Age-related changes in the control of finger force vectors

Optimization and variability of motor behavior in multifinger tasks: what variables does the brain use?

The neural control of movement has been described using different sets of elemental variables. Two possible sets of elemental variables have been suggested for finger pressing tasks: the forces of individual fingers and the finger commands (also called finger modes or central commands). The authors analyzed which of the 2 sets of the elemental variables is more likely used in the optimization of the finger force sharing and which set is used for the stabilization of performance. They used two recently developed techniques-the analytical inverse optimization (ANIO) and the uncontrolled manifold (UCM) analysis-to evaluate each set of elemental variables with respect to both aspects of performance. The results of the UCM analysis favored the finger commands as the elemental variables used for performance stabilization, while ANIO worked equally well on both sets of elemental variables. A simple scheme is suggested as to how the CNS could optimize a cost function dependent on the finger forces, but for the sake of facilitation of the feed forward control it substitutes the original cost function by a cost function, which is convenient to optimize in the space of finger commands.

Improving finger coordination in young and elderly persons

We studied the effects of a single practice session of a variable task with subject-specific adjustments of task difficulty (instability) on indices of multi-finger coordination in young and elderly persons. The main hypothesis was that practicing such a task would lead to contrasting changes in the amounts of two components of variance estimated across repetitive trials within the uncontrolled manifold (UCM) hypothesis: V UCM that had no effect on total force and V ORT that affected total force. In addition, we also expected to see strong transfer effects to a different task. A variable task with graded instability was designed to encourage use of variable solutions during the accurate production of total force with two fingers. The subjects practiced with the index and middle fingers pressing on individual force sensors. Overall, the older subjects showed lower indices of performance and higher indices of both V UCM and V ORT. After about 1 h of practice, both groups showed an increase in the index of involuntary force production by non-task fingers (enslaving). Both groups improved the indices of performance. The two variance indices showed opposite effects of practice: V ORT dropped with practice, while V UCM increased leading to an increase in the total amount of variance in the space of commands to fingers and in the index of force-stabilizing synergy. Performance in a simpler, non-practiced task improved, but there was no transfer of the changes in the structure of variance. Specifically, both variance components, V ORT and V UCM, dropped in the non-practiced task. The results show that the neural system responsible for synergies stabilizing important features of performance is highly adaptable to practice of tasks designed to encourage use of variable solutions. We view the results as highly promising for future use in populations with impaired coordination characterized by low synergy indices.

Movements that are both variable and optimal

This brief review addresses two major aspects of the neural control of multi-element systems. First, the principle of abundance suggests that the central nervous system unites elements into synergies (co-variation of elemental variables across trials quantified within the framework of the uncontrolled manifold hypothesis) that stabilize important performance variables. Second, a novel method, analytical inverse optimization, has been introduced to compute cost functions that define averaged across trials involvement of individual elements over a range of values of task-specific performance variables. The two aspects reflect two features of motor coordination: (1) using variable solutions that allow performing secondary tasks and stabilizing performance variables; and (2) selecting combinations of elemental variables that follow an optimization principle. We suggest that the conflict between the two approaches (a single solution vs. families of solutions) is apparent, not real. Natural motor variability may be due to using the same cost function across slightly different initial states; on the other hand, there may be variability in the cost function itself leading to variable solutions that are all optimal with respect to slightly different cost functions. The analysis of motor synergies has revealed specific changes associated with atypical development, healthy aging, neurological disorders, and practice. These have allowed formulating hypotheses on the neurophysiological mechanisms involved in the synergic control of actions.

Control of fingertip forces in young and older adults pressing against fixed low- and high-friction surfaces

