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Chapter 14 Kinesiology
Movement Disorders Handbook of Clinical Neurophysiology, Vol. I M. Hallett (Ed.) © 2003 Elsevier S.v. All rights reserved
191 CHAPTER 14
Kinesiology Christian Dohle* and Hans-Joachim Freund Department ofNeurology, University Hospital Dusseldorf, D-40225 Dusseldorf, Germany
14.1. Overview In motor control studies, recordings of the kinematics and dynamics of movement can be employed at a high level of sophistication. These techniques have been applied for research, for the study of pathophysiology and for diagnostic purposes. In clinical settings, their use is still restricted because they are expensive, time consuming and require skilled technical personnel. Another reason is that better insights into movement kinematics are often not critical for differential diagnosis or therapeutic decisions. The situation is going to change as rehabilitative strategies and procedures become more specific. The previously used qualitative or semiquantitative scores are increasingly complemented by electrophysiological and kinematic recordings. Kinematic techniques will therefore be required for both the study of normal motor behavior and for restorative neurology. Neuroimaging techniques are providing new insights into brain function. They produce high resolution, spatial information about brain structure and functional activation states during a broad range of cognitive and sensorimotor behaviors. In contrast to the highly sophisticated imaging techniques the assessment of behavior is often poorly controlled. For motor control, this is partly due to the restricted mechanical conditions for measurement in the scanners or to interference with the magnetic devices. This situation is just about to change as more recording techniques are available for application in neurofunctional studies. This article gives an outline of the most commonly used kinematic recording techniques and their
* Correspondence to: Dr. C. Dohle, Neurologische Klinik, Universitatsklinikum Dusseldorf, MoorenstraBe 5, D-40225 Dusseldorf, Germany. E-mail address:[email protected] Tel.: +49-211-81 18678; fax: +49-211-81 18485.
methodical backgrounds. The major emphasis is put on the analysis of upper limb movements, as gait analysis is dealt with in a separate chapter.
14.2. Technical principles Depending on the purpose of the movement study, different movement recording systems can be used. One-dimensional systems, such as goniometers or lever constructions can record only one degree of freedom - usually unidirectional limb movements. In contrast, three-dimensional recording systems allow the recording of the entire movement of a limb in three-dimensional space. Both systems have specific advantages. One-dimensional techniques are simpler and cheaper and often serve the desired purpose. Three-dimensional movement recording systems are usually marker-based: the position of (active or passive) markers that are fixed to certain body parts is recorded by detectors. Their position in three-dimensional space has to be calibrated before the measurement. In principle, two different types of systems can be distinguished: (a) Active systems with markers emitting a signal (usually infrared light) in well-defined time intervals which is recorded by CCD cameras (e.g. Optotrak, Selspot (now out of business». (b) Passive systems with markers consisting of colorful or light-reflecting material. Their position is recorded by video- or infrared-based camera systems (e.g. Elite, MacReflex, Vicon). Usually, the recording procedure is based on infrared light. Active systems make use of infrared emitting diodes (LEOs) that are recorded by sensitive cameras. In passive systems the camera is coupled with an array of infrared emitting diodes, and the light is reflected by the markers. The most prominent problem of both systems is noise due to reflections from extraneous shiny surfaces. Other systems can detect markers directly from video recordings with-
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out any additional technical setup. In principle, these systems are very versatile as they do not require any extensive technical set-up. On the other hand, they require considerable manual post-processing and assignment of the markers at the beginning of each recording session. These systems are also much more viable to optical interferences. A completely different technique is based on ultrasound (ZEBRIS). The markers consist of small ultra-sound emitting sources and the signals are recorded by an array of three microphones fixed on a rack at well-defined distances to each other. Although these markers make use of small piezo elements to generate ultrasound, they barely emit electromagnetic waves, so that they can also be used in environments that are sensitive to electromagnetic 'noise' such as magnetoencephalography or magnetic resonance tomography. On the other hand, the system has only a limited range and is very liable to acoustic noise. Common to all these systems is the limitation that one camera can only detect positions in two dimensions. Thus, at least two cameras are necessary in order to obtain a three-dimensional position. Other systems are available that make use of different technologies. Gained from developments for the market of virtual reality techniques, electromagnetic tracking devices are partly employed for movement analysis (mainly Polhemus, Flock of Birds). In these systems, one emitter generates a static electromagnetic field of a certain dimension. The sensor consists of a coil that indicates position and orientation of the moving part relative to the emitter. In principle, these systems are rather cheap, very accurate and can provide both position and orientation of a marker in real-time. On the other hand, they are very liable to any type of metal within the range of the emitter as well as electromagnetic 'noise', e.g. of nearby located large computer monitors. All systems have specific advantages and disadvantages. Active systems are usually more expensive. For measurements, it is necessary to 'wire' the subjects, i.e. to connect the marker and the control unit. This ensures that every marker is always uniquely defined, even if it is occluded during certain measurement epochs. Passive measurement systems are usually cheaper and the measurement is easier to realize, because they do not require direct connections of the markers
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and the control unit. The major drawback of these systems is the ambiguity of marker identification. Consequently, the analysis of every trial has to be preceded by a manual assignment of markers that are then tracked further by the analysis software. Depending on the type of movement, marker trajectories can cross - at least in the field of view of one of the cameras. In this case, the marker assignment has to be corrected manually. In summary, passive systems are cheaper and easier in the initial acquisition of data. This advantage, however, has to be counterbalanced against a much higher demand in manual post-processing. All measurement systems of this type deliver time series of X-, Y: and z-coordinates as a result, that are sampled at certain frequencies. The further analysis of these data is poorly standardized, so that not many commercial programs are available. Usually, different laboratories make use of different, custom-made software packages. A good (and still valid) overview of the general principles of human movement analysis can be found in Winter (1990). Since this review is mostly restricted to applications in neurophysiology and sports medicine, we will mainly concentrate on the analysis of patients. Such recordings offer two advantages: They can quantify certain aspects of motor behavior and disclose qualitative features that cannot be detected otherwise.
14.3. Coordinate systems As outlined above, the majority of three-dimensional movement measurement systems provide (Cartesian) X-, y- and z-coordinates in time. The point of origin and the orientation of these coordinates is defined by a calibration procedure that has to be performed prior to the measurement. The calibration should be adjusted to match the coordinate axes to the main axes of the movement task to be recorded. Generally, the x- and y-axis define the floor or table plane with the z-axis pointing upwards. Although it is clear that the brain does not use cartesian coordinates for its computations, it is less clear what type of coordinates are used instead. In their pioneering studies on goal-directed movements, Soechting and Flanders (Soechting and Terzuolo, 1988; Soechting and Flanders, 1989a, b; Flanders et al., 1992) analyzed the variability of upper-limb pointing movements directed at targets in
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three-dimensional space. From their analysis, they proposed that arm movements are organized in a polar system based at the respective shoulder, having two angles (yaw and elevation) and one length (distance) as coordinate values. This proposal was theoretically based and inferred solely from kinematic measurements. Recent cell recordings in the awake monkey, however, demonstrated different cell populations responding specifically to the variation of one of the three values (Lacquaniti et al., 1995). On the other hand, a study of sensorimotor transformation errors in patients with lesions of the inferior parietal lobule showed dissociations of distance and direction errors, supporting the idea of different central processing modules for these two variables (Darling et al., 2001). 14.4. Levels of observation When subjects are required to write their signature either on a sheet of paper with a fine pen or on a blackboard with a piece of chalk attached to a broomstick, the individual features of their signature remain basically preserved - even when the writing is performed with the foot or a pen between their teeth. This phenomenon is called motor equivalence and neatly demonstrates that the brain is capable to transfer a given movement blueprint into a distinct kinematic pattern, irrespective of the biomechanical requirements. This is accomplished by converting the movement scheme into joint torques and muscle activation patterns specific for the limb that is actually performing the movement (inverse dynamics). For the analysis of human movements, this implies that movement analysis can be performed at different levels of observation: (1) Examination of (one-dimensional) limb angles.
