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1 Sensorimotor Control Laboratory, University of Minnesota, Minneapolis, Minnesota 55455 2 Department of Neurology, University of Minnesota, Minneapolis, Minnesota 55455 3 Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455 4 Department of Neurology, University of Duisburg-Essen, 45122 Essen, Germany
Submitted 10 June 2003; accepted in final form 10 September 2003
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ABSTRACT |
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INTRODUCTION |
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; Martin et al. 1996; Maschke et al. 2000a,b; Timmann et al. 2000; Weiner et al. 1983; Woodruff-Pak 1997). In addition, the cerebellum is said to be involved in the learning of complex skills as shown by many functional imaging and behavioral studies (Deuschl et al. 1996; Doyon et al. 2002; Flament et al. 1996; Friston et al. 1992; Imamizu et al. 2000; Miall et al. 2001; Nezafat et al. 2001; Penhune and Doyon 2002; Seitz and Roland 1992; Seitz et al. 1994; Topka et al. 1998b). However, the exact function of the cerebellum in implicit motor learning is still matter of debate. For example, system-theoretic approaches suggested that the acquisition of an internal model of the limb dynamics serves as the basis of skill learning (Kawato et al. 1987; Shadmehr and Mussa-Ivaldi 1994; Wolpert et al. 1995) and that the cerebellar cortex appears to participate in acquisition and storage of these internal models (Imamizu et al. 2000; Nezafat et al. 2001; Kawato and Gomi 1992; Kitazawa et al. 1998; Shadmehr and Holcomb 1997; Shidara et al. 1993; Wolpert et al. 1998). This claim for a prominent role of the cerebellum in sensorimotor learning is contrasted by recent evidence demonstrating that the cerebellum is important for the expression of an improved motor performance during skill learning but might not have been involved in the learning process itself (Seidler et al. 2002).
In light of this discussion, it is noteworthy that only a few studies investigated patients with cerebellar disorders during motor learning of novel situations such as throwing while looking through prisms (Martin et al. 1996; Weiner et al. 1983). While it seems that an intact cerebellar cortex is necessary for visuomotor adaptation, it remains unclear how adaptation to an unknown force field is mediated by the cerebellum. From an anatomical point of view, one may presume that the cerebellum plays a role in force adaptation given that the cerebellum receives major sensory input from the spinocerebellar tracts (Bloedel and Courville 1981; Bosco and Poppele 2001). Likewise, a recent imaging study in which subjects performed goal-directed arm movements in an external force field revealed an activation of the ipsilateral cerebellar cortex during the initial adaptation, whereas a region within the anterior cerebellar cortex showed a decrease in activity during long-term adaptation (Nezafat et al. 2001). These results hint at a cerebellar involvement during force adaptation.
The present study seeks to systematically determine the dependency of adaptation of goal-directed arm movements to an unknown force field on the integrity of the cerebellum. We choose to investigate this paradigm in patients with spinocerebellar ataxia (SCA) type 6 and type 8. Both SCA types are genetically caused by trinucleotide repeat expansions and characterized as slowly progressive, pure, or predominately cerebellar ataxia (Day et al. 2000; Gomez et al. 1997). Autopsy studies have revealed intact dorsal columns and spinocerebellar tracts and nerve conduction studies have shown only a mild sensorimotor neuropathy in lower but not upper limb nerves in SCA6 (Gomez et al. 1997). Moreover, patients with SCA8 do not present signs of sensory nerve or tract involvement (Day et al. 2000). Thus proprioceptive arm afferents are intact in SCA6 and SCA8 patients. Therefore possible adaptation deficits found in these patients would be not due to disturbed sensory input from the periphery. We hypothesized that SCA patients would exhibit difficulties in force-field adaptation, which could not be explained by motor control deficits alone. Furthermore, if an intact cerebellum is essential for this form of adaptation then progressive cerebellar degeneration ought to lead to decreased learning, generalization, and retention.
