The Hand: Shall We Ever Understand How it Works?

Motor Control, 2015, 19, 108 -126 http://dx.doi.org/10.1123/mc.2014-0025 © 2015 Human Kinetics, Inc.

TARGET ARTICLE

The Hand: Shall We Ever Understand How It Works? Mark L. Latash The target article presents a review of the neural control of the human hand. The review emphasizes the physical approach to motor control. It focuses on such concepts as equilibrium-point control, control with referent body configurations, uncontrolled manifold hypothesis, principle of abundance, hierarchical control, multidigit synergies, and anticipatory synergy adjustments. Changes in aspects of the hand neural control with age and neurological disorder are discussed. The target article is followed by six commentaries written by Alexander Aruin, Kelly Cole, Monica Perez, Robert Sainburg, Marco Sanello, and Wei Zhang. Keywords: synergy, uncontrolled manifold hypothesis, equilibrium-point hypothesis, prehension, anticipatory control, hierarchical control

The Hand: A Model of the Neural Control of Biological Motion The human hand is simultaneously a very attractive and a very challenging object for motor control researchers. From the point of view of mechanics, the design of the hand combines both serial and parallel chains and, therefore, can be used to study the famous problem of motor redundancy at both kinetic and kinematic levels (Bernstein, 1967; Zatsiorsky & Latash, 2004, 2008). From the point of view of muscle anatomy, the hand possesses a very complex design with digitspecific intrinsic and multidigit extrinsic muscles as well as the unique extensor mechanism. From the point of view of brain neurophysiology, the hand has large brain representations, both sensory and motor, which make it highly attractive for the study of brain projections and their plastic changes with training, disorder, and recovery. From the point of view of motor disorders, the hand function is notoriously easy to lose and notoriously hard to restore. And, on top of all these features, the hand possesses unmatched dexterity, which makes all robotic grippers look clumsy and inept. The purpose of this paper is to revisit the current theoretical and experimental approaches to motor control in their application to the hand function. The title of the paper may be interpreted differently depending on the meaning attached to the The author is with the Dept. of Kinesiology, Pennsylvania State University, University Park, PA. Address author correspondence to Mark L. Latash at [email protected].

108

Neural Control of the Hand 109

Downloaded by Univ of Massachusetts Lib on 09/16/16, Volume 19, Article Number 2

word understand. For example, for researchers in the field of robotics, understand may mean being able to build a robotic hand that can perform the same range of tasks as the human hand. For the author of this paper, the purpose of motor control (understanding) means discovering laws of nature that form the basis for the hand function. This paper will not consider such broadly used notions as internal models (Kawato, 1999; Shadmehr & Wise, 2005) in application to the hand function because these notions are not physical (physiological) but computational.

Referent Configuration: A Factor Driving Hand Action There is only one coherent physical (physiological) theory that tries to identify laws of physics that account for the neural control of biological movement. Several names have been used for this theory, from the equilibrium-point hypothesis and lambda-model to the referent configuration (RC) hypothesis and threshold control theory. Here, the theory will be addressed as the RC hypothesis. Since the theory is described in detail in recent reviews (Feldman, 2009; Latash, 2010) only a brief account of the main axioms and features of this theory is presented here. One of the main ideas of the RC hypothesis is that shifting a referent frame may be an effective way to induce forces on and motion of a material object. If one assumes that the central nervous system (CNS) is able to shift referent coordinates for the body and its parts, such shifts result in discrepancies between the actual configuration (AC) and RC that lead to changes in muscle activation trying to minimize the (AC–RC) discrepancy. If motion is possible, the system moves to the RC. If motion is blocked, nonzero forces are produced. The RC hypothesis associates the neurophysiological variable used to shift RCs with the difference between the potential on the neuronal membrane and the threshold for action potential generation. This variable can be shifted by subthreshold depolarization of the membrane and/or by changes in the threshold potential. This scheme can be applied to various neuronal pools, from alpha-motoneuronal pools for the control of individual muscles to unknown pools related to the control of multimuscle natural actions. Consider now one of the common hand actions, the precision pinch grip performed by the thumb and the index finger (Figure 1a). Assuming that the object is vertical and there is a certain combination of weight and external torque, most commonly the hand action is described with mechanical variables such as normal (grip) force, load-resisting (tangential) force, and moment of force counterbalancing the external torque. These variables are present, however, under the specific loading conditions. If the object suddenly disappeared, the digits would move toward each other, while the hand would move upwards and rotate (Latash et al., 2010). In other words, the hand and digits would move toward their referent coordinates that comprise the RC for this task. Hence, action of the central nervous system can be adequately described not in terms of forces but in terms of RCs. At the level of task variables, RC has been associated with referent grip aperture that defines the grip force (an internal force with zero resultant), referent vertical coordinate of the hand that defines the load-resisting force, and referent orientation of the hand with respect to the vertical that defines the moment of MC Vol. 19, No. 2, 2015

110 Latash

force. Alternatively, grip force production has been described as a combination of relatively independent actions of the opposing digits (Smeets & Brenner, 1999, 2001). Implementation of the static prehensile action, similarly to any biological action, involves a sequence of redundant transformations (see more in the next section). For example, even the minimalistic set of digits illustrated in Figure 1a generates a redundant set of mechanical variables. The equations of statics for the two digits in the plane are:


FTn + FIn = 0 FTt + FIt + L = 0 n T T

n

t T T

(1)

t I I

F d + FI d I + F r + F r + TQ = 0     n

t

M M where L is external load, TQ is external torque, d and r are moment arms for the tangential and normal forces respectively, the superscripts refer to the normal (n) and tangential (t) forces (F) and the corresponding moments of force (M), and the subscripts refer to the thumb (T) and index finger (I). The equations (1) have an infinite number of solutions, which means that the system of two digits is redundant at the task level. Analysis of the hand function does not have to stop at the level of digit action. For a given digit force, there is an infinite number of muscle force combinations, for example differing by the amount of cocontraction of the corresponding agonistantagonist pairs. For a given muscle activation level, there is an infinite number of recruitment patterns of motoneurons. In other words, implementation of any action is associated with a chain of few to many transformations. Each of the transformation presents a problem of redundancy (reviewed in Latash 2010).

Figure 1 — A: An illustration of the precision grip with two digits, T = the thumb and I = the index finger. The object can be characterized with a load (L) and external torque (TQ) Each digit produces two force components (in the plane), normal (Fn) and tangential (Ft). The points of the normal force application can also shift (not illustrated). B: The five-digit prismatic grasp can be viewed as built on a two-level synergy. First, the task is shared between the thumb and the virtual finger (VF). Second, the VF action is shared among the four fingers, I = index, M = middle, R = ring, and L = little.