This is particularly useful in the consideration of intra- and inter-limb coupling. When trajectories are recorded in using threedimensional measuring systems, more information is available about the position of the entire limb in space. Thus, additional levels of observation can be analyzed. (2) Examination of trajectories in three-dimensional space (kinematics). (3) Examination of multi-joint angles (limb configuration)
(4) Examination of joint torques (dynamics) Obviously, these considerations mainly apply to whole limb movements, i.e. the analysis of arm movements or gait. As gait analysis is covered in a separate chapter of this volume, the following analysis will focus on upper limb movements. Especially, we will demonstrate how the different levels of observation have been used to determine features of normal and pathological movements in patients with neurological disorders. 14.5. Analysis of patient populations The optimum strategy for the different applications depends whether the kinematic analysis should either elaborate previously defined patient's profiles or to allow the identification of different subpopulations of patients based on the kinematic data. In the first case, the mean values of pre-assigned kinematic parameters are determined for certain, apparently homogenous patient groups. These data can then be referenced either to those of normal subjects, or - if the type of disturbance only affects one side of the body - to the 'unaffected' body side. This procedure can only be applied if the patient groups are well defined and homogenous, such as hemiparkinsonism or hemiparesis. Conversely, kinematic data can be used to identify subgroups of patients with similar kinematic abnormalities and then to examine a possible correlation with distinct lesion localizations. This approach is more demanding, because the identification of the respective kinematic measures that are suitable to differentiate 'affected' from 'not affected' patients requires extensive prior research. 14.6. Temporal kinematics The kinematic analysis of recorded movements is exclusively based upon the trajectory of the recorded body segment, e.g. the hand or the fingertip. As the movement recording is usually performed at sampling frequencies around 100 Hz, it produces a large number of data points. In order to compare the trajectories of one person under different experimental conditions or trajectories of different patient populations it is necessary to decrease the dimensions of the trajectory, i.e. to parameterize the movement. One way to parameterize movements is the determination of fixed temporal 'landmarks'. This approach was extensively used in the analysis of
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grasping movements by Jeannerod and his coworkers (Jeannerod, 1981, 1984, 1988). He focused on hand velocity as a measure for the (proximal) reach component of the arm and grip aperture as a measure for the (distal) grasp component of the fingers. This work demonstrated the independence of these two components, by studying the times of maximum values of the two measures as representatives of the two components of the prehension movement (for review, see Paulignan and Jeannerod, 1996). The independence of these two components was the focus of many subsequent studies in normal subjects. In a typical paradigm in normal subjects, a determinant of one of the two components was varied (e.g. object size) and its effect on the second component (e.g. time of maximum hand velocity) was studied. Further support for the independent organization of these two components came from patients with focal cerebral lesions, especially of the posterior parietal cortex. Single case reports confirmed the role of the posterior parietal cortex for these types of sensorimotor transformations (Jeannerod, 1986a, b; Pause et al., 1989; Jakobson et al., 1991). In a first systematic study in patients with lesions of the parietal cortex, presenting the clinical picture of optic ataxia, Perenin and Vighetto showed deficits in the transport component of both arms in the left visual field following right parietal lesions ('field effect'). On the other hand, the transport component of patients with optic ataxia due to left parietal lesions was not only found to be impaired in the right visual field, but additionally also with all movements of the right arm in the left visual field and with central fixation ('hand effect'). The grasping component was tested for by adjustment of hand orientation in the frontal plane, following the paradigm of Haaxma and co-workers (Haaxma and Kuypers, 1974). Deficits in hand orientation showed the same distribution as deficits in the transport component, so that in this condition no dissociation between the two components could be demonstrated. It should be noted, however, that hand orientation is a kind of intermediate task that has to be accomplished in a synergy of proximal and distal joint angles (Dohle et al., 1995; Desmurget et aI., 1996). A kinematic study of prehension movements in patients with parietal lesions (Binkofski et al., 1998b) demonstrated an impairment of the grasp task but not of the reach component, supporting the
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concept of two independent visuomotor channels. This finding is of special interest as it corresponds to experimental inactivation studies in monkeys where the transport and the grasp component could be impaired independently (Gallese et aI., 1994). In an attempt to identify measures for disturbances of apraxic arm movements, Poizner and co-workers applied the approach of comparing temporal landmarks on repetitive movements. In the analysis of the times of maximum hand velocity and minimum hand path curvature during each stroke of a cyclical arm movement, the decoupling of these two measures was proposed to indicate a specific deficit in apraxic movements (Fig. 1, Poizner et al., 1990). Such interpretations have to be met with caution by two reasons. First, the underlying mathematics is not clear. Based on basic differential geometry (Morasso, 1983; Bartsch, 1986), the absolute value of hand path curvature c(t) at a given point of the three-dimensional trajectory r(t) (with its temporal derivatives velocity r'(t)=v(t) and acceleration r'(t)=a(t)) is calculated as: c(t)
=1(r' (r) x r'(t))/I r' (t) 131 =1(v(t) x a(t))/I v(t) 131
In other words, the value of curvature is calculated as a derivative of hand velocity. This fact on its own makes it highly unlikely that the two measures can be varied by the central commands independently. Second, Poizner's analysis was based mainly on the consideration of maximums and minimums of the time course of velocity and curvature. In normal subjects' movements, no significant time delay was observed between the timing of these extreme values. In apraxic patients, however, their occurrence did not correlate at all. Inspection of the raw data clearly shows that the movements were fragmented and consisted of several movement segments. It is clear, that this type of noisy data produce more extreme values that do not match each other. This leaves the reader wondering what is the phenomenom of decoupling of tangential velocity and hand path curvature. 14.7. Temporal aspects of movement organization
Quantitative measurements can provide insights in new principles of organization. An example are recordings from hand and finger movements that
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Fig. I. Velocity - curvature coupling during carving movements in an apraxic patient (right) in comparison to a normal subject (left). Upper panels: tangential velocity (mis, broken lines) and radius of curvature (m, continuous lines). Lower panels: time correspondences between velocity minima and radius of curvature minima (from Poizner et aI., 1990).
provided evidence for a clear cut functional dichotomy of manipulative hand and finger movements. On a qualitative basis, movements of the hand as a whole used for writing, shading and hammering had been previously classified as extrinsic and movements of the fingers against each other or the palm as intrinsic hand movements. Quantitatively, it has been demonstrated that these two types of hand movements are performed at distinctly different preferred frequencies. Extrinsic hand movement are usually performed at 4-7 Hz (type I) and intrinsic hand movements at 1-1.5 Hz (type II; Kunesch et aI., 1989). Surprisingly, there is no overlap between the two ranges (Fig. 2). Since the difference in their characteristic frequencies cannot be explained by mechanical reasons it has been concluded (Kunesch et aI., 1989) that the grouping into distinct frequency ranges represents different types of sensorimotor interactions. The slower type I movements require a continuous sensory guidance for their execution and must be carried out slowly in order to meet the time requirements for the detailed sensory analysis. The faster type II movements are rapid automated subroutines that are largely predictive and require only some crude sensory monitoring.
