Summary

Tremor is defined as a “… rhythmical, involuntary oscillatory movement of a body part …” (Deuschl et al., 1998). These involuntary movements can easily affect the voluntary movements of reaching and grasping up to the total loss of control in patients with severe tremor disorders. The following chapter will review the clinical characteristics and pathophysiological concepts of the most frequent and pathophysiologically important tremor disorders and link the findings to the control of grasping and other hand functions.

Physiological tremor

Clinical characteristics

Any movement or isometric contraction is accompanied by the mostly invisible normal physiological tremor. The limits between normal and pathological tremors can be difficult to define. A pragmatic clinical approach is to define abnormal tremor whenever it is visible to the naked eye. The frequency of physiological tremor is usually greater than 7–8 Hz. It has recently been proposed that any tremor at lower frequencies is likely to be pathological (Elbe et al., 2005), but in cases of gradual transitions this clinical criterion can be problematic.

Pathophysiology

Theoretically tremor oscillations can emerge from two basic mechanisms. Any movable limb can be regarded as a pendulum with the capability to swing rhythmically (oscillate). These oscillations will automatically assume the resonant frequency of this limb which is dependent on its mechanical properties; the greater its weight the lower its resonant frequency, the greater the joint stiffness the higher its frequency (Elbe & Randall, 1978; Lakie et al.,1986). Any mechanical perturbation can activate such an oscillation.

Summary

Research on grasping kinematics has proved to be particularly insightful in revealing important aspects of the motor control and selection processes underlying the control of hand action. The aim of this chapter is to provide a synthetic overview of the main kinematic techniques which have been utilized for describing and quantifying grasping movements. The first part of the chapter includes a brief description of the basic kinematic principles. The second part focuses on the main techniques used to perform kinematic analysis of grasping movements. Specifically, it describes video, optoelectronic, and sensor bending techniques. The third part is concerned with a brief review of studies which have characterized the kinematics of grasping not only in healthy adults but also in developmental, neuropsychological and comparative research. We conclude the chapter by discussing recent research issues and technical approaches which have recently started to emerge.

Introduction

Interest in the kinematic analysis of grasping was largely stimulated by the work of Marc Jeannerod who identified how specific kinematic landmarks modulate with respect to object properties to allow for both successful hand positioning and object grasping (Jeannerod, 1981, 1984). Since these early observations, this movement has been well characterized in those with no neurological damage. For subjects with central nervous system damage kinematic assessment allows a complete description of dysfunction and consequently assists with diagnosis and design of appropriate therapeutic regimes.

Summary

Stroke is the leading cause of disability in the adult worldwide. The most common neurological impairment following stroke is weakness or loss of sensibility of the extremities contralateral to the side of the brain lesion. Only about 40% of affected individuals regain full recovery; the remaining 60% have persistent neurological deficits that impact on their social functioning in private and community life. By now, much of our clinical and scientific interest is focused on stroke prevention and acute stroke therapy. In contrast, there is less effort in developing novel strategies for hand motor rehabilitation after stroke. This is surprising since about two-thirds of stroke survivors are left with permanent sensory or motor impairment. This chapter discusses the intrinsic capacity of the cortical motor system for reorganization and gives an overview of established and novel concepts for sensorimotor rehabilitation of the hand after stroke.

Introduction

Stroke is the leading cause of disability in the adult worldwide (Kolominsky-Rabas et al., 2001). The annual incidence of stroke is 100–300 per 100,000 (Broderick et al., 1998). The most common impairment following stroke is weakness of the limbs contralateral to the side of the brain lesion (Kelly-Hayes et al., 1998). Only about 40% of stroke survivors recover completely (Hankey et al., 2002) and among the remaining 60% permanent sensory and/or motor disability of the hand constitutes a major problem (Stein, 1998).