Mobile computing devices (e.g., smartphones and tablets) that have low-friction surfaces require well-directed fingertip forces of sufficient and precise magnitudes for proper use. Although general impairments in manual dexterity are well-documented in older adults, it is unclear how these sensorimotor impairments influence the ability of older adults to dexterously manipulate fixed, low-friction surfaces in particular. 21 young and 18 older (65+ yrs) adults produced maximal voluntary contractions (MVCs) and steady submaximal forces (2.5 and 10% MVC) with the fingertip of the index finger. A Teflon covered custom-molded splint was placed on the fingertip. A three-axis force sensor was covered with either Teflon or sandpaper to create low- and high-friction surfaces, respectively. Maximal downward forces (F_z) were similar (p = .135) for young and older adults, and decreased by 15% (p<.001) while pressing on Teflon compared to sandpaper. Fluctuations in F_z during the submaximal force-matching tasks were 2.45× greater (p<.001) for older adults than in young adults, and reached a maximum when older adults pressed against the Teflon surface while receiving visual feedback. These age-associated changes in motor performance are explained, in part, by altered muscle activity from three hand muscles and out-of-plane forces. Quantifying the ability to produce steady fingertip forces against low-friction surfaces may be a better indicator of impairment and disability than the current practice of evaluating maximal forces with pinch meters. These age-associated impairments in dexterity while interacting with low-friction surfaces may limit the use of the current generation of computing interfaces by older adults.

Age-related differences in finger force control are characterized by reduced force production

It has been repeatedly shown that precise finger force control declines with age. The tasks and evaluation parameters used to reveal age-related differences vary between studies. In order to examine effects of task characteristics, young adults (18-25 years) and late middle-aged adults (55-65 years) performed precision grip tasks with varying speed and force requirements. Different outcome variables were used to evaluate age-related differences. Age-related differences were confirmed for performance accuracy (TWR) and variability (relative root mean square error, rRMSE). The task characteristics, however, influenced accuracy and variability in both age groups: Force modulation performance at higher speed was poorer than at lower speed and at fixed force levels than at force levels adjusted to the individual maximum forces. This effect tended to be stronger for older participants for the rRMSE. A curve fit confirmed the age-related differences for both spatial force tracking parameters (amplitude and intercept) and for one temporal parameter (phase shift), but not for the temporal parameter frequency. Additionally, matching the timing parameters of the sine wave seemed to be more important than matching the spatial parameters in both young adults and late middle-aged adults. However, the effect was stronger for the group of late middle-aged, even though maximum voluntary contraction was not significantly different between groups. Our data indicate that changes in the processing of fine motor control tasks with increasing age are caused by difficulties of late middle-aged adults to produce a predefined amount of force in a short time.

Handling objects in old age: forces and moments acting on the object

We measured the external moments and digit-tip force directions acting on a freely moveable object while it was grasped and manipulated by old (OA) and young (YA) adults. Participants performed a grasp and lift task and a precision orientation (key-slot) task with a precision (thumb-finger) grip. During the grasp-lift task the OA group misaligned their thumb and finger contacts and produced greater grip force, greater external moments on the object around its roll axis, and oriented force vectors differently compared with the YA group. During the key-slot task, the OA group was more variable in digit-tip force directions and performed the key-slot task more slowly. With practice the OA group aligned their digits, reduced their grip force, and minimized external moments on the object, clearly demonstrating that the nervous system monitored and actively manipulated one or more variables related to object tilt. This was true even for the grip-lift task, a task for which no instructions regarding object orientation were given and which could tolerate modest amounts of object tilt without interfering with task goals. Although the OA group performed the key-slot task faster with experience, they remained slower than the YA group. We conclude that with old age comes a reduced ability to control the forces and moments applied to objects during precision grasp and manipulation. This may contribute to the ubiquitous slowing and deteriorating manual dexterity in healthy aging.

Age effects on rotational hand action

We investigated age-related differences in finger coordination during rotational hand actions. Two hypotheses based on earlier studies were tested: higher safety margins and lower synergy indices were expected in the elderly. Young and elderly subjects held a handle instrumented with five six-component force sensors and performed discrete accurate pronation and supination movements. The weight of the system was counterbalanced with another load. Indices of synergies stabilizing salient performance variables, such as total normal force, total tangential force, moments produced by these forces, and total moment of force were computed at two levels of a hypothetical control hierarchy, at the virtual finger-thumb level and at the individual finger level. At each level, synergy indices reflected the normalized difference between the sum of the variances of elemental variables and variance of their combined output, both computed at comparable phases over repetitive trials. The elderly group performed the task slower and showed lower safety margins for the thumb during the rotation phase. Overall, the synergy indices were not lower in the elderly group. In several cases, these indices were significantly higher in the elderly than in the younger participants. Hence, both main hypotheses have been falsified. We interpret the unexpectedly low safety margins in the elderly as resulting from several factors such as increased force variability, impaired feed-forward control, and the fact that there was no danger of dropping the object. Our results suggest that in some natural tasks, such as the one used in this study, healthy elderly persons show no impairment, as compared to younger persons, in their ability to organize digits into synergies stabilizing salient performance variables.