The observed grouping at preferred frequencies fits well into the more general scheme that most of our repetitive motor behaviors such as speaking, eyemovements during reading, chewing, walking, swimming or dancing is performed at characteristic frequencies. Central pattern generators in the brainstem play an important role for their generation. Therefore, the assessment of the temporal aspects of motor organization is only possible on the basis of quantitative movement analysis.
14.8. Spatial kinematics Apart from determining the timing of certain landmarks during the movement, the spatial position of some body part at the time is of interest as well. For prehension movements, this approach was first proposed by Haggard and co-workers (Haggard et aI., 1994). In their study, it could be demonstrated that the aperture during a prehension movement was not only scaled on the basis of the temporal pattern of the prehension movement, but also depending on the spatial position of the hand in the entire trajectory. In a patient with cerebellar damage, the strategic trial-to-trial coordination seemed to be
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Fig. 2. Distribution of the frequencies at which finger movements are performed during four tactile exploratory tasks (left) and of hand movements during execution of three normal skills (right) (from Kunesch et aI., 1989).
preserved in spite of gross abnormalities of the arm trajectory (Haggard et al., 1994). Another spatial feature of arm trajectories is preservation of the movement plane, which was presented by Poizner and colleagues as a typical feature of movements in apraxic patients (Poizner et al., 1990). In their study, it could be shown that the plane of motion in cyclical arm movements shows a high inter-trial variability when compared to those of normal subjects. These findings convincingly characterize the deranged movements. However, similar deficits were later shown to occur in deafferented patients (Sainburg et al., 1993), raising the question of specificity of this finding. 14.9. Limb and body configuration
The analysis methods presented so far were only focused on the hand and the finger. However, the three-dimensional recording systems also offer the possibility to catch the movement of the entire limb,
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namely the upper arm and forearm. By adding two further markers at the shoulder and at the elbow, three further limb angles can be calculated: upper arm elevation, upper arm yaw and elbow angle (Soechting and Lacquaniti, 1981). This method does not take into account forearm pronation or supination, which can simply be regarded as a radial segment rotating around an ulnar segment (Reich and Daunicht, 2000). Its calculation out of recorded kinematic data requires a further marker on the forearm defining the transversal axis of the hand joint in space. Soechting and Lacquaniti's pilot studies in normal subjects demonstrated that upper arm elevation (usually referred to as shoulder elevation) and elbow angle are tightly coupled during a pointing (or grasping) task (Soechting and Lacquaniti, 1981). Subsequently, this relationship was found to be disturbed in a variety of neurological disorders, such as deafferentation (Sainburg et al., 1993; Ghez and Sainburg, 1995) (Fig. 3), apraxia (Poizner et al., 1995), in patients with parietal lesions with and without apraxia (Binkofski et al., 1998a), cerebellar ataxia (Bastian et al., 1996; Massaquoi and Hallett, 1996), hemiparesis (Levin, 1996; Beer et al., 2000; Cirstea and Levin, 2000) or Parkinson's disease (Seidler et al., 2001). The authors of these studies noted the similarities of their findings, but it remains unclear if the loss of interjoint coupling is a specific pathological phenomenon, or rather some epiphenomenon that is to be found in different types of deranged arm movements. 14.10. Dynamics
When both information about the joint angles and estimates about the inertial mass of the body segments (e.g. Winter, 1990) are available, inverse dynamics equations can be applied to calculate the joint torques that are necessary to produce the observed movements. Different authors have used different terminologies (Soechting and Lacquaniti, 1981; Hollerbach and Flash, 1982; Bastian et al., 1996), but in general, one has to distinguish (according to the terminology used by Bastian and co-workers): • Net torque: sum of all the torques acting at a joint.
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Control 2
Fig. 3. Coupling of elbow and shoulder motions in two patients with deafferentation (middle and bottom) in comparison to a normal subject (top). The elbow flexion! extension angle has been plotted against the upper arm elevation (top) and yaw (bottom) angles for individual cycles of a four cycle movement (from Sainburg et al., 1993).
According to the formulas presented in Soechting and Lacquaniti (1981) and Bastian et al. (1996), this consists from the sum of:
• muscle torques: sum of all muscle and passive tissue forces that are necessary to generate the observed movement; • gravitational torque: torque produced by the force of gravity acting on the limb segments of the arm; • interaction torques: motion-dependent torques that occur at any joint due to movements that are linked. In normal subjects, it could be demonstrated that phasic EMG activity occurs in muscles solely acting at adjacent joints, even if those joints are mechanically immobilized (Gribble and Ostry, 1999). This was interpreted as experimental support for the theoretical proposal that central control signals to muscles are adjusted, in a predictive manner, to compensate for interaction torques. In only a very limited number of studies have these analysis techniques been applied to patient movements. In studies on patients with deafferentation (Sainburg et al., 1993, 1995), cerebellar ataxia (Bastian et al., 1996) and Parkinson's disease (Seidler et al., 2001) these interaction torques were found to be inadequately adjusted in order to generate smooth, straight movements. It remains unclear, however, if this deficit can be pinpointed to the abnormal control of interaction torques as result of a deficient processing of somatosensory information or whether this disturbance is the pure description of a lack of joint coordination at a different level of observation. Further studies are necessary to clarify this point. In a later study, Bastian and co-workers (Bastian et al., 2000) tried to exclude that this inaccurate adjustment of interaction torques in cerebellar patients was solely due to a general inability to generate sufficient levels of phasic torque. For this purpose, they studied practically identical upper-arm pointing movements in cerebellar patients, either with their shoulder free or their shoulder fixed (Bastian et al., 2000). Cerebellar patients changed the pattern of muscle activity fundamentally when changing between these two conditions, producing inadequately scaled interaction torques, due to deficits in coordination of multi-joint movements. 14.11. Manipulative hand movements
Apart from whole-limb movements of either arm, manipulative movements, such as those during exploration of small objects, can be recorded and
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analyzed quantitatively. In this case, movements of the fingertips of the thumb and the forefinger have to be recorded. Although movement paths are restricted to a narrow workspace, the same trajectory in space is never repeated (Kunesch et al., 1989). The temporal profile, however, shows a regular, periodic pattern (Fig. 4). Spectral analysis typically exhibits a single peak in the frequency spectrum between 1 and 2 Hz, showing high variability across, but not within different subjects (Kunesch et al., 1989). This is typically impaired in subjects with lesions of the posterior parietal cortex, showing deficits in the recognition of objects by haptic exploration (astereognosis). In these patients, the space of exploration is grossly enlarged and the mean frequency and the regularity of the movement is reduced (Kunesch et al., 1995; Binkofski et al., 2001). A more detailed analysis of subgroups of 3D-trajectories
these patients show that astereognosis is correlated with grossly enlarged space of exploration, but not with slower or more irregular movements per se (Binkofski et al., 2001).
