Poststroke consequences upon optimization properties of postural sway during upright stance: a cross-sectional study

BACKGROUND

The understanding of human postural control has advanced with the introduction of optimization process modeling. These models, however, only provide control parameters, rather than analytical descriptors of optimization processes. Here, we use a newly developed direct (pattern) search algorithm to investigate changes in postural optimization process in poststroke individuals.

OBJECTIVE

This cross-sectional study investigated the optimization properties of postural stability during upright standing in poststroke individuals.

METHODS

Twenty-nine poststroke and 15 healthy age-matched individuals underwent posturography with a force platform while standing for 60 s for acquisition of center-of-pressure data. Poststroke individuals were grouped depending on their weight-bearing (WB) pattern and their balance capability assessed through Berg Balance Scale (BBS). The optimization properties of postural stability were computed assuming the minimization of postural sway as cost function.

RESULTS

The asymmetric WB poststroke group showed larger convergence rate toward the local minimum of postural sway than the symmetric WB group. Additionally, the low-balance capability group exhibited smaller values for averaged local minima and global minimum of postural sway coordinates compared with high-balance capability group. Significant correlations were found for BBS and the local minima and global minimum (Pearson's r ranged 0.378-0.424, P < 0.05).

CONCLUSIONS

In summary, the optimization properties describing postural dynamic stability, steadiness, and global reference are altered in poststroke individuals with asymmetric WB pattern and low-balance capability.

Moving, fast and slow: behavioural insights into bradykinesia in Parkinson's disease

The debilitating symptoms of Parkinson's disease, including the hallmark slowness of movement, termed bradykinesia, were described more than 100 years ago. Despite significant advances in elucidating the genetic, molecular and neurobiological changes in Parkinson's disease, it remains conceptually unclear exactly why patients with Parkinson's disease move slowly. To address this, we summarize behavioural observations of movement slowness in Parkinson's disease and discuss these findings in a behavioural framework of optimal control. In this framework, agents optimize the time it takes to gather and harvest rewards by adapting their movement vigour according to the reward that is at stake and the effort that needs to be expended. Thus, slow movements can be favourable when the reward is deemed unappealing or the movement very costly. While reduced reward sensitivity, which makes patients less inclined to work for reward, has been reported in Parkinson's disease, this appears to be related mainly to motivational deficits (apathy) rather than bradykinesia. Increased effort sensitivity has been proposed to underlie movement slowness in Parkinson's disease. However, careful behavioural observations of bradykinesia are inconsistent with abnormal computations of effort costs due to accuracy constraints or movement energetic expenditure. These inconsistencies can be resolved when considering that a general disability to switch between stable and dynamic movement states can contribute to an abnormal composite effort cost related to movement in Parkinson's disease. This can account for paradoxical observations such as the abnormally slow relaxation of isometric contractions or difficulties in halting a movement in Parkinson's disease, both of which increase movement energy expenditure. A sound understanding of the abnormal behavioural computations mediating motor impairment in Parkinson's disease will be vital for linking them to their underlying neural dynamics in distributed brain networks and for grounding future experimental studies in well-defined behavioural frameworks.

Do muscle synergies reduce the dimensionality of behavior?

The muscle synergy hypothesis is an archetype of the notion of Dimensionality Reduction (DR) occurring in the central nervous system due to modular organization. Toward validating this hypothesis, it is important to understand if muscle synergies can reduce the state-space dimensionality while maintaining task control. In this paper we present a scheme for investigating this reduction utilizing the temporal muscle synergy formulation. Our approach is based on the observation that constraining the control input to a weighted combination of temporal muscle synergies also constrains the dynamic behavior of a system in a trajectory-specific manner. We compute this constrained reformulation of system dynamics and then use the method of system balancing for quantifying the DR; we term this approach as Trajectory Specific Dimensionality Analysis (TSDA). We then investigate the consequence of minimization of the dimensionality for a given task. These methods are tested in simulations on a linear (tethered mass) and a non-linear (compliant kinematic chain) system. Dimensionality of various reaching trajectories is compared when using idealized temporal synergies. We show that as a consequence of this Minimum Dimensional Control (MDC) model, smooth straight-line Cartesian trajectories with bell-shaped velocity profiles emerged as the optima for the reaching task. We also investigated the effect on dimensionality due to adding via-points to a trajectory. The results indicate that a trajectory and synergy basis specific DR of behavior results from muscle synergy control. The implications of these results for the synergy hypothesis, optimal motor control, motor development, and robotics are discussed.

Optimal motor control may mask sensory dynamics

Properties of neural controllers for closed-loop sensorimotor behavior can be inferred with system identification. Under the standard paradigm, the closed-loop system is perturbed (input), measurements are taken (output), and the relationship between input and output reveals features of the system under study. Here we show that under common assumptions made about such systems (e.g. the system implements optimal control with a penalty on mechanical, but not sensory, states) important aspects of the neural controller (its zeros mask the modes of the sensors) remain hidden from standard system identification techniques. Only by perturbing or measuring the closed-loop system "between" the sensor and the control can these features be exposed with closed-loop system identification methods; while uncommon, there exist noninvasive techniques such as galvanic vestibular stimulation that perturb between sensor and controller in this way.