MC Vol. 19, No. 2, 2015



The Principle of Abundance: The Basis for Multidigit Synergies Traditionally, motor redundancy has been viewed as a problem that the CNS has to solve (eliminate the redundant degrees-of-freedom, Bernstein, 1967), for example with methods of optimization (Prilutsky & Zatsiorsky 2002; see also later). Recently, this problem has been recast as a solution of abundance (Gelfand & Latash, 1998; Latash, 2012). Within the principle of abundance, no degrees of freedom (DOFs) are eliminated but the apparently redundant sets of DOFs are organized in a task-specific way to ensure dynamic stability of important features of performance. Under dynamic stability, we imply an ability of the system to return to a trajectory in cases of small transient perturbations and variations in the initial state. Note that dynamic stability is a crucial feature of all natural actions, which are typically performed in poorly predictable, varying external force fields and starting from varying initial conditions (including the initial state of the body). There are two main methods to study stability. First, to quantify system’s reaction to perturbations applied in the course of a movement. Second, to observe natural variability of the system trajectories across repetitive trials. The latter approach has formed the basis for a computational method associated with the uncontrolled manifold (UCM) hypothesis (Scholz & Schöner, 1999). According to the UCM hypothesis, action of the neural controller is organized in such a way that different directions in the space of elemental variables (those produced by a redundant set of elements at the selected level of analysis) show different stability properties that reflect the task and/or intention of the moving person. If a person’s action is associated with stabilization of a particular performance variable, a particular variance structure can be observed across repetitive trials: The amount of variance per DOF parallel to the space that corresponds to no changes in that performance variable (the UCM for that variable) is expected to be larger compared with the amount of variance orthogonal to the UCM (VUCM > VORT). This analysis is typically performed in a linear approximation, and the UCM is approximated with the null-space of the Jacobian matrix (J) that maps infinitesimal changes in the elemental variables on changes in the performance variable. Application of this method to multidigit action is complicated by the fact that human digits are not independent force generators. When one moves a finger, other fingers of the hand move. When one presses with a finger, other fingers press unintentionally (Kilbreath & Gandevia, 1994; Li et al., 1998; Lang & Schieber, 2004; Kapur et al., 2010). This covariation of finger action, addressed as enslaving (Zatsiorsky et al., 2000), leads to nonspherical distributions of variance in the finger force space even if the subject is not trying to perform any specific task. By pure chance, such an elliptical cloud of data points across trials may be oriented parallel or orthogonal to the UCM computed for a particular performance variable leading to a false conclusion about this variable being or not being stabilized. To avoid this problem, studies of multifinger pressing used hypothetical variables (finger modes) to analyze the structure of variance (Latash et al., 2001; Danion et al., 2003). Unfortunately, enslaving shows relatively consistent, regular patterns for pressing forces only. The patterns are more complex for abduction-adduction forces (Pataky et al., 2007). Comprehensive patterns of enslaving that would link

MC Vol. 19, No. 2, 2015


112 Latash

all six components of the digit force/moment vector across digits are unknown. This forces the researchers to perform analysis of digit coordination during prehensile tasks using digit forces and moments of force. The method of analysis offered by the UCM hypothesis allows quantifying multidigit synergies across tasks, conditions, and populations. In this context, synergy is defined as a task-specific covariation of elemental variables (across trials) stabilizing a specific performance variable (reviewed in Latash et al., 2007). Multidigit synergies stabilizing multifinger pressing force, resultant normal force, resultant tangential force, and resultant moment of force have been documented. Indices of synergies (for example, the relative amount of VUCM in total variance) are sensitive to such factors as fatigue, practice, healthy aging, and neurological disorder (reviewed in Latash, 2008, 2010; also see later). The direct relation of the notion of synergy to dynamic stability leads to specific patterns of reaction of multielement systems to external perturbations. By definition of stability, a perturbation is expected to lead to larger deviations of the system along the direction of lower stability, that is, within the UCM. This refers to natural reactions of the system to external perturbations assuming no corrective action at the task level, but may also be true for quick corrective reactions. The latter statement sounds counterintuitive because, by definition, any motion within the UCM is ineffective in correcting a deviation of the corresponding performance variable produced by the perturbation. Nevertheless, several experiments have shown that most of the corrective action to unexpected perturbation takes place within the UCM, thus representing self-motion of the system (Mattos et al., 2011, 2013). Another consequence of the direction-dependent stability in a redundant space of elemental variables is that a transient perturbation is expected to demonstrate equifinality in task-related variables but not in the space of elemental variables. Equifinality is a property of a system to attain the same final state despite possible transient perturbations. It is a natural consequence of control with RCs: Indeed, final equilibrium state of a system depends on its referent state and final external force field and is not expected to depend on transient changes in external forces. Both equifinality and its violations were reported in earlier studies (Kelso and Holt, 1980; Schmidt & McGown, 1980; Latash & Gottlieb, 1990; Lackner & DiZio, 1994; Dizio & Lackner, 1995; Hinder & Milner, 2003) and discussed within the framework of the RC hypothesis (Feldman & Latash, 2005). Recent experiments with multifinger accurate force production tasks used transient short-lasting perturbations of one of the fingers (Wilhelm et al., 2013). The results showed that the final force level and its variance across trials were unaffected by the perturbation confirming equifinality at the task level. In contrast, variance in the finger force space (and in the mode space) was high and structured in a way VUCM > VORT demonstrating that about the same total force values were achieved with variable means in the space of finger forces (modes). This is a direct confirmation that a multielement, redundant system shows task-specific differences in stability in different directions of its state space.

Hierarchical Control: Relations Among the Levels Prehensile hand actions have been traditionally viewed as based on a two-level hierarchy. At the upper level of the hierarchy, the task is shared between the thumb MC Vol. 19, No. 2, 2015



and the opposing set of fingers (the virtual finger, VF, Arbib et al. 1985). At the lower level, VF action is shared among the actual fingers. So, there is one more level in addition to the three levels described for the precision grip (Figure 1b). Hierarchical schemes present certain nontrivial features and challenges. In particular, there is an inherent trade-off between synergies at the two levels of a hierarchy. Imagine a very simple task of producing a constant sum of two variables (X + Y = C) at the upper level of a hierarchy, while each of the variables is the sum of two other variables at the lower level (X = x1 + x2; Y = y1 + y2). A strong synergy at the upper level leads to an ellipse of data points on the XY plane elongated along the UCM for this task (the dashed line in Figure 2a). Note that variance of X (and Y) is defined by the projection of the long axis of the ellipse onto the corresponding axis: VX = VUCM,UP·cos(45°). At the lower level, variance of X (and Y) is VORT,LOW by definition (Figure 2b). This means that to have a synergy (VUCM > VORT) at the lower level, VUCM,LOW has to be larger than VUCM,UP·cos(45°). While this is not impossible, having synergies at both levels becomes nontrivial. A series of studies of two-hand, multifinger pressing tasks have shown that strong between-hand synergies in such tasks are commonly accompanied by complete lack of synergies at the within-a-hand level (Gorniak et al. 2007a,b). During prehensile tasks, some variables show strong synergies at the upper level (the task is shared between the thumb and VF) and no synergies at the lower level (VF action shared among the fingers), while other variables show synergies at both levels (Gorniak et al., 2009). Analysis of synergies at different levels of a hierarchy becomes potentially a useful tool for understanding stability of what variables is perceived by the actor’s CNS as crucial for success. The hierarchical scheme means that an elemental variable at one level of analysis may be a stabilized performance variable at a different level. For example, analysis of multimuscle synergies stabilizing mechanical variables produced by sets of muscles has frequently used the idea of muscle modes assuming that individual muscles are united into groups (M-modes), which are recruited with different gains is individual trials (Krishnamoorthy et al., 2003). The number of M-modes has been expected to be much lower than the number of involved muscles; M-modes (addressed as “synergies” in some studies, d’Avella et al., 2003; Ivanenko et al., 2004, 2005; Ting & Macpherson, 2005) have been identified using matrix factorization techniques such as principal component analysis, factor analysis, nonnegative matrix factorization, etc. applied to sets of indices of activation of individual muscles. The composition of individual M-modes is typically assumed to be stable (unchanged) within the range of studied tasks. Several studies have shown, however, that changing stability conditions for the task may lead to a rearrangement of M-modes resulting in different muscle combinations comprising each of the M-modes (Asaka et al., 2008; Danna-dos-Santos, Degani, & Latash, 2008). These results suggest that M-modes are not fixed patterns of muscle involvement but synergies organized in a task-specific way that may show substantial variance organized to stabilize a particular performance variable produced by each M-mode. Individual M-mode composition may reflect a particular change in referent body configuration, a primitive (cf. Bizzi & Cheung, 2013), which may be used to construct complex, task-specific changes in body RC (Robert et al., 2008). Indeed, the composition of the most commonly observed M-modes in studies of wholeMC Vol. 19, No. 2, 2015