14.12. Bilateral synergies The last paragraphs lead to another question: To which extent can different limb segments be controlled independently, especially between both sides of the body? The most basic model of coupled limb movements is that of rhythmic flexion and extension movements of the index fingers or hands of both arms, that are moving either in-phase or anti-phase. This paradigm was extensively studied by Kelso and co-workers who noted that in-phase movements are much more stable than anti-phase movements (Kelso, 1981, Movement<:haraeteflslics of thumb andindex finger
Movement characteristics of lhumb and indexfinger
Frequency spectrum
OIlhumb movemems
Frequencyspectrum 01 thumb movements
Fig. 4. Exploratory finger movements in two patients with focal cerebral lesions (middle and bottom) in comparison to a normal subject (top). Left panels: 3-D reconstruction of thumb (T) and index finger (FF) movement trajectories. Middle panels: scan paths of thumb and index finger. Right panels: frequency distribution of the thumb movements (from Binkofski et aI.,
2001).
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1984; Kelso and Tuller, 1984). Furthermore, if subjects perform externally paced anti-phase movements with increasing movement frequency, a sudden transition to in-phase movements occurs. These observations led to the Haken-Kelso-Bunz model, explaining the superior stability of the inphase pattern by a theoretical model of coupled oscillators (Haken et al., 1985). Although this model has been extensively studied and refined since then (Kay et al., 1987; Forrester and Whitall, 2000; Beek et al., 2002), it is only recently that the perceptual component of this phenomenon has come into the focus of attention. For example, it could be shown that the same stability of the in-phase pattern when compared to the anti-phase holds true when two people have to
synchronize their movements (Bingham et al., 1999). Finally, in a recent study Mechsner and coworkers studied bimanual finger movements when one hand was either pronated or supinated (Mechsner et al., 2001). Surprisingly, in this setup, the symmetrical movements of the fingers were also more stable than parallel movements when one of the two hands was pronated and the other supinated. In other words, the superior stability of the in-phase pattern only depended on the actual movement direction of the fingers, but not on the finger muscles involved. In a second experiment, the subjects had to synchronize two circular-moving handles. In one handle, however, the ratio of transmission was altered in such a way that three rotations of the hand resulted in four rotations of the handle (Mechsner et
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Fig. 5. Bilateral synchronous movements of shoulder (top), elbow (middle) and wrist (bottom) in a patient with right sided hemispherectomy. Left panels: movement cycles (right side: upper trace). Right panels: bilateral correlation (from Miiller et aI., 1991).
200 al., 2001). In this set-up, stability of in-phase circular movements was achieved flawlessly, further supporting the idea that it is the perception of in-phase pattern that is controlled rather than the implementation in the subjects' biomechanical system. All these experiments were performed with movements of distal parts of the arm (hand or finger movements) and synchrony was usually assessed by comparison of the inter-tap-times of each hand. In this analysis procedure, the actual movement trajectory is not considered, but only the final 'goal' (Wiesendanger et al., 1994). Analysis of phase diagrams comparing right- and left sided movements and calculating correlation coefficients between the entire trajectory of either side might help to understand the mechanisms of bilateral coordination in more detail. In a study on bilateral prehension movements using this approach, Dohle and coworkers could demonstrate different coupling mechanisms for the reach and the grasp component respectively (Dohle et al., 2000). Whereas the grasp (thus distal) component shows only little coordination during the movement, suggesting largely independent movement organization mechanisms of either side, the reach (thus proximal) component seems to be 'hard-wired' connected between both sides. This is compatible with a study in a patient with right-sided hemispherectomy, showing preserved coordination between proximal arm movements of either side, but largely independent, fractionated movements of more distal parts of the body (Muller et al., 1991) (Fig. 5).
14.14. Summary In this chapter, we have presented an overview about the different techniques and methods in kinesiology, especially with respect to their application in the movement analysis of neurological patients. A number of different disturbance patterns provide useful information about the characteristic changes in a particular pathological condition. Some of them are specific, while others reflect more general abnormalities.
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Dohle, C, Ostermann, G, Hefter, H and Freund, H-I (2000) Different coupling for the reach and the grasp components in bimanual prehension movements. NeuroReport, 11: 3787-3791.
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Flanders, M, Helms Tillery, SI and Soechting, JF (1992) Early Stages in a sensorimotor transformation. Behav. Brain Sci., 15: 309-362. Forrester, Land Whitall, J (2000) Bimanual finger tapping: effects of frequency and auditory information on timing consistency and coordination. J. Mot. Behav., 32: 176-191. Gallese, V, Murata, A, Kaseda, M, Niki, N and Sakata, H (1994) Deficit of hand preshaping after muscimol injection in monkey parietal cortex. NeuroReport, 5: 1525-1529. Ghez, C and Sainburg, R (1995) Proprioceptive control of interjoint coordination. Can. 1. Physiol. Pharmacol., 73: 273-284. Gribble, PL and Ostry, OJ (1999) Compensation for interaction torques during single- and multijoint limb movements. J. Neurophysiol., 82: 2310-2326. Haaxma, Rand Kuypers, H (1974) Role of occipitofrontal cortico-cortical connections in visual guidance of reatively independent hand and finger movements in rhesus monkey. Brain Res., 71: 361-366. Haggard, P, Jenner, J and Wing, A (1994) Coordination of aimed movements in a case of unilateral cerebellar damage. Neuropsychologia, 32: 827-846. Haken, H, Kelso, JA and Bunz, H (1985) A theoretical model of phase transitions in human hand movements. BioI. Cybem., 51: 347-356. Hollerbach, JM and Flash, T (1982) Dynamic interactions between limb segments during planar movements. Bioi. Cybem., 44: 67-77. Jakobson, LS, Archibald, YM, Carey, DP and Goodale, MA (1991) A kinematic analysis of reaching and grasping movements in a patient recovering from optic ataxia. Neuropsychologia, 29: 803-809. Jeannerod, M (1981) Intersegmental coordination during reaching at natural visual objects. In: J Long and A Baddeley (Eds.), Attention and Performance IX. Hillsdale, NJ: Lawrence, pp. 153-169. Jeannerod, M (1984) The timing of natural prehension movements. 1. Mot. Behav., 16: 235-254. Jeannerod, M (1986a) The formation of finger grip during prehension. A cortically mediated visuomotor pattern. Behav. Brain Res., 19: 99-116. Jeannerod, M (1986b) Mechanisms of visuomotor coordination: a study in normal and brain-damaged subjects. Neuropsychologia, 24: 41-78. Jeannerod, M (1988) The neural and behavioural organization of goal-directed movements. Oxford: Oxford University Press. Kay, BA, Kelso, JA, Saltzman, EL and Schoner, G (1987) Space-time behavior of single and bimanual rhythmical movements: data and limit cycle model. J. Exp. Psychol. Hum. Percept. Perform., 13: 178-192.