Summary

Transcranial magnetic stimulation (TMS) has emerged as a suitable technique to investigate the network of cortical areas involved in human grasp/reach movements. Applied over the primary motor cortex (M1), TMS reveals the pattern of activation of different muscles during complex reaching-to-grasp tasks. Repetitive TMS (rTMS) used to induce “virtual lesions” of other cortical areas has allowed investigation of other cortical structures such as the ventral premotor cortex (PMv), dorsal premotor cortex (PMd) and the anterior intraparietal sulcus (aIPS). Each of these appears to contribute to specific aspects of reaching, grasping and lifting objects. Finally, twin-coil TMS studies can illustrate the time course of operation of parallel intracortical circuits that mediate functional connectivity between the PMd, PMv, the posterior parietal cortex and the primary motor cortices.

Introduction

The ease with which we can make reach-to-grasp movements conceals a good deal of the underlying complexity of the task. Thus, the target of the reach must be located in space; a decision must be made about the most appropriate type and orientation of grasp according to the weight and shape of the object; and the timing of the reaching movement of the arm must be synchronized with the opening of the hand so that the object can be grasped as effectively and quickly as possible (for a review see Castiello, 2005; see also Chapters 2 and 10).

Summary

Cerebral palsy (CP) is the most common cause of severe physical disability in childhood. Spastic hemiplegia, characterized by motor impairments largely affecting one side of the body, is the most common form of CP. The resulting impaired hand function is one of the most disabling symptoms of hemiplegia, affecting self-care activities such as feeding, dressing and grooming. Consequently, children with hemiplegic CP tend not to use the more affected extremity. This “developmental non-use” can lead to further deficits, most notably affecting bimanual coordination. To date, there is unfortunately little evidence of efficacy of any specific treatment approach. Nevertheless, several lines of evidence suggest the impairments are not static. Upper extremity performance in children with CP may improve with practice and development, indicating that hand function may well be amenable to treatment. In this chapter we review this evidence along with studies involving intensive unilateral practice; i.e. constraint-induced movement therapy (CIMT). We then discuss important limitations of CIMT (most importantly, bimanual impairments underlie functional limitations) and introduce a new form of intensive training to address these limitations: Hand–Arm Bimanual Intensive Training (HABIT). The clinical implications of these findings and future directions for pediatric rehabilitation research are discussed.

Introduction

Cerebral palsy (CP) is a development disorder of movement and posture causing limitations in activity and deficits in motor skill (Bax et al., 2005) and is attributed to non-progressive disturbances in the developing fetal or infant brain.

Summary

Imprecise and unwanted movements characterize the clinical presentation of focal hand dystonia. It is often task-specific, manifesting as writer's cramp or musician's dystonia (e.g. guitarist's cramp). Repetitive, stereotyped movements play a role in the development of the dystonia, but clearly, a pathophysiological substrate must be present for the disorder to manifest. This substrate is likely due to genetics; however, the exact genetic abnormality is not yet known. Although presenting as a motor disorder, dystonia is also a sensory disorder with subtle abnormalities found in spatial and temporal discrimination and with disordered sensory cortical maps. Abnormal cortical plasticity and a failure of homeostatic mechanisms also are seen in dystonia. Finally, a loss of inhibition from excessive muscle discharge to alterations in cortical circuits has been identified in dystonia. As a result, abnormal sensorimotor integration, abnormal plasticity and a loss of inhibition all are implicated in the pathophysiology of focal hand dystonia. Currently, it is not known which of these pathophysiological abnormalities is primary or secondary to the disorder development. Treatment strategies are aimed at ameliorating these physiological changes by improving the sensory deficit, normalizing plasticity and restoring inhibition.

Focal hand dystonia

Dystonia, a neurological disorder, is characterized by abnormal posturing due to sustained muscle contractions, which interferes with the normal performance of motor tasks (Hallett, 2004). Dystonia can be classified by age at onset, by distribution and by cause (Tarsy & Simon, 2006).