114 Latash

Figure 2 — An illustration of a task requiring accurate production of the sum of two variables: X + Y = C. A: If a synergy stabilizes (X+Y), data distributions across many trials are expected to form an ellipse elongated along the UCM (dashed, slanted line). Variance in each of the two variables (e.g., VX) is defined by variance along the UCM (VUCM,UP). B: If each of the two variables represents the sum of two other variables, e.g., X = x1 + x2, VX corresponds to variance orthogonal to the UCM at that level (VORT,LO).

body movements in the anterior-posterior directions suggests rather direct relations to kinematic patterns observed for eigenmovement (Alexandrov et al., 2001) with the addition of cocontraction patterns (parallel changes in the activation level of agonist-antagonist muscle pairs). Eigenmovements are movements in the space of rotations of the main leg joint corresponding to proportional joint involvement along directions where movement depends only on the torque vector along the same direction. Two of the eignmovements produce joint rotations very similar to the ankle strategy and hip strategy (Horak & Nashner, 1986). So far, no studies of synergies in the muscle activation space have been performed for multidigit actions.

Feedforward Control of the Hand: Forces and Synergies Feedforward control is essential for everyday movements. In fact, all movements are controlled in a feedforward manner, that is, neural signals for movement production are generated before the movement outcome is known. Given that the outcome cannot be predicted perfectly because of the unavoidable variability and poor predictability of external forces, any movement initiation is feedforward. Certainly, in the course of movement execution, corrections based on sensory signals are common. In the field of motor control, however, feedforward control typically refers to motor adjustments in preparation to a movement, adjustments that are not explicit parts of the planned action or even necessary for the action. A typical example is anticipatory postural adjustments (APAs, Massion, 1992) seen as changes in activation levels of apparently postural muscles in preparation to a quick action or a predictable perturbation. There are similar feedforward adjustments seen during hand actions. Arguably, the most frequently studied phenomenon is the adjustments of grip force to loadresisting force during actions with a hand-held object (Forssberg et al., 1992; MC Vol. 19, No. 2, 2015



Flanagan & Wing, 1993; Flanagan & Tresilian, 1994; Jaric et al., 2006). Grip force, by definition, is an internal force with a zero resultant. Hence, by itself, grip force does not resist external load and cannot lead to motion of the object. The main function of the grip force is to generate sufficient friction between the digit tips and the object surface. During typical prehensile actions, grip force is higher than the minimal value required to avoid slippage. The extra grip force above that threshold is addressed as the safety margin (Johansson & Westling, 1988). When a person makes an action associated with inertial forces acting on the hand-held object, grip force changes in anticipation of those actions. Another example of feedforward hand action is the drop in the grip force in anticipation of a supporting force provided by the other hand that helps keep the load against gravity (Scholz & Latash, 1998). As described earlier, within the RC hypothesis, changes in grip force result from changes in the referent aperture, which is part of the body RC (Pilon et al., 2007; Latash et al., 2010). Separation of these adjustments from the “focal action” seems artificial. It may be in the minds of the researchers but not in the mind of the actor. Another, relatively novel, example of feedforward control is changes in multielement synergies stabilizing a specific variable in preparation to an action associated with a quick change of that variable. These anticipatory synergy adjustments (ASAs, Olafsdottir et al., 2005) ensure that excessive stability of the performance variable does not interfere negatively with its planned change. ASAs have been observed in multifinger tasks and in postural tasks (Shim et al., 2006; Klous, Mikulic, & Latash, 2011; Krishnan et al., 2012). They can be seen about 200–300 ms before the action initiation, which is about 100–200 ms earlier than the APAs that can be seen in some of the same tasks. Note that only APAs, not ASAs, can be seen in averaged across trials data. ASAs are seen only at the level of covariation of elemental variables across trials. Another important difference between APAs and ASAs is in that the former are specific to direction of the expected perturbation and action (an APA in a “wrong direction” may lead to highly undesirable consequences) while the latter can be used independently of direction of the planned action (Zhou et al., 2013). The commonly observed large body sway of a tennis player who gets ready for a powerful serve may be an example of ASA.

Neurological Disorders: The Effects on Hand Function and Synergies Impairments in the hand function can originate from a variety of sources. For example, healthy aging is associated with changes at the level of hand muscles and their innervation: The loss of muscle mass (sarcopenia) and the processes of denervation and reinnervation of muscle fibers contribute to changes in properties of motor units, muscle force, speed of contraction, and variability of steady force production (reviewed in Grabiner & Enoka, 1995; Cole et al., 1999). On the other hand, there is loss of neurons across many structures within the CNS with age (Henderson et al., 1980; Eisen et al., 1996; Christou, 2009) that leads to a host of consequences, some of which may look counterintuitive. In particular, older adults show lower indices of enslaving that suggest better individualized control of fingers (Shinohara et al., 2003; Kapur, et al. 2010). MC Vol. 19, No. 2, 2015


116 Latash

The idea of a distributed cortical control of the hand (Schieber, 2001) with individual cortical neurons being able to induce activation changes in many different muscles (divergence) and numerous, broadly distributed neurons projecting on the same muscle or effector (convergence) assumes the availability of a hugely redundant set of neurons. When neuronal redundancy decreases with age, a shift toward more focused, somatotopic control scheme may happen resulting in lower enslaving. Note that lower enslaving does not mean better control of the hand across everyday tasks. It has been shown that patterns of enslaving are organized to reduce variance of the total moment of force produced by a set of fingers (Zatsiorsky et al., 2000) thus helping to stabilize rotational action of the hand. It is not surprising, therefore, that lower enslaving in the elderly is associated with worse control of rotational actions as reflected in indices of synergies during both pressing and prehensile tasks (Shim et al., 2004; Olafsdottir et al., 2007). Studies of motor synergies in neurological patients have produced unexpected results. In particular, patients after a unilateral cortical stroke typically show poor control of the contralesional arm. Variance in the joint configuration space is increased dramatically. In contrast, the relative amounts of the two components of variance, VUCM and VORT, remain nearly the same in the impaired arm and the relatively unimpaired, ipsilesional arm (Reisman & Scholz, 2003) suggesting unchanged synergies stabilizing the hand trajectory. In contrast, patients with subcortical disorders such as Parkinson’s disease and olivo-ponto-cerebellar atrophy (a multisystem disorder with a combination of cerebellar and parkinsonian signs) show significantly changed indices of synergies

Figure 3 — An illustration of the index of synergy stabilizing total force in a task to produce constant pressing force with four fingers and then, at a self-selected time (t0), a quick force pulse to a target. Data for patients with Parkinson’s disease (lower, solid line) and healthy Controls (upper, dashed line) are shown. Note the higher values of the synergy index (z-transformed, ΔVZ) in the Controls and the early drop in this index in preparation to action (anticipatory synergy adjustment, ASA). Modified by permission from Park et al. 2012.

