The brain has evolved an internal model of gravity to cope with life in the Earth's gravitational environment. How this internal model benefits the implementation of skilled movement has remained unsolved. One prevailing theory has assumed that this internal model is used to compensate for gravity's mechanical effects on the body, such as to maintain invariant motor trajectories. Alternatively, gravity force could be used purposely and efficiently for the planning and execution of voluntary movements, thereby resulting in direction-depending kinematics. Here we experimentally interrogate these two hypotheses by measuring arm kinematics while varying movement direction in normal and zero-G gravity conditions. By comparing experimental results with model predictions, we show that the brain uses the internal model to implement control policies that take advantage of gravity to minimize movement effort.DOI: http://dx.doi.org/10.7554/eLife.16394.001

. Although there is converging experimental and clinical evidences suggesting that mental training with motor imagery can improve motor performance, it is unclear how humans can learn movements through mental training despite the lack of sensory feedback from the body and the environment. In a first experiment, we measured the trial-by-trial decrease in durations of executed movements (physical training group) and mentally simulated movements (motor-imagery training group), by means of training on a multiple-target arm-pointing task requiring high accuracy and speed. Movement durations were significantly lower in posttest compared with pretest after both physical and motor-imagery training. Although both the posttraining performance and the rate of learning were smaller in motor-imagery training group than in physical training group, the change in movement duration and the asymptotic movement duration after a hypothetical large number of trials were identical. The two control groups (eye-movement training and rest groups) did not show change in movement duration. In the second experiment, additional kinematic analyses revealed that arm movements were straighter and faster both immediately and 24 h after physical and motor-imagery training. No such improvements were observed in the eye-movement training group. Our results suggest that the brain uses state estimation, provided by internal forward model predictions, to improve motor performance during mental training. Furthermore, our results suggest that mental practice can, at least in young healthy subjects and if given after a short bout of physical practice, be successfully substituted to physical practice to improve motor performance.

An important question in the literature focusing on motor control is to determine which laws drive biological limb movements. This question has prompted numerous investigations analyzing arm movements in both humans and monkeys. Many theories assume that among all possible movements the one actually performed satisfies an optimality criterion. In the framework of optimal control theory, a first approach is to choose a cost function and test whether the proposed model fits with experimental data. A second approach (generally considered as the more difficult) is to infer the cost function from behavioral data. The cost proposed here includes a term called the absolute work of forces, reflecting the mechanical energy expenditure. Contrary to most investigations studying optimality principles of arm movements, this model has the particularity of using a cost function that is not smooth. First, a mathematical theory related to both direct and inverse optimal control approaches is presented. The first theoretical result is the Inactivation Principle, according to which minimizing a term similar to the absolute work implies simultaneous inactivation of agonistic and antagonistic muscles acting on a single joint, near the time of peak velocity. The second theoretical result is that, conversely, the presence of non-smoothness in the cost function is a necessary condition for the existence of such inactivation. Second, during an experimental study, participants were asked to perform fast vertical arm movements with one, two, and three degrees of freedom. Observed trajectories, velocity profiles, and final postures were accurately simulated by the model. In accordance, electromyographic signals showed brief simultaneous inactivation of opposing muscles during movements. Thus, assuming that human movements are optimal with respect to a certain integral cost, the minimization of an absolute-work-like cost is supported by experimental observations. Such types of optimality criteria may be applied to a large range of biological movements.

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Psychomotor retardation is a central feature of depression which includes motor and cognitive impairments. Effective management may be useful to improve the classification of depressive subtypes and treatment selection, as well as prediction of outcome in patients with depression. The aim of this paper was to review the current status of knowledge regarding psychomotor retardation in depression, in order to clarify its role in the diagnostic management of mood disorders. Retardation modifies all the actions of the individual, including motility, mental activity, and speech. Objective assessments can highlight the diagnostic importance of psychomotor retardation, especially in melancholic and bipolar depression. Psychomotor retardation is also related to depression severity and therapeutic change and could be considered a good criterion for the prediction of therapeutic effect. The neurobiological process underlying the inhibition of activity includes functional deficits in the prefrontal cortex and abnormalities in dopamine neurotransmission. Future investigations of psychomotor retardation should help improve the understanding of the pathophysiological mechanisms underlying mood disorders and contribute to improving their therapeutic management.

Recent kinematic results, combined with model simulations, have provided support for the hypothesis that the human brain shapes motor patterns that use gravity effects to minimize muscle effort. Because many different muscular activation patterns can give rise to the same trajectory, here, we specifically investigate gravity-related movement properties by analyzing muscular activation patterns during single-degree-of-freedom arm movements in various directions. Using a well-known decomposition method of tonic and phasic electromyographic activities, we demonstrate that phasic electromyograms (EMGs) present systematic negative phases. This negativity reveals the optimal motor plan’s neural signature, where the motor system harvests the mechanical effects of gravity to accelerate downward and decelerate upward movements, thereby saving muscle effort. We compare experimental findings in humans to monkeys, generalizing the Effort-optimization strategy across species.

We permanently deal with gravity force. Experimental evidences revealed that moving against gravity strongly differs from moving along the gravity vector. This directional asymmetry has been attributed to an optimal planning process that optimizes gravity force effects to minimize energy. Yet, only few studies have considered the case of vertical movements in the context of optimal control. What kind of cost is better suited to explain kinematic patterns in the vertical plane? Here, we aimed to understand further how the central nervous system (CNS) plans and controls vertical arm movements. Our reasoning was the following: if the CNS optimizes gravity mechanical effects on the moving limbs, kinematic patterns should change according to the direction and the magnitude of the gravity torque being encountered in the motion. Ten subjects carried out single-joint movements, i.e., rotation around the shoulder (whole arm), elbow (forearm), and wrist (hand) joints, in the vertical plane. Joint kinematics were analyzed and compared with various theoretical optimal model predictions (minimum absolute work-jerk, jerk, torque change, and variance). We found both direction-dependent and joint-dependent variations in several kinematic parameters. Notably, directional asymmetries decreased according to a proximodistal gradient. Numerical simulations revealed that our experimental findings could be attributed to an optimal motor planning (minimum absolute work-jerk) that integrates the direction and the magnitude of gravity torque and minimizes the absolute work of forces (energy-related cost) around each joint. Present results support the general idea that the CNS implements optimal solutions according to the dynamic context of the action.

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