201 Kelso, JAS (1981) On the oscillatory basis of movement. Bull. Psychon. Soc., 18. Kelso, JAS (1984) Phase transitions and critical behavior in human bimanual coordination. Am. J. Physiol., 246: R 1000-1 004. Kelso, JAS and Tuller, B (1984) A dynamical basis for action systems. In: MS Gazzaniga (Ed.), Handbook of Cognitive Neuroscience. New York: Plenum, pp. 321356. Kunesch, E, Binkofski, F and Freund, HJ (1989) Invariant temporal characteristics of manipulative hand movements. Exp. Brain Res., 78: 539-546. Kunesch, E, Binkoski, F, Steinmetz, H and Freund, H-J (1995) The pattern of motor deficits in relation to the site of stroke lesion. Eur. Neurol., 35: 20-26. Lacquaniti, F, Guigon, E, Bianchi, L, Ferraina, S and Caminiti, R (1995) Representing spatial information for limb movement: role of area 5 in the monkey. Cereb. Cortex, 5: 391-409. Levin, MF (1996) Interjoint coordination during pointing movements is disrupted in spastic hemiparesis. Brain, 119: 281-293. Massaquoi, S and Hallett, M (1996) Kinematics of initiating a two-joint arm movement in patients with cerebellar ataxia. Can. J. Neurol. Sci., 23: 3-14. Mechsner, F, Kerzel, D, Knoblich, G and Prinz, W (2001) Perceptual basis of bimanual coordination. Nature, 414: 69-73. Morasso, P (1983) Three dimensional arm trajectories. Bioi. Cybem., 48: 187-194. Muller, F, Kunesch, E, Binkofski, F and Freund, H-J (1991) Residual sensorimotor functions in a patient after right-sided hemispherectomy. Neuropsychologia, 29: 125-145. Paulignan, Y and Jeannerod, M (1996) Prehension Movements. The Visuomotor Channels Hypothesis Revisited. In: AM Wing, P Haggard and R Flanagan (Eds.), Hand and Brain. San Diego: Academic Press, pp. 265-282. Pause, M, Kunesch, E, Binkofski, F and Freund, HJ (1989) Sensorimotor disturbances in patients with lesions of the parietal cortex. Brain, 112: 1599-1625. Poizner, H, Mack, L, Verfaellie, M, Rothi, LJG and Heilman, KM (1990) Three-dimensional computergraphic analysis of apraxia. Brain, 113: 85-101. Poizner, H, Clark, MA, Merians, AS, Macauley, B, Rothi, LJG and Heilman, KM (1995) Joint coordination deficits in limb apraxia. Brain, 118: 227-242. Reich, J and Daunicht, WJ (2000) A rigid body model of the forearm. J. Biomech., 33: 1159-1168. Sainburg, RL, Poizner, H and Ghez, C (1993) Loss of proprioception produces deficits in interjoint coordination.1. Neurophysiol., 70: 2136-2147.
202 Sainburg, RL, Ghilardi, MF, Poizner, H and Ghez, C (1995) Control of limb dynamics in normal subjects and patients without proprioception. J. Neurophysiol., 73: 820-835. Seidler, RD, Alberts, JL and Stelmach, GE (2001) Multijoint movement control in Parkinson's disease. Exp. Brain Res., 140: 335-344. Soechting, JF and Lacquaniti, F (1981) Invariant characteristics of a pointing movement in man. J. Neurosci., 1: 710-720. Soechting, JF and Terzuolo, CA (1988) Sensorimotor transformations underlying the organization of arm
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movements in three-dimensional space. Can. J. Physiol. Pharmacol., 66: 502-507. Soechting, JF and Flanders, M (1989a) Sensorimotor representations for pointing to targets in threedimensional space. J. Neurophysiol., 62: 582-594. Soechting, JF and Flanders, M (1989b) Errors in pointing are due to approximations in sensorimotor transformation. J. Neurophysiol., 62: 595-608. Wiesendanger, M, Kaluzny, P, Kazennikov, 0, Palmeri, A and Perrig, S (1994) Temporal coordination in bimanual actions. Can. J. Physiol. Pharmacol., 72: 591-594. Winter, DA (1990) Biomechanics and motor control of human movement. New York: Wiley.
191 CHAPTER 14
Kinesiology Christian Dohle* and Hans-Joachim Freund Department ofNeurology, University Hospital Dusseldorf, D-40225 Dusseldorf, Germany
14.1. Overview In motor control studies, recordings of the kinematics and dynamics of movement can be employed at a high level of sophistication. These techniques have been applied for research, for the study of pathophysiology and for diagnostic purposes. In clinical settings, their use is still restricted because they are expensive, time consuming and require skilled technical personnel. Another reason is that better insights into movement kinematics are often not critical for differential diagnosis or therapeutic decisions. The situation is going to change as rehabilitative strategies and procedures become more specific. The previously used qualitative or semiquantitative scores are increasingly complemented by electrophysiological and kinematic recordings. Kinematic techniques will therefore be required for both the study of normal motor behavior and for restorative neurology. Neuroimaging techniques are providing new insights into brain function. They produce high resolution, spatial information about brain structure and functional activation states during a broad range of cognitive and sensorimotor behaviors. In contrast to the highly sophisticated imaging techniques the assessment of behavior is often poorly controlled. For motor control, this is partly due to the restricted mechanical conditions for measurement in the scanners or to interference with the magnetic devices. This situation is just about to change as more recording techniques are available for application in neurofunctional studies. This article gives an outline of the most commonly used kinematic recording techniques and their
* Correspondence to: Dr. C. Dohle, Neurologische Klinik, Universitatsklinikum Dusseldorf, MoorenstraBe 5, D-40225 Dusseldorf, Germany. E-mail address:[email protected] Tel.: +49-211-81 18678; fax: +49-211-81 18485.
methodical backgrounds. The major emphasis is put on the analysis of upper limb movements, as gait analysis is dealt with in a separate chapter.