MC Vol. 19, No. 2, 2015



in multifinger tasks while the overall performance in such tasks may be relatively unaffected (Park et al., 2012, 2013a,b). Figure 3 illustrates the index of multifinger synergy stabilizing total force during the production of a simple four-finger pressing task by patients at an early-stage Parkinson’s disease (Hoehn-Yahr stage I-II) and by age-matched controls. Over the first portion of the task, the subjects were instructed to produce accurately a certain low level of total force by matching the target line on the screen. Then, they were asked to produce a quick force pulse into a target zone in a self-paced manner. Note that the patients showed significantly lower synergy indices during steady force production, while their early adjustments of the synergy index in preparation to the quick force pulse (ASAs) were delayed and decreased in magnitude. Two more findings are potentially important. First, the patients with symptoms limited to only one side of the body (stage-I Parkinson’s disease) showed similar changes in the indices of force-stabilizing synergies and ASAs in both hands. Second, testing patients off their dopamine-replacement medication (early in the morning, before taking the first dose) showed indices of synergies and ASAs that were further reduced as compared with the values seen one hour later, after the patients took their morning pills (Park et al., 2014). Taken together, these observations suggest that tests of multidigit hand synergies may be a very sensitive (and very easy to perform!) behavioral task reflecting the state of dopamine-sensitive circuits in the brain; the outcome indices may even be sensitive to preclinical states and used as early biomarkers of Parkinson’s disease and, maybe, other subcortical disorders. The contrast between the effects of cortical and subcortical disorders on the overall motor performance and multielement synergies suggests that the subcortical structures may be more directly involved in defining stability of action while its overall characteristics may be defined with significant cortical contribution. Implementation of this general idea may be based on the notion of distributed processing modules (Houk, 2005), which is a development of the notion of brain operators introduced by Bernstein and his colleagues earlier (Bassin et al., 1966; see Latash et al., 2000).

In What Sense is Hand Function Optimal? One of the commonly used approaches to the problem of motor redundancy is based on an idea that specific patterns of involvement of elemental variables are selected to optimize (minimize or maximize) a particular cost function associated with performing the action. This approach suggests that unique, optimal patterns of involvement of elemental variables are selected each time a movement is performed. Starting with the classical experiments of Bernstein on blacksmiths (Bernstein, 1930), it has been shown many times that even the most skilled performers show substantial variability in the trial-to-trial trajectories of elemental variables (reviewed in Newell & Corcos, 1993). Those observations ultimately led to the principle of motor abundance, which seems incompatible with the ideas of optimization: It suggests that families of trajectories are facilitated for each movement. Obviously, only one of those trajectories can be optimal with respect to any given cost function. MC Vol. 19, No. 2, 2015


118 Latash

Figure 4 illustrates a cloud of data points in a very simple task to produce a constant sum of two variables (for example, a constant pressing force with two index fingers): F1 + F2 = C. The cloud is elongated along the solution space (the dashed line), which is the UCM for this task. Only one of the points in the solution space corresponds to a minimum of a cost function (shown with a large dot). Clearly, large variance along the UCM corresponds to a strong synergy stabilizing the sum of the two variables, while it also corresponds to the large violations of the optimization criterion. There has been an attempt to reconcile the principle of abundance with ideas of optimization (Park et al., 2010). According to this approach, an optimization criterion can be used to define average coordinates of the cloud of data points along the solution space (for example, the centers of the clouds can be on the same straight line as in Figure 4), while magnitudes of elemental variables can deviate from the optimal values in individual trials and covary to stabilize the performance variable. One of the problems of optimization is that selection of a candidate cost function has typically been done rather arbitrarily based on common sense and experience of researchers. Only recently, an approach has been developed that allows reconstructing cost functions based on experimental observations. This approach, called analytical inverse optimization (ANIO), has been applied to multifinger force and momentof-force production tasks and resulted in consistent findings across young healthy subjects who demonstrated a close match between the experimental data and optimal data sets computed using the reconstructed cost function (Terekhov et al., 2010; Niu et al., 2012). In contrast, older persons and, particularly, patients with subcortical disorders showed large deviations in the two sets of data (Park et al. 2010, 2013a,b). These findings suggest that young subjects are consistent in following an optimization criterion across a range of tasks while older persons and patients may be shifting from one criterion to another. Everyday human movements, including hand movements, do not seem optimal in any single sense while they are commonly successful in meeting the goals. A range of mechanical, physiological, psychological, and complex cost functions have been explored; many of them resulted in movement patterns that resembled actual biological actions (otherwise, those papers would have never been published) suggesting that human movement do not violate any of those criteria by much. One can call natural movements reasonably sloppy (Latash, 2008). The notion of reasonable sloppiness suggests a different approach to movement optimality. Consider how reasonable sloppiness can be based on control with referent body configurations. Such control can be based on different sets of variables. For example, body-related elemental variables may be organized into smaller sets of higher-order variables. This effectively reduces the dimensionality of RCs. As mentioned earlier, an example is the organization of the multiple leg and trunk muscles into M-modes for the control of whole-body actions while standing. The composition of M-modes suggests that they reflect shifts in a handful of higher-order RCs combined with task-specific gains to perform typical everyday actions (Robert et al., 2008). The idea of a small number of higher-order RCs is close to the ideas of primitives (Kargo et al., 2010; Bizzi & Cheung, 2013) and of template control (Niu et al., 2007). The latter idea has been illustrated recently in a study of digit action on a hand-held horizontal handle with various external loads and torques (Wu, Zatsiorsky, & Latash, 2013). In that study, force vectors produced by the digits preserved their direction but scaled in amplitude across different external loading MC Vol. 19, No. 2, 2015



Figure 4 — An illustration of the task of accurate total force production with two digits. For any force level, data across repetitive trials form an ellipse elongated along the UCM (dashed lines). By definition, only one data point in each cloud is optimal (large, black dots), while other points violate the unknown optimality principle.

conditions and also across time samples during a quick lifting movement of the handle. Such patterns are expected if direction of RC shift is kept constant for each digit within the handle-based referent frame and only scales in a task-specific way. This is an example of using a simple rule to organize control of a multielement action rather than trying to compute optimal patterns of action for each condition. Using such simple rules sometimes leads to clearly suboptimal movement patterns. For example, a quick elbow movement is associated with large inertial forces acting on the hand. To avoid hand flapping, the CNS organizes control to produce simultaneous patterns of muscle activation at both the elbow and wrist joints (Koshland et al., 1991; Latash et al., 1995). If wrist motion is blocked mechanically, activation bursts in muscles acting at the wrist become unnecessary. Nevertheless, they are observed in such conditions as well (Koshland et al., 1991) suggesting that a suboptimal template pattern cannot be replaced quickly with a “more optimal” pattern.