Summary

Skilled manipulatory behaviors require complex spatial and temporal coordination of the digits. In healthy individuals, visual and somatosensory feedback is processed and integrated with motor commands thus ensuring successful interactions with objects. This process can be disrupted by a number of neuromuscular diseases. One of the most severely debilitating diseases of hand function is carpal tunnel syndrome (CTS), a compression neuropathy of the median nerve resulting in (1) somatosensory deficits in the thumb, index, middle and ring fingers (lateral half) and, in severe cases, (2) motor deficits in the thumb. Most studies that have investigated the effect of CTS on grasp control have focused on force coordination between the affected digits only. For patients with CTS, control of whole-hand grasping poses the additional challenge of coordinating all digits, a subset of which is characterized by deficits in sensorimotor capabilities. Our research on five-digit grasping shows that CTS affects patients' ability to create accurate sensorimotor memories of multi-digit forces for dexterous manipulation. This knowledge significantly extends and complements the information provided by existing clinical tools to diagnose and monitor CTS, with potential to improve the efficacy of clinical interventions such as physical rehabilitation and hand surgery.

Introduction

The coordination of digit forces during manipulatory behaviors relies on the ability to effectively integrate somatosensory and visual feedback with motor commands responsible for modulating forces at individual digits.

Summary

The clinical spectrum of idiopathic normal pressure hydrocephalus (INPH) comprises gait impairment, cognitive decline and urinary incontinence, all associated with ventricular enlargement and normal cerebrospinal fluid (CSF) pressure on random spinal taps. There is significant variation in the clinical presentation and progression of the disorder and correct diagnosis frequently represents a challenge to the clinical neurologist. Several reports have suggested that the motor disability to be found in INPH may also involve the upper limbs and recent reports provide direct kinetic evidence for this suggestion. Grip-force analysis may also allow an objective evaluation of the beneficial effects of therapeutic strategies in this entity. This chapter reviews the pertinent literature upon the kinetic assessment of upper limb motor disability in the diagnosis and therapy of INPH.

The symptom complex of INPH

Idiopathic normal pressure hydrocephalus (INPH), first described by Hakim & Adams (1965) and Adams et al. (1965), is characterized by the clinical triad of gait disorder, dementia and urinary incontinence, all in the presence of ventriculomegaly and normal cerebrospinal fluid (CSF) pressure on random lumbar puncture. The cause of INPH is not known. When the clinical syndrome occurs as a result of other diseases, such as hemorrhage, traumatic brain injury, cerebral infarction or meningitis, it is referred to as secondary normal pressure hydrocephalus (Gallia et al., 2006). The incidence of INPH has been reported to be about two cases per 100,000 individuals (Vanneste et al., 1992; Krauss & Halve, 2004).

Summary

The human brain has a great potential for reorganizing itself after lesions to regain lost function. In recent years, functional imaging techniques such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have revealed complex changes in cortical networks that are functionally relevant for recovery of function following stroke. In this chapter, we demonstrate how stroke may influence cortical activity over time depending on structural damage and functional outcome. We furthermore discuss different techniques to modulate human brain function, e.g. via pharmacological interventions and transcranial magnetic stimulation (TMS), and their effects on cortical activity in both healthy subjects and patients. Assessing the changes in cortical network architecture following neuromodulation in individual patients will help to design novel treatment strategies based on neurobiological principles to minimize functional impairment resulting from brain lesions.

Introduction

As discussed in the preceding chapters, stroke is the leading cause of permanent disability in Europe and the USA (Gresham et al., 1975; Whisnant, 1984; Taylor et al., 1996) (see Chapters 21 and 29). Treatment of stroke patients in specialized facilities such as neurological intensive care and stroke units has led to a significant reduction of mortality rates in the acute stage of cerebral ischemia or hemorrhage in the past decades (Howard et al., 2001). This positive development is, however, associated with an increasing number of people living with residual neurological symptoms such as hemiparesis, aphasia or other neuropsychological deficits.

Summary

Examination of a well-coordinated task such as prehension in patients with Parkinson's disease (PD) provides an opportunity to gain a better understanding of how basic movement control parameters are altered in patients with this disorder, and provides insights into how altered basal ganglia are involved in the control and regulation of movement when compared with healthy control subjects. In this chapter, evidence is presented for prehensile movements that show that patients have reduced amplitudes of maximum grip aperture and are less able to modulate grip aperture to account for changes in object shape and mass. The coordination between the transport and grasp component also shows some dissimilarity between patients and controls, as patients begin opening the fingers later and reach maximum peak aperture later in time. Patients also begin aperture closure closer to the object than controls, and have a reduced ability to regulate grip forces than controls when an object is grasped, as evidenced by delays in grip-force production and variable force profiles. A neural noise hypothesis is discussed as the neural mechanism that leads to the impairments found in Parkinson's disease patients.