Finger coordination under artificial changes in finger strength feedback: a study using analytical inverse optimization

A recently developed method of analytical inverse optimization (ANIO) was used to compute cost functions based on sets of experimental observations in 4-finger pressing tasks with accurate total force and moment production. In different series, feedback on total force and moment was provided using the index finger force at its value, doubled, or halved. Finger force data across different force-moment combinations formed a plane. This allowed reconstructing cost functions as 2nd-order polynomials with linear terms. Changes in the coefficients of the cost function across the 3 series allowed the authors to offer a biomechanical interpretation related to constraints on finger forces with different lever arms. ANIO allows the authors to describe preferred regions within the space of solutions for redundant tasks in terms of cost functions.

Manipulation of a fragile object by elderly individuals

We investigated strategies of healthy elderly participants (74-84 years old) during prehension and transport of an object with varying degrees of fragility. Fragility was specified as the maximal normal force that the object could withstand without collapsing. Specifically, kinetic and kinematic variables as well as and force covariation indices were quantified and compared to those shown by young healthy persons (19-28 years old). We tested three hypotheses related to age-related changes in two safety margins (slip safety margin and crush safety margin) and indices of force covariation. Compared to young controls, elderly individuals exhibited a decrease in object acceleration and an increase in movement time, an increase in grip force production, a decrease in the correlation between grip and load forces, an overall decrease in indices of multi-digit synergies, and lower safety margin indices computed with respect to both dropping and crushing the object. Elderly participants preferred to be at a relatively lower risk of crushing the object even if this led to a higher risk of dropping it. Both groups showed an increase in the index of synergy stabilizing total normal force produced by the four fingers with increased fragility of the object. Age-related changes are viewed as a direct result of physiological changes due to aging, not adaptation to object fragility. Such changes in overall characteristics of prehension likely reflect diminished synergic control by the central nervous system of finger forces with aging. The findings corroborate an earlier hypothesis on an age-related shift from synergic to element-based control.

Age-related changes in optimality and motor variability: an example of multifinger redundant tasks

We used two methods, analytical inverse optimization (ANIO) and uncontrolled manifold (UCM) analysis of synergies, to explore age-related changes in finger coordination during accurate force and moment of force production tasks. The two methods address two aspects of the control of redundant systems: Finding an optimal solution (an optimal sharing pattern) and using variable solutions across trials (covarying finger forces) that are equally able to solve the task. Young and elderly subjects produced accurate combinations of total force and moment by pressing with the four fingers of the dominant hand on individual force sensors. In session-1, single trials covered a broad range of force-moment combinations. Principal component (PC) analysis showed that the first two PCs explained about 90% and 75% of finger force variance for the young and elderly groups, respectively. The magnitudes of the loading coefficients in the PCs suggested that the young subjects used mechanical advantage to produce moment while elderly subjects did not (confirmed by analysis of moments produced by individual digits). A co-contraction index was computed reflecting the magnitude of moment produced by fingers acting against the required direction of the total moment. This index was significantly higher in the young group. The ANIO approach yielded a quadratic cost function with linear terms. In the elderly group, the contribution of the forces produced by the middle and ring fingers to the cost function value was much smaller than in the young group. The angle between the plane of experimental observations and the plane of optimal solutions (D-angle), was very small (about 1.5°) in the young group and significantly larger (about 5°) in the elderly group. In session-2, four force-moment combinations were used with multiple trials at each. Covariation among finger forces (multifinger synergies) stabilizing total force, total moment, and both was seen in both groups with larger synergy indices in the young group. Multiple regression analysis has shown that, at higher force magnitudes, the synergy indices defined with the UCM method were significantly related to the percent of variance accounted by the first two PCs and to the D-angle computed using the ANIO method. We interpret the results as pointing at a transition with age from synergic control to element-based control (back-to-elements hypothesis). Optimization and analysis of synergies are complementary approaches that focus on two aspects of multidigit coordination, sharing and covariation, respectively.