The Brain and the Hand: Do We Know Much or Little? Attempts to link the control of the hand to processes in specific neurophysiological structures have come from three main areas, observations in patients with brain disorders, brain imaging studies, and studies with brain stimulation. Linking MC Vol. 19, No. 2, 2015


120 Latash

these results to the main quest of motor control—search for laws of nature that form the basis of voluntary movements—has not been easy. Partly, this is due to the fact that most such studies have tried to relate stimulation of brain structures and changes in neuronal activation in those structures to muscle activation and mechanical characteristics of corresponding actions. Only a handful of studies have tries to link brain-related variables to control variables that might be used during voluntary movements. There is also a deep philosophical issue that has been in the center of attention of brain scientists for many years: What is the relation between function, in particular motor function, and its brain representation? To cite one of the most important papers by Bernstein (1935): “Localization is not “Where?” but “What?” and “How?” is represented.” There has been substantial progress in analysis of the relations between patterns of activation of brain neurons and voluntary action. Earlier studies tried to link activation of individual neurons to changes in peripheral variables such as muscle activations and forces (Evarts, 1968; Asanuma, 1973). More recently, the idea of neuronal populations encoding salient features of voluntary actions has started to dominate (Georgopoulos et al., 1982, 1986) with potentially important implications for such areas as brain-computer interface and motor recovery (Birbaumer & Cohen, 2007; Wolpaw, 2007; Nicolelis & Lebedev, 2009). With all due respect to the great progress in this field, one has to admit that the question “What is represented in activation patterns of brain neuronal populations?” remains unanswered. Or, more precisely, the available answers (typically, related directly to patterns of mechanical performance variables) are specific to particular tasks and experimental conditions. Bernstein wrote in 1936, “In the higher motor centers of the brain (very probably in the cortex of the large hemispheres) one can find a localized reflection of a projection of the external space in a form in which the subject perceives the external space with motor means.” This statement deserves being read slowly. It implies that cortical neuronal populations show patterns reflective not of a motor process but perception of the task with means related to action production. So far, only a handful of studies tried to interpret brain activity within a motor control theory. In particular, movement disorders in patients with spasticity have been interpreted as reflections of problems with shifts of the threshold of the tonic stretch reflex (TSR) for involved muscle groups within its whole range (Levin & Feldman, 1994; Jobin & Levin, 2000; Calota & Levin, 2009). Recall that TSR threshold is the minimal, elemental component of referent body configuration at the level of control of single muscles. The control of any voluntary action ultimately has to use shifts of the TSR threshold and, if those are severely limited, both inability to perform a range of actions and inability to relax in a range of postures can be expected. Studies of finger coordination in patients with neurological disorders reviewed in the previous section suggest that there may be distribution of responsibilities for patterns of action vs. their stability between cortical and subcortical structures. Transcranial magnetic stimulation (TMS; reviewed in Rothwell, 2011) presents a powerful tool to study effects of stimulation of specific brain (typically, cortical) areas on motor actions. It has been used in experiments that associated the TMSinduced output of the cortex with shifts in referent positions of the wrist joint (Raptis et al., 2010; Ilmane et al., 2013). In those experiments, muscle responses MC Vol. 19, No. 2, 2015



to TMS, when matched by the baseline muscle activation level, were smaller at joint locations corresponding to longer length of the target muscles. This was interpreted as a reflection of a larger reflex contribution to muscle activation for longer muscles, which required smaller contribution from descending pathways. This was associated with smaller background activation of cortical neurons and, correspondingly, their lower excitability. To answer the question of this section, we definitely know very little about the brain control of the hand. The modest knowledge we have allows making hypotheses about the role of different brain structures in hand control and turns this field into an exciting and promising area of research. Acknowledgments Preparation of this manuscript was in part supported by NIH grants AR-048563 and NS-035032.

References Alexandrov, A.V., Frolov, A.A., & Massion, J. (2001). Biomechanical analysis of movement strategies in human forward trunk bending. I. Modeling. Biological Cybernetics, 84, 425–434. PubMed doi:10.1007/PL00007986 Arbib, M.A., Iberall, T., & Lyons, D. (1985). Coordinated control programs for movements of the hand. In A.W. Goodwin & I. Darian-Smith (Eds.), Hand function and the neocortex (pp. 111–129). Berlin: Springer-Verlag. Asaka, T., Wang, Y., Fukushima, J., & Latash, M.L. (2008). Learning effects on muscle modes and multi-mode synergies. Experimental Brain Research, 184, 323–338. PubMed doi:10.1007/s00221-007-1101-2 Asanuma, H. (1973). Cerebral cortical control of movements. The Physiologist, 16, 143–166. PubMed Bassin, P.V., Bernstein, N.A., & Latash, L.P. (1966) On the problem of the relation between structure and function in the brain from a contemporary point of view. In: Grastschenkov, N.I. (Ed.) Physiology in Clinical Practice, pp. 38-71, Nauka: moscow (in Russian). Bernstein, N.A. (1930) A new method of mirror cyclographie and its application towards the study of labor movements during work on a workbench. Hygiene, Safety and Pathology of Labor, # 5, p. 3-9, and # 6, p. 3-11. (in Russian). Bernstein, N.A. (1935). The problem of interrelation between coordination and localization. Archives of Biological Science, 38, 1–35 (in Russian). Bernstein, N.A. (1967). The Co-ordination and Regulation of Movements. Oxford: Pergamon Press. Bernstein, N.A. (2003). Contemporary Studies on the Physiology of the Neural Process. Moscow, Russia: Smysl. Birbaumer, N., & Cohen, L.G. (2007). Brain-computer interfaces: communication and restoration of movement in paralysis. The Journal of Physiology, 579, 621–636. PubMed doi:10.1113/jphysiol.2006.125633 Bizzi, E. & Cheung, V.C. (2013) The neural origin of muscle synergies. Frontiers in Computational Neuroscience 29;7:51. Calota, A., & Levin, M.F. (2009). Tonic stretch reflex threshold as a measure of spasticity: implications for clinical practice. Topics in Stroke Rehabilitation, 16, 177–188. PubMed doi:10.1310/tsr1603-177 Christou, E.A. (2009). Aging and neuromuscular adaptations with practice. In M. Shinohara (Ed.), Advances in Neuromuscular Physiology of Motor Skills and Muscle Fatigue (pp. 65–79). Kerala, India: Research Signpost. MC Vol. 19, No. 2, 2015