Introduction

Fine motor skills are important to tasks of everyday living and include movements such as grasping a door handle, buttoning a shirt, or reaching and holding a beverage. Prehensile actions, more simply referred to as reach-to-grasp movements, are well-practiced movements that require precise control in transporting the hand to a specified object and grasping the object with the grip aperture (see Chapters 2 and 10).

Summary

Series of action phases characterize natural object manipulation tasks where each phase is responsible for satisfying a task subgoal. Subgoal attainment typically corresponds to distinct mechanical contact events, either involving the making or breaking of contact between the digits and an object or between a held object and another object. Subgoals are realized by the brain selecting and sequentially implementing suitable action-phase controllers that use sensory predictions and afferents signals in specific ways to tailor the motor output in anticipation of requirements imposed by objects' physical properties. This chapter discusses the use of tactile and visual sensory information in this context. It highlights the importance of sensory predictions, especially related to the discrete and distinct sensory events associated with contact events linked to subgoal completion, and considers how sensory signals influence and interact with such predictions in the control of manipulation tasks.

Sensory systems supporting object manipulation

In addition to multiple motor systems (arm, hand, posture), most natural object manipulation tasks engage multiple sensory systems. Vision provides critical information for control of task kinematics. In reaching, we use vision to locate objects in the environment and to identify contact sites for the digits that will be stable and advantageous for various actions we want to perform with the grasped object (Goodale et al., 1994; Santello & Soechting, 1998; Cohen & Rosenbaum, 2004; Cuijpers et al., 2004; Lukos et al., 2007).

Summary

When transporting an object during locomotion, the inertial forces that are indirectly generated through the motion of multiple body parts must be taken into account to prevent object slippage. The grip–inertial force coupling that maintains a secure grasp on a hand-held object is preserved across a variety of locomotor tasks that include variations in velocity and precision demands (e.g. transporting a cup of water). When the locomotor pattern is altered by changing the step length or stepping over an obstacle, the grip–inertial force coupling continues to be under anticipatory control. However, the coupling is less robust and can be explained by increased attention demands. Furthermore, the fine motor grasping functions and gross motor locomotor functions are precisely coordinated across multiple limb segments to ensure successful performance right from the onset of gait initiation. These findings support the notion that grip force is based on moment-to-moment predictions of inertial forces acting on the object at gait initiation and throughout predictable variations in the gait cycle. Internal representations of the interactions between body segments through which inertia is transferred to the object–digit interface are proposed to provide the basis for this anticipatory grip force control.

Introduction

A central question in the study of systems motor control is how simultaneous tasks involving multiple body segments are coordinated. For example, during voluntary movements with a hand-held object, grip (normal) force is coupled to the object's load as well as to the motion-induced inertial (tangential) force in an anticipatory manner to prevent slippage.

Summary

Skilled object manipulation requires the ability to estimate, in advance, the motor commands needed to achieve desired sensory outcomes and the ability to predict the sensory consequences of the motor commands. Because the mapping between motor commands and sensory outcomes depends on the physical properties of grasped objects, the motor system may store and access internal models of objects in order to estimate motor commands and predict sensory consequences. In this chapter, we outline evidence for internal models and discuss their role in object manipulation tasks. We also consider the relationship between internal models of objects employed by the sensorimotor system and representations of the same objects used by the perceptual system to make judgements about objects.

Introduction

Although we have designed computers that can beat grand masters at chess, we have yet to design robots that can manipulate chess pieces with anything like the dexterity of a 5-year-old child. What makes humans so good at object manipulation in comparison to robots? There is no question that the anatomy of the human hand is well adapted for manipulation. On the sensory side, the hand is richly endowed with tactile sensors that provide exquisitely precise information about mechanical interactions between the skin and objects. On the motor side, the numerous kinematic degrees of freedom of the hand enable it to grasp objects of all shapes and sizes.