14.2. Technical principles Depending on the purpose of the movement study, different movement recording systems can be used. One-dimensional systems, such as goniometers or lever constructions can record only one degree of freedom - usually unidirectional limb movements. In contrast, three-dimensional recording systems allow the recording of the entire movement of a limb in three-dimensional space. Both systems have specific advantages. One-dimensional techniques are simpler and cheaper and often serve the desired purpose. Three-dimensional movement recording systems are usually marker-based: the position of (active or passive) markers that are fixed to certain body parts is recorded by detectors. Their position in three-dimensional space has to be calibrated before the measurement. In principle, two different types of systems can be distinguished: (a) Active systems with markers emitting a signal (usually infrared light) in well-defined time intervals which is recorded by CCD cameras (e.g. Optotrak, Selspot (now out of business». (b) Passive systems with markers consisting of colorful or light-reflecting material. Their position is recorded by video- or infrared-based camera systems (e.g. Elite, MacReflex, Vicon). Usually, the recording procedure is based on infrared light. Active systems make use of infrared emitting diodes (LEOs) that are recorded by sensitive cameras. In passive systems the camera is coupled with an array of infrared emitting diodes, and the light is reflected by the markers. The most prominent problem of both systems is noise due to reflections from extraneous shiny surfaces. Other systems can detect markers directly from video recordings with-
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out any additional technical setup. In principle, these systems are very versatile as they do not require any extensive technical set-up. On the other hand, they require considerable manual post-processing and assignment of the markers at the beginning of each recording session. These systems are also much more viable to optical interferences. A completely different technique is based on ultrasound (ZEBRIS). The markers consist of small ultra-sound emitting sources and the signals are recorded by an array of three microphones fixed on a rack at well-defined distances to each other. Although these markers make use of small piezo elements to generate ultrasound, they barely emit electromagnetic waves, so that they can also be used in environments that are sensitive to electromagnetic 'noise' such as magnetoencephalography or magnetic resonance tomography. On the other hand, the system has only a limited range and is very liable to acoustic noise. Common to all these systems is the limitation that one camera can only detect positions in two dimensions. Thus, at least two cameras are necessary in order to obtain a three-dimensional position. Other systems are available that make use of different technologies. Gained from developments for the market of virtual reality techniques, electromagnetic tracking devices are partly employed for movement analysis (mainly Polhemus, Flock of Birds). In these systems, one emitter generates a static electromagnetic field of a certain dimension. The sensor consists of a coil that indicates position and orientation of the moving part relative to the emitter. In principle, these systems are rather cheap, very accurate and can provide both position and orientation of a marker in real-time. On the other hand, they are very liable to any type of metal within the range of the emitter as well as electromagnetic 'noise', e.g. of nearby located large computer monitors. All systems have specific advantages and disadvantages. Active systems are usually more expensive. For measurements, it is necessary to 'wire' the subjects, i.e. to connect the marker and the control unit. This ensures that every marker is always uniquely defined, even if it is occluded during certain measurement epochs. Passive measurement systems are usually cheaper and the measurement is easier to realize, because they do not require direct connections of the markers
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and the control unit. The major drawback of these systems is the ambiguity of marker identification. Consequently, the analysis of every trial has to be preceded by a manual assignment of markers that are then tracked further by the analysis software. Depending on the type of movement, marker trajectories can cross - at least in the field of view of one of the cameras. In this case, the marker assignment has to be corrected manually. In summary, passive systems are cheaper and easier in the initial acquisition of data. This advantage, however, has to be counterbalanced against a much higher demand in manual post-processing. All measurement systems of this type deliver time series of X-, Y: and z-coordinates as a result, that are sampled at certain frequencies. The further analysis of these data is poorly standardized, so that not many commercial programs are available. Usually, different laboratories make use of different, custom-made software packages. A good (and still valid) overview of the general principles of human movement analysis can be found in Winter (1990). Since this review is mostly restricted to applications in neurophysiology and sports medicine, we will mainly concentrate on the analysis of patients. Such recordings offer two advantages: They can quantify certain aspects of motor behavior and disclose qualitative features that cannot be detected otherwise.
14.3. Coordinate systems As outlined above, the majority of three-dimensional movement measurement systems provide (Cartesian) X-, y- and z-coordinates in time. The point of origin and the orientation of these coordinates is defined by a calibration procedure that has to be performed prior to the measurement. The calibration should be adjusted to match the coordinate axes to the main axes of the movement task to be recorded. Generally, the x- and y-axis define the floor or table plane with the z-axis pointing upwards. Although it is clear that the brain does not use cartesian coordinates for its computations, it is less clear what type of coordinates are used instead. In their pioneering studies on goal-directed movements, Soechting and Flanders (Soechting and Terzuolo, 1988; Soechting and Flanders, 1989a, b; Flanders et al., 1992) analyzed the variability of upper-limb pointing movements directed at targets in
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three-dimensional space. From their analysis, they proposed that arm movements are organized in a polar system based at the respective shoulder, having two angles (yaw and elevation) and one length (distance) as coordinate values. This proposal was theoretically based and inferred solely from kinematic measurements. Recent cell recordings in the awake monkey, however, demonstrated different cell populations responding specifically to the variation of one of the three values (Lacquaniti et al., 1995). On the other hand, a study of sensorimotor transformation errors in patients with lesions of the inferior parietal lobule showed dissociations of distance and direction errors, supporting the idea of different central processing modules for these two variables (Darling et al., 2001). 14.4. Levels of observation When subjects are required to write their signature either on a sheet of paper with a fine pen or on a blackboard with a piece of chalk attached to a broomstick, the individual features of their signature remain basically preserved - even when the writing is performed with the foot or a pen between their teeth. This phenomenon is called motor equivalence and neatly demonstrates that the brain is capable to transfer a given movement blueprint into a distinct kinematic pattern, irrespective of the biomechanical requirements. This is accomplished by converting the movement scheme into joint torques and muscle activation patterns specific for the limb that is actually performing the movement (inverse dynamics). For the analysis of human movements, this implies that movement analysis can be performed at different levels of observation: (1) Examination of (one-dimensional) limb angles.
This is particularly useful in the consideration of intra- and inter-limb coupling. When trajectories are recorded in using threedimensional measuring systems, more information is available about the position of the entire limb in space. Thus, additional levels of observation can be analyzed. (2) Examination of trajectories in three-dimensional space (kinematics). (3) Examination of multi-joint angles (limb configuration)
(4) Examination of joint torques (dynamics) Obviously, these considerations mainly apply to whole limb movements, i.e. the analysis of arm movements or gait. As gait analysis is covered in a separate chapter of this volume, the following analysis will focus on upper limb movements. Especially, we will demonstrate how the different levels of observation have been used to determine features of normal and pathological movements in patients with neurological disorders. 14.5. Analysis of patient populations The optimum strategy for the different applications depends whether the kinematic analysis should either elaborate previously defined patient's profiles or to allow the identification of different subpopulations of patients based on the kinematic data. In the first case, the mean values of pre-assigned kinematic parameters are determined for certain, apparently homogenous patient groups. These data can then be referenced either to those of normal subjects, or - if the type of disturbance only affects one side of the body - to the 'unaffected' body side. This procedure can only be applied if the patient groups are well defined and homogenous, such as hemiparkinsonism or hemiparesis. Conversely, kinematic data can be used to identify subgroups of patients with similar kinematic abnormalities and then to examine a possible correlation with distinct lesion localizations. This approach is more demanding, because the identification of the respective kinematic measures that are suitable to differentiate 'affected' from 'not affected' patients requires extensive prior research. 14.6. Temporal kinematics The kinematic analysis of recorded movements is exclusively based upon the trajectory of the recorded body segment, e.g. the hand or the fingertip. As the movement recording is usually performed at sampling frequencies around 100 Hz, it produces a large number of data points. In order to compare the trajectories of one person under different experimental conditions or trajectories of different patient populations it is necessary to decrease the dimensions of the trajectory, i.e. to parameterize the movement. One way to parameterize movements is the determination of fixed temporal 'landmarks'. This approach was extensively used in the analysis of
