Madhusudhan Venkadesan

Foot strike patterns and collision forces in habitually barefoot versus shod runners

Humans have engaged in endurance running for millions of years, but the modern running shoe was not invented until the 1970s. For most of human evolutionary history, runners were either barefoot or wore minimal footwear such as sandals or moccasins with smaller heels and little cushioning relative to modern running shoes. We wondered how runners coped with the impact caused by the foot colliding with the ground before the invention of the modern shoe. Here we show that habitually barefoot endurance runners often land on the fore-foot (fore-foot strike) before bringing down the heel, but they sometimes land with a flat foot (mid-foot strike) or, less often, on the heel (rear-foot strike). In contrast, habitually shod runners mostly rear-foot strike, facilitated by the elevated and cushioned heel of the modern running shoe. Kinematic and kinetic analyses show that even on hard surfaces, barefoot runners who fore-foot strike generate smaller collision forces than shod rear-foot strikers. This difference results primarily from a more plantarflexed foot at landing and more ankle compliance during impact, decreasing the effective mass of the body that collides with the ground. Fore-foot- and mid-foot-strike gaits were probably more common when humans ran barefoot or in minimal shoes, and may protect the feet and lower limbs from some of the impact-related injuries now experienced by a high percentage of runners.

Structured variability of muscle activations supports the minimal intervention principle of motor control

Numerous observations of structured motor variability indicate that the sensorimotor system preferentially controls task-relevant parameters while allowing task-irrelevant ones to fluctuate. Optimality models show that controlling a redundant musculo-skeletal system in this manner meets task demands while minimizing control effort. Although this line of inquiry has been very productive, the data are mostly behavioral with no direct physiological evidence on the level of muscle or neural activity. Furthermore, biomechanical coupling, signal-dependent noise, and alternative causes of trial-to-trial variability confound behavioral studies. Here we address those confounds and present evidence that the nervous system preferentially controls task-relevant parameters on the muscle level. We asked subjects to produce vertical fingertip force vectors of prescribed constant or time-varying magnitudes while maintaining a constant finger posture. We recorded intramuscular electromyograms (EMGs) simultaneously from all seven index finger muscles during this task. The experiment design and selective fine-wire muscle recordings allowed us to account for a median of 91% of the variance of fingertip forces given the EMG signals. By analyzing muscle coordination in the seven-dimensional EMG signal space, we find that variance-per-dimension is consistently smaller in the task-relevant subspace than in the task-irrelevant subspace. This first direct physiological evidence on the muscle level for preferential control of task-relevant parameters strongly suggest the use of a neural control strategy compatible with the principle of minimal intervention. Additionally, variance is nonnegligible in all seven dimensions, which is at odds with the view that muscle activation patterns are composed from a small number of synergies.

Elastic energy storage in the shoulder and the evolution of high-speed throwing in Homo

Some primates, including chimpanzees, throw objects occasionally, but only humans regularly throw projectiles with high speed and accuracy. Darwin noted that the unique throwing abilities of humans, which were made possible when bipedalism emancipated the arms, enabled foragers to hunt effectively using projectiles. However, there has been little consideration of the evolution of throwing in the years since Darwin made his observations, in part because of a lack of evidence of when, how and why hominins evolved the ability to generate high-speed throws. Here we use experimental studies of humans throwing projectiles to show that our throwing capabilities largely result from several derived anatomical features that enable elastic energy storage and release at the shoulder. These features first appear together approximately 2 million years ago in the species Homo erectus. Taking into consideration archaeological evidence suggesting that hunting activity intensified around this time, we conclude that selection for throwing as a means to hunt probably had an important role in the evolution of the genus Homo.

The strength-dexterity test as a measure of dynamic pinch performance.