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METHODS |
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Nine patients with degenerative cerebellar disorders (age 46.2 ± 7.5 (SD) yr, range 38–63 yr, 4 females, 5 males) and nine age- and sex-matched healthy control subjects (age 46.2 ± 7.1 yr, range 38–59 yr, 4 females, 5 males) with no neurological or general medical limitations participated. All patients were recruited from the cerebellar ataxia outpatient clinic at the University of Minnesota and were diagnosed as having a genetically defined spinocerebellar ataxia (SCA) either type 6 (n = 5) or type 8 (n = 4). They had a moderate to severe cerebellar ataxia based on the International Cooperative Ataxia Rating Scale of the World Federation of Neurology (WFN scale) (Trouillas et al. 1997). Main symptoms were gait and stance instability, limb ataxia predominantly involving the lower limbs, cerebellar dysarthria, and a variable severe cerebellar oculomotor dysfunction (gaze-evoked nystagmus, saccadic dysmetria). Neurological examination including sensory testing (vibration sense, light touch, pinprick sensation, and position sense at index finger and first toe) revealed no extracerebellar signs such as peripheral nerve disease or motor neuron involvement. All patients were right-handed based on results of the Edinburgh Handedness Inventory (Oldfield 1971). Eight control subjects were right-handed and one left-handed. Descriptive characteristics of patients are summarized in Table 1. All patients and healthy subjects gave informed consent. The study was approved by the institutional review board of the University of Minnesota.
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Subjects were asked to move a cursor (cross with a length of 0.5 cm) on a monitor screen (size: 30.8 x 23 cm) by moving a robot arm manipulandum with two translational degrees of freedom for elbow and forearm motion (Fig. 1A). The manipulandum control was implemented through a UNIX and PC workstation. During testing, subjects sat on a chair in front of the manipulandum. Shoulder straps restrained forward movements of the shoulders. The right shoulder was aligned with the starting circle on the screen and with the torque engines of the robotic arm. The distance to the manipulandum and chair height was individually adjusted to assure that the initial arm posture was almost identical for all subjects at the holding position (upper limb to forearm in clockwise direction: patients: 104.4 ± 2.8°, controls: 104.8 ± 1.5°).
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The paradigm consisted of a center-out task to selected targets. Subjects had to bring the cursor within a starting circle (
2 cm; Fig. 1B). Once the cursor was within the starting circle, its color turned to yellow, and a smaller target ( 0.5 cm) appeared in yellow. The disappearance of the starting circle and the change of the target color to green represented the go signal. Subjects were instructed to move the cursor fast, accurate and in a straight line to the target at either 45 or 90° or to 0, 30, 60, 120, and 150°. The path length from the starting circle to each of the targets was 10 cm on the monitor (Fig. 1B). The experiment was divided into five blocks each consisting of 120 trials (Fig. 1C). Within each block the targets were presented in a pseudorandomized order. In the first block, subjects learned to move to the two targets with no external force present (baseline condition). During the next three blocks, a velocity-dependent force field was produced through the torque motors of the manipulandum. The force field was defined as (F = force applied on the handle, v = resulting velocity)
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In the adaptation (block 2), generalization (block 3), and retention conditions (block 4), unperturbed trials, so called catch trials, were interspersed every 10th trial. These catch trials produced a displacement of the movement in the opposite direction of the displacement observed in perturbed trials and were used to obtain a measure for the degree of learning related aftereffects. During the second and fourth block, catch trials were performed to either 45 or 90° targets. In contrast, during the third block (generalization condition), catch trials consisted of movements to 0, 30, 60, 120, and 150°. After the generalization condition, subjects had a 3-h lunch break before the start of block 4 (retention condition). The fifth and last block tested the unperturbed performance to targets used to test generalization (i.e., 0, 30, 60, 120, and 150° targets).
Data analysis
The position and velocity of the manipulandum handle in the transverse plane were recorded for each trial and subsequently analyzed using Matlab and SAS. To obtain spatial information of the trajectories the maximal perpendicular displacement (YDmax) in the y direction for each trial was calculated. Therefore the time-position series of each trial to the 45° target was rotated by 45° to align the x axis to the 45° target. Cerebellar patients are known to produce large endpoint errors due to their intention tremor and dysmetria. To avoid erroneous amplification of the YDmax by these endpoint errors, we only analyzed the first 75% of the individual trial length, which represented the major portion of the transport phase. To provide information about the time course of learning, we calculated an adaptation index (AI) (Criscimagna-Hemminger et al. 2003), which combined the mean of the YDmax of 10 successive perturbed trials to one target (i.e., 45 or 90°) and the YDmax of one catch trial to the same target, which was interspersed in these 10 trials
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Because a total of 10 trials to each target were interspersed in the second block (adaptation condition) and in the fourth block (retention condition), 10 AIs were obtained for each target. An analysis of covariance (ANCOVA) was separately performed for each target with AI (1st and 5th AI of the adaptation and retention) as the dependent variable (group = between-subjects factor, mean peak velocity of a bin of 10 successive trials as covariate). The perpendicular displacement of catch trials to targets used to test the ability to generalize learning was compared with the baseline performance of the last 16 movements to these targets obtained in the fifth block (adjusted perpendicular displacement for each target = mean perpendicular displacement of catch trials minus mean perpendicular displacement of 16 trials during baseline performance). A within-group ANOVA was performed with adjusted perpendicular displacement of each target as dependent variable and target as within-subject factor.