122 Latash

Cole, K.J., Rotella, D.L., & Harper, J.G. (1999). Mechanisms for age-related changes of fingertip forces during precision gripping and lifting in adults. The Journal of Neuroscience, 19, 3238–3247. PubMed Danion, F., Schöner, G., Latash, M.L., Li, S., Scholz, J.P., & Zatsiorsky, V.M. (2003). A force mode hypothesis for finger interaction during multi-finger force production tasks. Biological Cybernetics, 88, 91–98. PubMed doi:10.1007/s00422-002-0336-z Danna-Dos-Santos, A., Degani, A.M., & Latash, M.L. (2008). Flexible muscle modes and synergies in challenging whole-body tasks. Experimental Brain Research, 189, 171–187. PubMed doi:10.1007/s00221-008-1413-x d’Avella, A., Saltiel, P., & Bizzi, E. (2003). Combinations of muscle synergies in the construction of a natural motor behavior. Nature Neuroscience, 6, 300–308. PubMed doi:10.1038/nn1010 Dizio, P., & Lackner, J.R. (1995). Motor adaptation to Coriolis force perturbations of reaching movements: endpoint but not trajectory adaptation transfers to the nonexposed arm. Journal of Neurophysiology, 74, 1787–1792. PubMed Eisen, A., Entezari-Taher, M., & Stewart, H. (1996). Cortical projections to spinal motoneurons: changes with aging and amyotrophic lateral sclerosis. Neurology, 46, 1396–1404. PubMed doi:10.1212/WNL.46.5.1396 Evarts, E.V. (1968). Relation of pyramidal tract activity to force exerted during voluntary movement. Journal of Neurophysiology, 31, 14–27. PubMed Feldman, A.G. (2009). Origin and advances of the equilibrium-point hypothesis. Advances in Experimental Medicine and Biology, 629, 637–643. Feldman, A.G., & Latash, M.L. (2005). Testing hypotheses and the advancement of science: Recent attempts to falsify the equilibrium-point hypothesis. Experimental Brain Research, 161, 91–103. PubMed doi:10.1007/s00221-004-2049-0 Flanagan, J.R., & Tresilian, J.R. (1994). Grip-load force coupling: a general control strategy for transporting objects. Journal of Experimental Psychology. Human Perception and Performance, 20, 944–957. PubMed doi:10.1037/0096-1523.20.5.944 Flanagan, J.R., & Wing, A.M. (1993). Modulation of grasp force with load force during point-to-point arm movements. Experimental Brain Research, 95, 131–143. PubMed doi:10.1007/BF00229662 Forssberg, H., Kinoshita, H., Eliasson, A.C., Johansson, R.S., Westling, G., & Gordon, A.M. (1992). Development of human precision grip. II. Anticipatory control of isometric forces targeted for object’s weight. Experimental Brain Research, 90, 393–398. PubMed doi:10.1007/BF00227253 Gelfand, I.M., & Latash, M.L. (1998). On the problem of adequate language in movement science. Motor Control, 2, 306–313. PubMed Georgopoulos, A.P., Kalaska, J.F., Caminiti, R., & Massey, J.T. (1982). On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. The Journal of Neuroscience, 2, 1527–1537. PubMed Georgopoulos, A.P., Schwartz, A.B., & Kettner, R.E. (1986). Neural population coding of movement direction. Science, 233, 1416–1419. PubMed doi:10.1126/science.3749885 Gorniak, S., Zatsiorsky, V.M., & Latash, M.L. (2007a). Hierarchies of synergies: An example of the two-hand, multi-finger tasks. Experimental Brain Research, 179, 167–180. PubMed doi:10.1007/s00221-006-0777-z Gorniak, S., Zatsiorsky, V.M., & Latash, M.L. (2007b). Emerging and disappearing synergies in a hierarchically controlled system. Experimental Brain Research, 183, 259–270. PubMed doi:10.1007/s00221-007-1042-9 Gorniak, S., Zatsiorsky, V.M., & Latash, M.L. (2009). Hierarchical control of static prehension: II. Multi-digit synergies. Experimental Brain Research, 194, 1–15. PubMed doi:10.1007/s00221-008-1663-7 Grabiner, M.D., & Enoka, R.M. (1995). Changes in movement capabilities with aging. Exercise and Sport Sciences Reviews, 23, 65–104. PubMed doi:10.1249/00003677199500230-00005 MC Vol. 19, No. 2, 2015



Henderson, G., Tomlinson, B.E., & Gibson, P.H. (1980). Cell counts in human cerebral cortex in normal adults throughout life using an image analysing computer. Journal of the Neurological Sciences, 46, 113–136. PubMed doi:10.1016/0022-510X(80)900489 Hinder, M.R., & Milner, T.E. (2003). The case for an internal dynamics model versus equilibrium point control in human movement. The Journal of Physiology, 549, 953–963. PubMed doi:10.1113/jphysiol.2002.033845 Horak, F.B., & Nashner, L.M. (1986). Central programming of postural movements: adaptation to altered support-surface configurations. Journal of Neurophysiology, 55, 1369–1381. PubMed Houk, J.C. (2005). Agents of the mind. Biological Cybernetics, 92, 427–437. PubMed doi:10.1007/s00422-005-0569-8 Ilmane, N., Sangani, S., & Feldman, A.G. (2013). Corticospinal control strategies underlying voluntary and involuntary wrist movements. Behavioural Brain Research, 236, 350–358. PubMed doi:10.1016/j.bbr.2012.09.008 Ivanenko, Y.P., Cappellini, G., Dominici, N., Poppele, R.E., & Lacquaniti, F. (2005). Coordination of locomotion with voluntary movements in humans. The Journal of Neuroscience, 25, 7238–7253. PubMed doi:10.1523/JNEUROSCI.1327-05.2005 Ivanenko, Y.P., Poppele, R.E., & Lacquaniti, F. (2004). Five basic muscle activation patterns account for muscle activity during human locomotion. The Journal of Physiology, 556, 267–282. PubMed doi:10.1113/jphysiol.2003.057174 Jaric, S., Collins, J.J., Marwaha, R., & Russell, E. (2006). Interlimb and within limb force coordination in static bimanual manipulation task. Experimental Brain Research, 168, 88–97. PubMed doi:10.1007/s00221-005-0070-6 Jobin, A., & Levin, M.F. (2000). Regulation of stretch reflex threshold in elbow flexors in children with cerebral palsy: a new measure of spasticity. Developmental Medicine and Child Neurology, 42, 531–540. PubMed doi:10.1017/S0012162200001018 Johansson, R.S., & Westling, G. (1988). Coordinated isometric muscle commands adequately and erroneously programmed for the weight during lifting task with precision grip. Experimental Brain Research, 71, 59–71. PubMed Kapur, S., Zatsiorsky, V.M., & Latash, M.L. (2010). Age-related changes in the control of finger force vectors. Journal of Applied Physiology, 109, 1827–1841. PubMed doi:10.1152/japplphysiol.00430.2010 Kargo, W.J., Ramakrishnan, A., Hart, C.B., Rome, L.C., & Giszter, S.F. (2010). A simple experimentally based model using proprioceptive regulation of motor primitives captures adjusted trajectory formation in spinal frogs. Journal of Neurophysiology, 103, 573–590. PubMed doi:10.1152/jn.01054.2007 Kawato, M. (1999). Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9, 718–727. PubMed doi:10.1016/S0959-4388(99)00028-8 Kelso, J.A., & Holt, K.G. (1980). Exploring a vibratory systems analysis of human movement production. Journal of Neurophysiology, 43, 1183–1196. PubMed Kilbreath, S.L., & Gandevia, S.C. (1994). Limited independent flexion of the thumb and fingers in human subjects. The Journal of Physiology, 479, 487–497. PubMed doi:10.1113/ jphysiol.1994.sp020312 Klous, M., Mikulic, P., & Latash, M.L. (2011). Two aspects of feedforward postural control: Anticipatory postural adjustments and anticipatory synergy adjustments. Journal of Neurophysiology, 105, 2275–2288. PubMed doi:10.1152/jn.00665.2010 Koshland, G.F., Gerilovsky, L., & Hasan, Z. (1991). Activity of wrist muscles elicited during imposed or voluntary movements about the elbow joint. Journal of Motor Behavior, 23, 91–100. PubMed doi:10.1080/00222895.1991.9942026 Krishnamoorthy, V., Latash, M.L., Scholz, J.P., & Zatsiorsky, V.M. (2003). Muscle synergies during shifts of the center of pressure by standing persons. Experimental Brain Research, 152, 281–292. PubMed doi:10.1007/s00221-003-1574-6 MC Vol. 19, No. 2, 2015