We have developed a method to quantify the dynamic interaction between fingertip force magnitude (strength) and directional control (dexterity) during pinch with a novel strength-dexterity (S-D) test based on the principle of buckling of compression springs. The test consists of asking participants to use key and opposition pinch to attempt to fully compress springs, in random order, with a wide range of combinations of strength and dexterity requirements. The minimum force required to fully compress the spring and the propensity of the spring to buckle define the strength and dexterity requirements, respectively. The S-D score for each pinch style was the sum of the strength values of all springs successfully compressed fully. We tested 3 participant groups: 18 unimpaired young adults (40yr), and 14 adults diagnosed with carpo-metacarpal osteoarthritis (CMC OA) (>or = 36yr). We investigated the repeatability of the S-D test with 74 springs by testing 14 young adults twice on different days. The per-spring repeatability across subjects was >or = 94%. A minimum performance score for young adults was found as they all could compress a subset of 39 springs. Using this subset of springs, we compared the ability of the S-D score vs. maximal pinch force values to distinguish unimpaired hands from those with CMC OA of the thumb. The score for this 39-spring S-D test distinguished between CMC OA and asymptomatic older adults, whereas pinch meter readings did not (p<0.05). We conclude that the S-D test is repeatable and applicable to clinical research. We propose including the S-D test in studies aiming to quantify impairment and compare treatment outcomes in orthopaedic and neurological afflictions that degrade dynamic manipulation.

Neural Control of Motion-to-Force Transitions with the Fingertip

The neural control of tasks such as rapid acquisition of precision pinch remains unknown. Therefore, we investigated the neural control of finger musculature when the index fingertip abruptly transitions from motion to static force production. Nine subjects produced a downward tapping motion followed by vertical fingertip force against a rigid surface. We simultaneously recorded three-dimensional fingertip force, plus the complete muscle coordination pattern using intramuscular electromyograms from all seven index finger muscles. We found that the muscle coordination pattern clearly switched from that for motion to that for isometric force ∼65 ms before contact (p = 0.0004). Mathematical modeling and analysis revealed that the underlying neural control also switched between mutually incompatible strategies in a time-critical manner. Importantly, this abrupt switch in underlying neural control polluted fingertip force vector direction beyond what is explained by muscle activation-contraction dynamics and neuromuscular noise (p ≤ 0.003). We further ruled out an impedance control strategy in a separate test showing no systematic change in initial force magnitude for catch trials where the tapping surface was surreptitiously lowered and raised (p = 0.93). We conclude that the nervous system predictively switches between mutually incompatible neural control strategies to bridge the abrupt transition in mechanical constraints between motion and static force. Moreover because the nervous system cannot switch between control strategies instantaneously or exactly, there arise physical limits to the accuracy of force production on contact. The need for such a neurally demanding and time-critical strategy for routine motion-to-force transitions with the fingertip may explain the existence of specialized neural circuits for the human hand.

Controlling instabilities in manipulation requires specific cortical-striatal-cerebellar networks

Dexterous manipulation requires both strength, the ability to produce fingertip forces of a specific magnitude, and dexterity, the ability to dynamically regulate the magnitude and direction of fingertip force vectors and finger motions. Although cortical activity in fronto-parietal networks has been established for stable grip and pinch forces, the cortical regulation in the dexterous control of unstable objects remains unknown. We used functional magnetic resonance imaging (fMRI) to interrogate cortical networks engaged in the control of four objects with increasing instabilities but requiring constant strength. In addition to expected activity in fronto-parietal networks we find that dexterous manipulation of increasingly unstable objects is associated with a linear increase in the amplitude of the BOLD signal in the basal ganglia (P = 0.007 and P = 0.023 for 2 compression tasks). A computational regression (connectivity) model identified independent subsets of cortical networks whose connection strengths were mutable and associated with object instability (P < 0.001). Our results suggest that in the presence of object instability, the basal ganglia may modulate the activity of premotor areas and subsequent motor output. This work, therefore, provides new evidence for the selectable cortical representation and execution of dynamic multifinger manipulation for grasp stability.

