provide an initial estimate of the complex patterns of muscle stimulation needed to produce a wide array of movements, including those involving object interaction, in paralyzed individuals.

Three-dimensional model to predict muscle forces and their relation to motor variances in reaching arm movements.

A three-dimensional (3-D) arm movement model is presented to simulate kinematic properties and muscle forces in reaching arm movements. Healthy subjects performed reaching movements repetitively either with or without a load in the hand. Joint coordinates were measured. Muscle moment arms, 3-D angular acceleration, and moment of inertias of arm segments were calculated to determine 3-D joint torques. Variances of hand position, arm configuration, and muscle activities were calculated. Ratios of movement variances observed in the two conditions (load versus without load) showed no differences for hand position and arm configuration variances. Virtual muscle force variances for all muscles except deltoid posterior and EMG variances for four muscles increased significantly by moving with the load. The greatly increased variances in muscle activity did not imply equally high increments in kinematic variances. We conclude that enhanced muscle cooperation through synergies helps to stabilize movement at the kinematic level when a load is added.

Motor training reduces surround inhibition in the motor cortex.

OBJECTIVE: Surround inhibition (SI) is thought to facilitate focal contraction of a hand muscle by keeping nearby muscles silent. Unexpectedly, SI is reduced in skilled pianists. We tested whether repeated practice of focal contraction in non-pianists could reduce SI. METHODS: Motor-evoked potentials were elicited by transcranial magnetic stimulation in the relaxed abductor digiti minimi randomly at the onset and 5s after offset of a 2s focal contraction (10% maximum) of the first dorsal interosseous (FDI). Over 5 blocks of 40 trials participants obtained points for increasing contraction speed and stability in FDI. In a final block, the interval between contractions was varied randomly to increase attention to the task. RESULTS: Over the first 5 blocks, SI declined as performance (points scored) improved. In the final "attention" block SI increased towards baseline without affecting performance. CONCLUSIONS: Although SI may be useful during the early stages of learning, skilled focal finger movement does not require SI to prevent activity in non-involved muscles. This could be due to better targeting of the excitatory command to move. Results from the final block suggest that increased attention can re-engage SI when task parameters change. SIGNIFICANCE: SI is not necessary for successful focal contraction, but may contribute during learning and during attention to task.

The effect of load on torques in point-to-point arm movements: a 3D model.

A dynamic, 3-dimensional model was developed to simulate slightly restricted (pronation-supination was not allowed) point-to-point movements of the upper limb under different external loads, which were modeled using 3 objects of distinct masses held in the hand. The model considered structural and biomechanical properties of the arm and measured coordinates of joint positions. The model predicted muscle torques generated by muscles and needed to produce the measured rotations in the shoulder and elbow joints. The effect of different object masses on torque profiles, magnitudes, and directions were studied. Correlation analysis has shown that torque profiles in the shoulder and elbow joints are load invariant. The shape of the torque magnitude-time curve is load invariant but it is scaled with the mass of the load. Objects with larger masses are associated with a lower deflection of the elbow torque with respect to the sagittal plane. Torque direction-time curve is load invariant scaled with the mass of the load. The authors propose that the load invariance of the torque magnitude-time curve and torque direction-time curve holds for object transporting arm movements not restricted to a plane.