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RESULTS |
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The first part of the experiment tested how patients with cerebellar degeneration and healthy controls adapted to an unknown force field during the execution of goal-directed arm movements. Figure 2 shows examples of hand trajectories that are representative of the performance of the cerebellar patients and the control group. In the baseline block, movements of the control subject and the two patients were relatively straight (Fig. 2, A–C). However, the patient with severe ataxia exhibited a higher variability and pronounced endpoint errors (Fig. 2C). When exposed to a force field, hand-trajectory formation of all subjects was immediately affected demonstrating a right- and downward deviation. While the control subject straightened his trajectories during subsequent trials, both patients showed a decomposed movement with little or no signs of straightening in successive trials regardless of the severity of their ataxia (Fig. 2, D–F).
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Generalization of learning
After the adaptation condition, the ability to generalize the adapted movement to five different targets outside the learned workspace was tested (generalization condition). Control subjects revealed prominent after-effects to targets near to the trained workspace, i.e., to the 60° and in a lesser amount to the 30° target (Fig. 7) as expressed by the perpendicular displacement adjusted to the baseline performance to these targets. After-effects to targets decreased with further distance (0, 120, and 150°). In contrast, cerebellar patients did not exhibit clear differences between movements to targets used during the adaptation block and targets presented during the generalization block (Fig. 7). The difference between SCA patients and control subjects was statistically significant (P < 0.01).
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After a consolidation block, subjects had a break of 3 h before beginning with the second portion of the experiment that tested the retention of force adaptation. Exemplar trajectories of a control subject and two patients are shown in (Fig. 2, D, G, E, F, H, and I). The group analysis revealed intact retention in the control group but reduced or absent retention in the cerebellar group. Mean AI of the first bin of retention block was higher than the mean AI of the first bin of the adaptation block in control subjects for movements to the 45° target (control group means: 0.36 vs. 0.59), whereas in cerebellar patients an inverse relationship was obtained (patient group means: 0.46 vs. 0.21; Fig. 4A). This difference was significant (ANCOVA: group x bin interaction effect: P = 0.003).
In contrast, the AI at the beginning of the retention appeared to be similar between control subjects and cerebellar patients to the 90° target (Fig. 4B). The mean AI of the first bin of the retention block was higher than the first bin of the adaptation block in both groups [control group means: 0.37 vs. 0.55, patient group means: 0.44 vs. 0.53; P > 0.05 (ANCOVA)].
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DISCUSSION |
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Increased cerebellar degeneration progressively impairs force adaptation
Results of the present study underline the role of the human cerebellum in adaptive processes as shown by studies investigating prism adaptation in patients with cerebellar lesions (Martin et al. 1996; Weiner et al. 1983) and complement results of animal experiments (Baizer and Glickstein 1974; Baizer et al. 1999). Thus it appears that the cerebellum is not only necessary for a visually driven adaptation but also involved in adaptive processes that rely to a larger extent on proprioceptive signals. Our finding of an impaired force adaptation in cerebellar patients further corroborates recent research showing that a decline in movement errors during early force adaptation is associated with a decrease in blood flow of the ipsilateral posterior cerebellar cortex (Nezafat et al. 2001). However, from results of a recent fMRI study, it was argued that the cerebellum contributes to motor performance but not to motor learning itself (Seidler et al. 2002). Against the background of this finding, one may speculate that the impaired force adaptation of cerebellar patients in the present study merely represents performance deficits. Obviously, our patients exhibited kinematic deficits, as shown by decomposed movements, larger endpoint errors, low movement velocities and longer movement times (Table 2, Fig. 2). Such low velocities translated into smaller forces at the handle. In healthy individuals, lower perturbation forces lead to faster adaptation. This was not observed in our patient group. On the contrary, although generally exposed to smaller forces, learning was not accelerated but revealed clear kinematic deficits and had a longer time course. Hence, we here saw learning deficits that cannot solely be explained by deficits in motor performance. The view that the cerebellum is indeed involved in motor adaptation and not only important for the expression of learning is supported by the fact that infarction of the territory of the posterior inferior artery (PICA) did prevent adaptation of movements to the wearing of prisms (Martin et al. 1996). Interestingly, PICA infarction is not accompanied by a relevant ataxia of arm movements. In contrast, patients with severe ataxia due to ischemic stroke within the territory of the superior cerebellar artery (SCA) were still able to adapt to prisms. This dichotomy suggests a functional compartmentalization of adaptation and motor control processes in the human cerebellum.