Maximal Voluntary Fingertip Force Production Is Not Limited by Movement Speed in Combined Motion and Force Tasks

Numerous studies of limbs and fingers propose that force–velocity properties of muscle limit maximal voluntary force production during anisometric tasks, i.e., when muscles are shortening or lengthening. Although this proposition appears logical, our study on the simultaneous production of fingertip motion and force disagrees with this commonly held notion. We asked eight consenting adults to use their dominant index fingertip to maximize voluntary downward force against a horizontal surface at specific postures (static trials), and also during an anisometric “scratching” task of rhythmically moving the fingertip along a 5.8 ± 0.5 cm target line. The metronome-timed flexion–extension movement speed varied 36-fold from “slow” (1.0 ± 0.5 cm/s) to “fast” (35.9 ± 7.8 cm/s). As expected, maximal downward voluntary force diminished (44.8 ± 15.6%; p = 0.001) when any motion (slow or fast) was added to the task. Surprisingly, however, a 36-fold increase in speed did not affect this reduction in force magnitude. These remarkable results for such an ordinary task challenge the dominant role often attributed to force–velocity properties of muscle and provide insight into neuromechanical interactions. We propose an explanation that the simultaneous enforcement of mechanical constraints for motion and force reduces the set of feasible motor commands sufficiently so that force–velocity properties cease to be the force-limiting factor. While additional work is necessary to reveal the governing mechanisms, the dramatic influence that the simultaneous enforcement of motion and force constraints has on force output begins to explain the vulnerability of dexterous function to development, aging, and even mild neuromuscular pathology.

Dynamical feature extraction at the sensory periphery guides chemotaxis.

Behavioral strategies employed for chemotaxis have been described across phyla, but the sensorimotor basis of this phenomenon has seldom been studied in naturalistic contexts. Here, we examine how signals experienced during free olfactory behaviors are processed by first-order olfactory sensory neurons (OSNs) of the Drosophila larva. We find that OSNs can act as differentiators that transiently normalize stimulus intensity—a property potentially derived from a combination of integral feedback and feed-forward regulation of olfactory transduction. In olfactory virtual reality experiments, we report that high activity levels of the OSN suppress turning, whereas low activity levels facilitate turning. Using a generalized linear model, we explain how peripheral encoding of olfactory stimuli modulates the probability of switching from a run to a turn. Our work clarifies the link between computations carried out at the sensory periphery and action selection underlying navigation in odor gradients. DOI: http://dx.doi.org/10.7554/eLife.06694.001

Effects of neuromuscular lags on controlling contact transitions

We present a numerical exploration of contact transitions with the fingertip. When picking up objects our fingertips must make contact at specific locations, and—upon contact—maintain posture while producing well-directed force vectors. However, the joint torques for moving the fingertip towards a surface (τm) are different from those for producing static force vectors (τf). We previously described the neural control of such abrupt transitions in humans, and found that unavoidable errors arise because sensorimotor time delays and lags prevent an instantaneous switch between different torques. Here, we use numerical optimization on a finger model to reveal physical bounds for controlling such rapid contact transitions. Resembling human data, it is necessary to anticipatorily switch joint torques to τf at about 30 ms before contact to minimize the initial misdirection of the fingertip force vector. This anticipatory strategy arises in our deterministic model from neuromuscular lags, and not from optimizing for robustness to noise/uncertainties. Importantly, the optimal solution also leads to a trade-off between the speed of force magnitude increase versus the accuracy of initial force direction. This is an alternative to prevailing theories that propose multiplicative noise in muscles as the driver of speed–accuracy trade-offs. We instead find that the speed–accuracy trade-off arises solely from neuromuscular lags. Finally, because our model intentionally uses idealized assumptions, its agreement with human data suggests that the biological system is controlled in a way that approaches the physical boundaries of performance.

Optimal strategies for throwing accurately

The accuracy of throwing in games and sports is governed by how errors in planning and initial conditions are propagated by the dynamics of the projectile. In the simplest setting, the projectile path is typically described by a deterministic parabolic trajectory which has the potential to amplify noisy launch conditions. By analysing how parabolic trajectories propagate errors, we show how to devise optimal strategies for a throwing task demanding accuracy. Our calculations explain observed speed–accuracy trade-offs, preferred throwing style of overarm versus underarm, and strategies for games such as dart throwing, despite having left out most biological complexities. As our criteria for optimal performance depend on the target location, shape and the level of uncertainty in planning, they also naturally suggest an iterative scheme to learn throwing strategies by trial and error.