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grasping movements by Jeannerod and his coworkers (Jeannerod, 1981, 1984, 1988). He focused on hand velocity as a measure for the (proximal) reach component of the arm and grip aperture as a measure for the (distal) grasp component of the fingers. This work demonstrated the independence of these two components, by studying the times of maximum values of the two measures as representatives of the two components of the prehension movement (for review, see Paulignan and Jeannerod, 1996). The independence of these two components was the focus of many subsequent studies in normal subjects. In a typical paradigm in normal subjects, a determinant of one of the two components was varied (e.g. object size) and its effect on the second component (e.g. time of maximum hand velocity) was studied. Further support for the independent organization of these two components came from patients with focal cerebral lesions, especially of the posterior parietal cortex. Single case reports confirmed the role of the posterior parietal cortex for these types of sensorimotor transformations (Jeannerod, 1986a, b; Pause et al., 1989; Jakobson et al., 1991). In a first systematic study in patients with lesions of the parietal cortex, presenting the clinical picture of optic ataxia, Perenin and Vighetto showed deficits in the transport component of both arms in the left visual field following right parietal lesions ('field effect'). On the other hand, the transport component of patients with optic ataxia due to left parietal lesions was not only found to be impaired in the right visual field, but additionally also with all movements of the right arm in the left visual field and with central fixation ('hand effect'). The grasping component was tested for by adjustment of hand orientation in the frontal plane, following the paradigm of Haaxma and co-workers (Haaxma and Kuypers, 1974). Deficits in hand orientation showed the same distribution as deficits in the transport component, so that in this condition no dissociation between the two components could be demonstrated. It should be noted, however, that hand orientation is a kind of intermediate task that has to be accomplished in a synergy of proximal and distal joint angles (Dohle et al., 1995; Desmurget et aI., 1996). A kinematic study of prehension movements in patients with parietal lesions (Binkofski et al., 1998b) demonstrated an impairment of the grasp task but not of the reach component, supporting the
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concept of two independent visuomotor channels. This finding is of special interest as it corresponds to experimental inactivation studies in monkeys where the transport and the grasp component could be impaired independently (Gallese et aI., 1994). In an attempt to identify measures for disturbances of apraxic arm movements, Poizner and co-workers applied the approach of comparing temporal landmarks on repetitive movements. In the analysis of the times of maximum hand velocity and minimum hand path curvature during each stroke of a cyclical arm movement, the decoupling of these two measures was proposed to indicate a specific deficit in apraxic movements (Fig. 1, Poizner et al., 1990). Such interpretations have to be met with caution by two reasons. First, the underlying mathematics is not clear. Based on basic differential geometry (Morasso, 1983; Bartsch, 1986), the absolute value of hand path curvature c(t) at a given point of the three-dimensional trajectory r(t) (with its temporal derivatives velocity r'(t)=v(t) and acceleration r'(t)=a(t)) is calculated as: c(t)
=1(r' (r) x r'(t))/I r' (t) 131 =1(v(t) x a(t))/I v(t) 131
In other words, the value of curvature is calculated as a derivative of hand velocity. This fact on its own makes it highly unlikely that the two measures can be varied by the central commands independently. Second, Poizner's analysis was based mainly on the consideration of maximums and minimums of the time course of velocity and curvature. In normal subjects' movements, no significant time delay was observed between the timing of these extreme values. In apraxic patients, however, their occurrence did not correlate at all. Inspection of the raw data clearly shows that the movements were fragmented and consisted of several movement segments. It is clear, that this type of noisy data produce more extreme values that do not match each other. This leaves the reader wondering what is the phenomenom of decoupling of tangential velocity and hand path curvature. 14.7. Temporal aspects of movement organization
Quantitative measurements can provide insights in new principles of organization. An example are recordings from hand and finger movements that
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Fig. I. Velocity - curvature coupling during carving movements in an apraxic patient (right) in comparison to a normal subject (left). Upper panels: tangential velocity (mis, broken lines) and radius of curvature (m, continuous lines). Lower panels: time correspondences between velocity minima and radius of curvature minima (from Poizner et aI., 1990).
provided evidence for a clear cut functional dichotomy of manipulative hand and finger movements. On a qualitative basis, movements of the hand as a whole used for writing, shading and hammering had been previously classified as extrinsic and movements of the fingers against each other or the palm as intrinsic hand movements. Quantitatively, it has been demonstrated that these two types of hand movements are performed at distinctly different preferred frequencies. Extrinsic hand movement are usually performed at 4-7 Hz (type I) and intrinsic hand movements at 1-1.5 Hz (type II; Kunesch et aI., 1989). Surprisingly, there is no overlap between the two ranges (Fig. 2). Since the difference in their characteristic frequencies cannot be explained by mechanical reasons it has been concluded (Kunesch et aI., 1989) that the grouping into distinct frequency ranges represents different types of sensorimotor interactions. The slower type I movements require a continuous sensory guidance for their execution and must be carried out slowly in order to meet the time requirements for the detailed sensory analysis. The faster type II movements are rapid automated subroutines that are largely predictive and require only some crude sensory monitoring.
The observed grouping at preferred frequencies fits well into the more general scheme that most of our repetitive motor behaviors such as speaking, eyemovements during reading, chewing, walking, swimming or dancing is performed at characteristic frequencies. Central pattern generators in the brainstem play an important role for their generation. Therefore, the assessment of the temporal aspects of motor organization is only possible on the basis of quantitative movement analysis.
14.8. Spatial kinematics Apart from determining the timing of certain landmarks during the movement, the spatial position of some body part at the time is of interest as well. For prehension movements, this approach was first proposed by Haggard and co-workers (Haggard et aI., 1994). In their study, it could be demonstrated that the aperture during a prehension movement was not only scaled on the basis of the temporal pattern of the prehension movement, but also depending on the spatial position of the hand in the entire trajectory. In a patient with cerebellar damage, the strategic trial-to-trial coordination seemed to be
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Fig. 2. Distribution of the frequencies at which finger movements are performed during four tactile exploratory tasks (left) and of hand movements during execution of three normal skills (right) (from Kunesch et aI., 1989).
preserved in spite of gross abnormalities of the arm trajectory (Haggard et al., 1994). Another spatial feature of arm trajectories is preservation of the movement plane, which was presented by Poizner and colleagues as a typical feature of movements in apraxic patients (Poizner et al., 1990). In their study, it could be shown that the plane of motion in cyclical arm movements shows a high inter-trial variability when compared to those of normal subjects. These findings convincingly characterize the deranged movements. However, similar deficits were later shown to occur in deafferented patients (Sainburg et al., 1993), raising the question of specificity of this finding. 14.9. Limb and body configuration
The analysis methods presented so far were only focused on the hand and the finger. However, the three-dimensional recording systems also offer the possibility to catch the movement of the entire limb,
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namely the upper arm and forearm. By adding two further markers at the shoulder and at the elbow, three further limb angles can be calculated: upper arm elevation, upper arm yaw and elbow angle (Soechting and Lacquaniti, 1981). This method does not take into account forearm pronation or supination, which can simply be regarded as a radial segment rotating around an ulnar segment (Reich and Daunicht, 2000). Its calculation out of recorded kinematic data requires a further marker on the forearm defining the transversal axis of the hand joint in space. Soechting and Lacquaniti's pilot studies in normal subjects demonstrated that upper arm elevation (usually referred to as shoulder elevation) and elbow angle are tightly coupled during a pointing (or grasping) task (Soechting and Lacquaniti, 1981). Subsequently, this relationship was found to be disturbed in a variety of neurological disorders, such as deafferentation (Sainburg et al., 1993; Ghez and Sainburg, 1995) (Fig. 3), apraxia (Poizner et al., 1995), in patients with parietal lesions with and without apraxia (Binkofski et al., 1998a), cerebellar ataxia (Bastian et al., 1996; Massaquoi and Hallett, 1996), hemiparesis (Levin, 1996; Beer et al., 2000; Cirstea and Levin, 2000) or Parkinson's disease (Seidler et al., 2001). The authors of these studies noted the similarities of their findings, but it remains unclear if the loss of interjoint coupling is a specific pathological phenomenon, or rather some epiphenomenon that is to be found in different types of deranged arm movements. 14.10. Dynamics
When both information about the joint angles and estimates about the inertial mass of the body segments (e.g. Winter, 1990) are available, inverse dynamics equations can be applied to calculate the joint torques that are necessary to produce the observed movements. Different authors have used different terminologies (Soechting and Lacquaniti, 1981; Hollerbach and Flash, 1982; Bastian et al., 1996), but in general, one has to distinguish (according to the terminology used by Bastian and co-workers): • Net torque: sum of all the torques acting at a joint.