124 Latash

Krishnan, V., Latash, M.L., & Aruin, A.S. (2012). Early and late components of feedforward postural adjustments to predictable perturbations. Clinical Neurophysiology, 123, 1016–1026. PubMed doi:10.1016/j.clinph.2011.09.014 Lackner, J.R., & DiZio, P. (1994). Rapid adaptation to Coriolis force perturbations of arm trajectory. Journal of Neurophysiology, 72, 299–313. PubMed Lang, C.E., & Schieber, M.H. (2003). Differential impairment of individuated finger movements in humans after damage to the motor cortex or the corticospinal tract. Journal of Neurophysiology, 90, 1160–1170. PubMed doi:10.1152/jn.00130.2003 Latash, L.P., Latash, M.L., & Mejier, O.G. (2000). Thirty years later: On the problem of the relation between structure and function in the brain from a contemporary viewpoint (1966). Part II. Motor Control, 4, 125–149. PubMed Latash, M.L. (2008). Synergy. New York: Oxford University Press. Latash, M.L. (2010). Motor synergies and the equilibrium-point hypothesis. Motor Control, 14, 294–322. PubMed Latash, M.L. (2012). The bliss (not the problem) of motor abundance (not redundancy). Experimental Brain Research, 217, 1–5. PubMed doi:10.1007/s00221-012-3000-4 Latash, M.L., Aruin, A.S., & Shapiro, M.B. (1995). The relation between posture and movement: A study of a simple synergy in a two-joint task. Human Movement Science, 14, 79–107. doi:10.1016/0167-9457(94)00046-H Latash, M.L., Friedman, J., Kim, S.W., Feldman, A.G., & Zatsiorsky, V.M. (2010). Prehension synergies and control with referent hand configurations. Experimental Brain Research, 202, 213–229. PubMed doi:10.1007/s00221-009-2128-3 Latash, M.L., & Gottlieb, G.L. (1990). Compliant characteristics of single joints: Preservation of equifinality with phasic reactions. Biological Cybernetics, 62, 331–336. PubMed doi:10.1007/BF00201447 Latash, M.L., Scholz, J.F., Danion, F., & Schöner, G. (2001). Structure of motor variability in marginally redundant multi-finger force production tasks. Experimental Brain Research, 141, 153–165. PubMed doi:10.1007/s002210100861 Latash, M.L., Scholz, J.P., & Schöner, G. (2007). Toward a new theory of motor synergies. Motor Control, 11, 276–308. PubMed Levin, M.F., & Feldman, A.G. (1994). The role of stretch reflex threshold regulation in normal and impaired motor control. Brain Research, 657, 23–30. PubMed doi:10.1016/00068993(94)90949-0 Li, Z-M., Latash, M.L., & Zatsiorsky, V.M. (1998). Force sharing among fingers as a model of the redundancy problem. Experimental Brain Research, 119, 276–286. PubMed doi:10.1007/s002210050343 Massion, J. (1992). Movement, posture and equilibrium: Interaction and coordination. Progress in Neurobiology, 38, 35–56. PubMed doi:10.1016/0301-0082(92)90034-C Mattos, D., Kuhl, J., Scholz, J.P., & Latash, M.L. (2013). Motor equivalence (ME) during reaching: Is ME observable at the muscle level? Motor Control, 17, 145–175. PubMed Mattos, D., Latash, M.L., Park, E., Kuhl, J., & Scholz, J.P. (2011). Unpredictable elbow joint perturbation during reaching results in multijoint motor equivalence. Journal of Neurophysiology, 106, 1424–1436. PubMed doi:10.1152/jn.00163.2011 Newell, K.M., & Corcos, D.M. (1993). Variability in motor control. Urbana, IL: Human Kinetics. Nicolelis, M.A., & Lebedev, M.A. (2009). Principles of neural ensemble physiology underlying the operation of brain-machine interfaces. Nature Reviews. Neuroscience, 10, 530–540. PubMed doi:10.1038/nrn2653 Niu, X., Latash, M.L., & Zatsiorsky, V.M. (2007). Prehension synergies in the grasps with complex friction patterns: local vs. synergic effects and the template control. Journal of Neurophysiology, 98, 16–28. PubMed doi:10.1152/jn.00058.2007 Niu, X., Terekhov, A.V., Latash, M.L., & Zatsiorsky, V.M. (2012). Reconstruction of the unknown optimization cost functions from experimental recordings during static multifinger prehension. Motor Control, 16, 195–228. PubMed MC Vol. 19, No. 2, 2015