Summary

This chapter provides a brief presentation of the available techniques for electromyogram (EMG) recordings in the awake monkey using chronically implanted electrodes. We illustrate how this technique can be used for the analysis of the monkey's motor behavior during dexterous grasp. We also investigate how the grasp specificity of EMG activity can be related to the activity of a population of pyramidal tract neurons (PTNs) recorded from the hand area of the primary motor cortex (M1).

Introduction

The ability to grasp and manipulate objects of various sizes and shapes is essential for a large range of human activities. The debilitating loss of skilled hand movements following stroke, spinal injury and many other pathological disorders results in a marked loss of autonomy for the affected patient. The characteristic structure of the human hand provides this organ with a unique combination of motor and sensory capacities that underpin the control of manual dexterity. The anatomy of the hand includes some 27 different bones, and some 39 different muscles located either in the forearm (extrinsic muscles) or in the hand itself (intrinsic muscles; Tubiana, 1981). Special features of bony structures in the hand contribute directly to dexterity, and are important for rotation of the human thumb during precision grip (Tallis, 2004). The muscular control of the multi-articulate hand presents some demanding biomechanical solutions.

Summary

Upper-limb speed and dexterity are frequently impaired after moderate or severe traumatic brain injury (TBI). The speed of functional hand movements can be assessed with standardized tasks, such as the Developmental Hand Function Test and the Purdue Pegboard test. Kinematic data on reaching and grasping can be obtained by optoelectronic motion analyses. The fingertip forces measured during a precision grip–lift task describe fine motor control. With these methods, a series of studies analyzed recovery of hand function in brain-injured children and adolescents (age 4–15 years) over 5 months of inpatient rehabilitation, starting ∼3 months post TBI. Compared with healthy age-matched controls, the patients were slower, their prehension movements exhibited curved and variable movement trajectories, and were delayed especially in the final approach phase. They needed more time to establish a precision grip and showed exaggeratedly high grip forces. Despite substantial recovery, differences in hand function between patients and controls were still present ∼8 months after TBI. Young age at injury was not associated with better recovery. Comparable data for adults are lacking so far.

Traumatic brain injury: incidence, severity and imaging

The annual incidence of traumatic brain injury (TBI) in Germany is about 300 per 100,000 inhabitants (Federal Statistical Office, www.destatis.de). Epidemiological studies from other countries report incidences of ∼200–500/100,000 per year; these variations reflect different inclusion criteria and study designs (Hillier et al., 1997; Servadei et al., 2002; Andersson et al., 2003). Common causes of TBI are traffic accidents, falls and sport-related accidents.

Summary

This chapter briefly reviews four issues: (1) Single-digit contacts, (2) multi-digit grasps, (3) constraints on digit forces and (4) prehension synergies. In the section on the single-digit contacts the main models of contact (the point model, hard-finger contact and soft-finger contact) as well as slip prevention are considered. A subchapter on multi-digit grasps is a tutorial-like introduction to grasp mechanics, in particular to the concept of grasp matrices. The following issues are addressed: (a) vertically oriented prismatic grasps, (b) grasp matrices, (c) non-vertical prismatic grasps, (d) arbitrary grasps, (e) virtual finger and (f) internal forces. The constraints on digit forces are classified as mechanical and biological. Among the biological constraints force deficit and finger enslaving are addressed. Inter-finger connection matrices and their use for reconstruction of the neural command are discussed. Prehension synergies, experimental methods of their research, and the principle of superposition are described.

To manipulate hand-held objects, people exert forces on them. Performers grasp objects in different ways. A simplest classification of the grasps (or grips) includes two varieties: a power grip when the object is in contact with the palm of the hand (e.g. holding a tennis racket) and a precision grip when only the digit tips are in contact with the object. The present chapter deals with the precision grip.

Single-digit contacts

This section concentrates on two issues: modeling the digit contacts and slip prevention.

Modeling the digit contacts

Contact is a collection of adjacent points where two bodies touch each other.