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Control 2
Fig. 3. Coupling of elbow and shoulder motions in two patients with deafferentation (middle and bottom) in comparison to a normal subject (top). The elbow flexion! extension angle has been plotted against the upper arm elevation (top) and yaw (bottom) angles for individual cycles of a four cycle movement (from Sainburg et al., 1993).
According to the formulas presented in Soechting and Lacquaniti (1981) and Bastian et al. (1996), this consists from the sum of:
• muscle torques: sum of all muscle and passive tissue forces that are necessary to generate the observed movement; • gravitational torque: torque produced by the force of gravity acting on the limb segments of the arm; • interaction torques: motion-dependent torques that occur at any joint due to movements that are linked. In normal subjects, it could be demonstrated that phasic EMG activity occurs in muscles solely acting at adjacent joints, even if those joints are mechanically immobilized (Gribble and Ostry, 1999). This was interpreted as experimental support for the theoretical proposal that central control signals to muscles are adjusted, in a predictive manner, to compensate for interaction torques. In only a very limited number of studies have these analysis techniques been applied to patient movements. In studies on patients with deafferentation (Sainburg et al., 1993, 1995), cerebellar ataxia (Bastian et al., 1996) and Parkinson's disease (Seidler et al., 2001) these interaction torques were found to be inadequately adjusted in order to generate smooth, straight movements. It remains unclear, however, if this deficit can be pinpointed to the abnormal control of interaction torques as result of a deficient processing of somatosensory information or whether this disturbance is the pure description of a lack of joint coordination at a different level of observation. Further studies are necessary to clarify this point. In a later study, Bastian and co-workers (Bastian et al., 2000) tried to exclude that this inaccurate adjustment of interaction torques in cerebellar patients was solely due to a general inability to generate sufficient levels of phasic torque. For this purpose, they studied practically identical upper-arm pointing movements in cerebellar patients, either with their shoulder free or their shoulder fixed (Bastian et al., 2000). Cerebellar patients changed the pattern of muscle activity fundamentally when changing between these two conditions, producing inadequately scaled interaction torques, due to deficits in coordination of multi-joint movements. 14.11. Manipulative hand movements
Apart from whole-limb movements of either arm, manipulative movements, such as those during exploration of small objects, can be recorded and
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analyzed quantitatively. In this case, movements of the fingertips of the thumb and the forefinger have to be recorded. Although movement paths are restricted to a narrow workspace, the same trajectory in space is never repeated (Kunesch et al., 1989). The temporal profile, however, shows a regular, periodic pattern (Fig. 4). Spectral analysis typically exhibits a single peak in the frequency spectrum between 1 and 2 Hz, showing high variability across, but not within different subjects (Kunesch et al., 1989). This is typically impaired in subjects with lesions of the posterior parietal cortex, showing deficits in the recognition of objects by haptic exploration (astereognosis). In these patients, the space of exploration is grossly enlarged and the mean frequency and the regularity of the movement is reduced (Kunesch et al., 1995; Binkofski et al., 2001). A more detailed analysis of subgroups of 3D-trajectories
these patients show that astereognosis is correlated with grossly enlarged space of exploration, but not with slower or more irregular movements per se (Binkofski et al., 2001).
14.12. Bilateral synergies The last paragraphs lead to another question: To which extent can different limb segments be controlled independently, especially between both sides of the body? The most basic model of coupled limb movements is that of rhythmic flexion and extension movements of the index fingers or hands of both arms, that are moving either in-phase or anti-phase. This paradigm was extensively studied by Kelso and co-workers who noted that in-phase movements are much more stable than anti-phase movements (Kelso, 1981, Movement<:haraeteflslics of thumb andindex finger
Movement characteristics of lhumb and indexfinger
Frequency spectrum
OIlhumb movemems
Frequencyspectrum 01 thumb movements
Fig. 4. Exploratory finger movements in two patients with focal cerebral lesions (middle and bottom) in comparison to a normal subject (top). Left panels: 3-D reconstruction of thumb (T) and index finger (FF) movement trajectories. Middle panels: scan paths of thumb and index finger. Right panels: frequency distribution of the thumb movements (from Binkofski et aI.,
2001).
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1984; Kelso and Tuller, 1984). Furthermore, if subjects perform externally paced anti-phase movements with increasing movement frequency, a sudden transition to in-phase movements occurs. These observations led to the Haken-Kelso-Bunz model, explaining the superior stability of the inphase pattern by a theoretical model of coupled oscillators (Haken et al., 1985). Although this model has been extensively studied and refined since then (Kay et al., 1987; Forrester and Whitall, 2000; Beek et al., 2002), it is only recently that the perceptual component of this phenomenon has come into the focus of attention. For example, it could be shown that the same stability of the in-phase pattern when compared to the anti-phase holds true when two people have to
synchronize their movements (Bingham et al., 1999). Finally, in a recent study Mechsner and coworkers studied bimanual finger movements when one hand was either pronated or supinated (Mechsner et al., 2001). Surprisingly, in this setup, the symmetrical movements of the fingers were also more stable than parallel movements when one of the two hands was pronated and the other supinated. In other words, the superior stability of the in-phase pattern only depended on the actual movement direction of the fingers, but not on the finger muscles involved. In a second experiment, the subjects had to synchronize two circular-moving handles. In one handle, however, the ratio of transmission was altered in such a way that three rotations of the hand resulted in four rotations of the handle (Mechsner et
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E
F
I~
I:n
I:n 1-------4
1.
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Fig. 5. Bilateral synchronous movements of shoulder (top), elbow (middle) and wrist (bottom) in a patient with right sided hemispherectomy. Left panels: movement cycles (right side: upper trace). Right panels: bilateral correlation (from Miiller et aI., 1991).
200 al., 2001). In this set-up, stability of in-phase circular movements was achieved flawlessly, further supporting the idea that it is the perception of in-phase pattern that is controlled rather than the implementation in the subjects' biomechanical system. All these experiments were performed with movements of distal parts of the arm (hand or finger movements) and synchrony was usually assessed by comparison of the inter-tap-times of each hand. In this analysis procedure, the actual movement trajectory is not considered, but only the final 'goal' (Wiesendanger et al., 1994). Analysis of phase diagrams comparing right- and left sided movements and calculating correlation coefficients between the entire trajectory of either side might help to understand the mechanisms of bilateral coordination in more detail. In a study on bilateral prehension movements using this approach, Dohle and coworkers could demonstrate different coupling mechanisms for the reach and the grasp component respectively (Dohle et al., 2000). Whereas the grasp (thus distal) component shows only little coordination during the movement, suggesting largely independent movement organization mechanisms of either side, the reach (thus proximal) component seems to be 'hard-wired' connected between both sides. This is compatible with a study in a patient with right-sided hemispherectomy, showing preserved coordination between proximal arm movements of either side, but largely independent, fractionated movements of more distal parts of the body (Muller et al., 1991) (Fig. 5).
14.14. Summary In this chapter, we have presented an overview about the different techniques and methods in kinesiology, especially with respect to their application in the movement analysis of neurological patients. A number of different disturbance patterns provide useful information about the characteristic changes in a particular pathological condition. Some of them are specific, while others reflect more general abnormalities.
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