Olafsdottir, H., Yoshida, N., Zatsiorsky, V.M., & Latash, M.L. (2005). Anticipatory covariation of finger forces during self-paced and reaction time force production. Neuroscience Letters, 381, 92–96. PubMed doi:10.1016/j.neulet.2005.02.003 Olafsdottir, H., Zhang, W., Zatsiorsky, V.M., & Latash, M.L. (2007). Age related changes in multi-finger synergies in accurate moment of force production tasks. Journal of Applied Physiology, 102, 1490–1501. PubMed doi:10.1152/japplphysiol.00966.2006 Park, J., Jo, H.J., Lewis, M.M., Huang, X., & Latash, M.L. (2013a). Effects of Parkinson’s disease on optimization and structure of variance in multi-finger tasks. Experimental Brain Research, 231, 51–63. PubMed doi:10.1007/s00221-013-3665-3 Park, J., Lewis, M.M., Huang, X., & Latash, M.L. (2013b). Effects of olivo-ponto-cerebellar atrophy (OPCA) on finger interaction and coordination. Clinical Neurophysiology, 124, 991–998. PubMed doi:10.1016/j.clinph.2012.10.021 Park, J., Lewis, M.M., Huang, X., & Latash, M.L. (2014). Dopaminergic modulation of motor coordination in Parkinson’s disease. Parkinsonism & Related Disorders, 20, 64–68. PubMed doi:10.1016/j.parkreldis.2013.09.019 Park, J., Wu, Y-H., Lewis, M.M., Huang, X., & Latash, M.L. (2012). Changes in multi-finger interaction and coordination in Parkinson’s disease. Journal of Neurophysiology, 108, 915–924. PubMed doi:10.1152/jn.00043.2012 Park, J., Zatsiorsky, V.M., & Latash, M.L. (2010). Optimality vs. variability: An example of multi-finger redundant tasks. Experimental Brain Research, 207, 119–132. PubMed doi:10.1007/s00221-010-2440-y Pataky, T.C., Latash, M.L., & Zatsiorsky, V.M. (2007). Finger interaction during radial and ulnar deviation: Experimental data and neural network modeling. Experimental Brain Research, 179, 301–312. PubMed doi:10.1007/s00221-006-0787-x Pilon, J-F., De Serres, S.J., & Feldman, A.G. (2007). Threshold position control of arm movement with anticipatory increase in grip force. Experimental Brain Research, 181, 49–67. PubMed doi:10.1007/s00221-007-0901-8 Prilutsky, B.I., & Zatsiorsky, V.M. (2002). Optimization-based models of muscle coordination. Exercise and Sport Sciences Reviews, 30, 32–38. PubMed doi:10.1097/00003677200201000-00007 Raptis, H., Burtet, L., Forget, R., & Feldman, A.G. (2010). Control of wrist position and muscle relaxation by shifting spatial frames of reference for motoneuronal recruitment: possible involvement of corticospinal pathways. The Journal of Physiology, 588, 1551–1570. PubMed doi:10.1113/jphysiol.2009.186858 Reisman, D., & Scholz, J.P. (2003). Aspects of joint coordination are preserved during pointing in persons with post-stroke hemiparesis. Brain, 126, 2510–2527. PubMed doi:10.1093/brain/awg246 Robert, T., Zatsiorsky, V.M., & Latash, M.L. (2008). Multi-muscle synergies in an unusual postural task: Quick shear force production. Experimental Brain Research, 187, 237–253. PubMed doi:10.1007/s00221-008-1299-7 Rothwell, J.C. (2011). Using transcranial magnetic stimulation methods to probe connectivity between motor areas of the brain. Human Movement Science, 30, 906–915. PubMed doi:10.1016/j.humov.2010.07.007 Schieber, M.H. (2001). Constraints on somatotopic organization in the primary motor cortex. Journal of Neurophysiology, 86, 2125–2143. PubMed Schmidt, R.A., & McGown, C. (1980). Terminal accuracy of unexpected loaded rapid movements: Evidence for a mass-spring mechanism in programming. Journal of Motor Behavior, 12, 149–161. PubMed doi:10.1080/00222895.1980.10735215 Scholz, J.P., & Latash, M.L. (1998). A study of a bimanual synergy associated with holding an object. Human Movement Science, 17, 753–779. doi:10.1016/S0167-9457(98)00025-6 Scholz, J.P., & Schöner, G. (1999). The uncontrolled manifold concept: Identifying control variables for a functional task. Experimental Brain Research, 126, 289–306. PubMed doi:10.1007/s002210050738 MC Vol. 19, No. 2, 2015


126 Latash

Shadmehr, R., & Wise, S.P. (2005). The computational neurobiology of reaching and pointing. Cambridge, MA: MIT Press. Shim, J.K., Lay, B., Zatsiorsky, V.M., & Latash, M.L. (2004). Age-related changes in finger coordination in static prehension tasks. Journal of Applied Physiology, 97, 213–224. PubMed doi:10.1152/japplphysiol.00045.2004 Shim, J.K., Park, J., Zatsiorsky, V.M., & Latash, M.L. (2006). Adjustments of prehension synergies in response to self-triggered and experimenter-triggered load and torque perturbations. Experimental Brain Research, 175, 641–653. PubMed doi:10.1007/ s00221-006-0583-7 Shinohara, M., Li, S., Kang, N., Zatsiorsky, V.M., & Latash, M.L. (2003). Effects of age and gender on finger coordination in maximal contractions and submaximal force matching tasks. Journal of Applied Physiology, 94, 259–270. PubMed doi:10.1152/ japplphysiol.00643.2002 Smeets, J.B., & Brenner, E. (1999). A new view on grasping. Motor Control, 3, 237–271. PubMed Smeets, J.B., & Brenner, E. (2001). Independent movements of the digits in grasping. Experimental Brain Research, 139, 92–100. PubMed doi:10.1007/s002210100748 Terekhov, A.V., Pesin, Y.B., Niu, X., Latash, M.L., & Zatsiorsky, V.M. (2010). An analytical approach to the problem of inverse optimization: An application to human prehension. Journal of Mathematical Biology, 61, 423–453. PubMed doi:10.1007/s00285-0090306-3 Ting, L.H., & Macpherson, J.M. (2005). A limited set of muscle synergies for force control during a postural task. Journal of Neurophysiology, 93, 609–613. PubMed doi:10.1152/ jn.00681.2004 Wilhelm, L., Zatsiorsky, V.M., & Latash, M.L. (2013). Equifinality and its violations in a redundant system: Multi-finger accurate force production. Journal of Neurophysiology, 110, 1965–1973. PubMed doi:10.1152/jn.00461.2013 Wolpaw, J.R. (2007). Brain-computer interfaces as new brain output pathways. The Journal of Physiology, 579, 613–619. PubMed doi:10.1113/jphysiol.2006.125948 Wu, Y-H., Zatsiorsky, V.M., & Latash, M.L. (2013). Control of finger force vectors with changes in fingertip referent coordinates. Journal of Motor Behavior, 45, 15–20. PubMed doi:10.1080/00222895.2012.736434 Zatsiorsky, V.M., & Latash, M.L. (2004). Prehension synergies. Exercise and Sport Sciences Reviews, 32, 75–80. PubMed doi:10.1097/00003677-200404000-00007 Zatsiorsky, V.M., & Latash, M.L. (2008). Multi-finger prehension: An overview. Journal of Motor Behavior, 40, 446–476. PubMed doi:10.3200/JMBR.40.5.446-476 Zatsiorsky, V.M., Li, Z-M., & Latash, M.L. (2000). Enslaving effects in multi-finger force production. Experimental Brain Research, 131, 187–195. PubMed doi:10.1007/ s002219900261 Zhou, T., Wu, Y-H., Bartsch, A., Cuadra, C., Zatsiorsky, V.M., & Latash, M.L. (2013). Anticipatory synergy adjustments: Preparing a quick action in an unknown direction. Experimental Brain Research, 226, 565–573. PubMed doi:10.1007/s00221-013-3469-5

MC Vol. 19, No. 2, 2015

How long shall we record electroencephalography?

Iterative reconstruction: how it works, how to apply it.

Liver allocation: can we ever get it right and should we ever get it right?

We know ADAPT works-Now it works for you.

Autostigmatic microscope and how it works.

Interferon (II): Learning about how it works.

How Can We Understand the Genomic Basis of Nematode Parasitism?

The liver: how we do it.

[Sex without backache: this is how it works].

Bruises: is it a case of "the more we know, the less we understand?".

What shall we teach undergraduates?

How do we understand dose of rehabilitative treatment?

What shall we teach undergraduates?

Fresh blood or old blood? How shall we manage the inventories?

Laparoscopic Pringle maneuver: how we do it?

Barrett's oesophagus: how should we manage it?

Chronic toxicity: How can we measure it?

Postadenoidectomy hemorrhage: how we do it?

Preventing readmissions: Geisinger: how we do it.

Left ventricular diverticulum: how we do it.

Should we treat pyrexia? And how do we do it?

What shall we teach undergraduates?

What shall we teach undergraduates?

What shall we teach undergraduates?