The progression of cerebellar ataxia quantified by the WFN ataxia scale was highly correlated with a progressive failure of force adaptation. Given that the clinical worsening of the disease signals a progressive Purkinje cell loss, our data show that cerebellar degeneration progressively impairs the ability of humans to adjust motor commands to changes in the environmental forces. Our finding that force adaptation was not abolished in patients with less severe ataxia but was massively impaired in those patients with a higher WFN ataxia score (Fig. 5) becomes plausible, if one considers that those areas that are active during force adaptation (cerebellar cortex of the posterior lobe and lateral cerebellum) (Nezafat et al. 2001) only show mild to moderate Purkinje cell loss in SCA 6 patients (Gomez et al. 1997). Future studies should clarify whether this paradigm is suitable to detect subtle cerebellar dysfunctions in asymptomatic SCA gene carriers and may serve as a surrogate marker for decline in cerebellar function in long-term follow-up of SCA patients. This would be interesting particularly with regard to the expanding knowledge about underlying disease mechanisms in SCA, which might led to therapeutic options in future.
As a final point, the incomplete force-adaptation in cerebellar patients cannot easily explain, why the adaptation index in the cerebellar group dropped off after the first bin, while it rose in the control group. One may argue that cerebellar patients might have had a higher and more sustained co-contraction during the first movements resulting in a higher AI in the initial bin of the present study. Recent studies suggested that cocontraction might be accompanied by a reduction of perturbing effects of external forces resulting in an increased accuracy (Thoroughman and Shadmehr 1999). Others revealed that cocontraction decreases perturbations caused by joint interaction torques (Gribble and Ostry 1998). This led to the assumption that co-contraction might have been used by the nervous system as a strategy to improve limb movement accuracy (Gribble et al. 2003). Against this background it might be speculated that cerebellar patients used a higher co-contraction to decrease perturbations and that this co-contraction declined in successive trials of the second bin due to fatigue or lack of attention. However, EMGs were not recorded, and, thus the question of a possible influence of co-contraction remains open.
Learning does not generalize to untrained workspace in SCA patients
Recent research on goal-directed action in humans led to the hypothesis that the brain uses a neural representation of the limb-dynamics as controllers for feedforward motor control (Jordan et al. 1994; Kalveram 1992; Kawato et al. 1987; Lackner and Dizio 1994; Shadmehr and Mussa-Ivaldi 1994; Shidara et al. 1993; Wolpert and Kawato 1998; Wolpert et al. 1995). Such representations, so-called inverse dynamics models, transform the kinematic data of a movement plan into the required limb dynamics. In contrast, a forward dynamics model calculates joint kinematics on the basis of a given set of dynamics. Evidence for the presence of an inverse model of the limb dynamics arose from studies investigating force-field adaptation in healthy human subjects (e.g., Shadmehr and Mussa-Ivaldi 1994). Characteristic features of the arm trajectories that were indicative of an underlying inverse dynamics model were 1) learning-related after-effects (i.e., overshoot in the opposite direction after removal of the force-field) and 2) generalization of learning to untrained workspace. Such generalization of learning would be absent in a system that associates limb position with experienced force in a simple look-up table. In the present study control subjects presented learning-related after-effects and were able to generalize learning to targets they never trained before. This finding is consistent with the notion that control subjects acquired an inverse motor model of the limb dynamics. However, learning was not universal given that the learning related aftereffects were not found over the entire arm's workspace, but decayed with distance from the perturbed locations. Our result that force-adaptation only partially extends to untrained portions of the workspace in healthy subjects is in line with the findings of a previous study (Gandolfo et al. 1996).
In contrast to control subjects, patients' failure to adapt and to exhibit generalization implies that the acquisition or modification of these inverse dynamics models is impaired in cerebellar disease. This finding in humans complements previous results from animal, computational and functional imaging studies suggesting that the cerebellum plays a role in acquisition of an inverse dynamic model (Imamizu et al. 2000; Kawato and Gomi 1992; Kitazawa et al. 1998; Nezafat et al. 2001; Shadmehr and Holcomb 1997; Shidara et al. 1993).
Retention of learning is differentially impaired in SCA patients
Cerebellar lesions impair the retention of simple forms of motor learning such as long-term habituation of reflexes in humans (Maschke et al. 2000a). Here cerebellar patients demonstrated a reduced or even absent retention of force adaptation. However, it is noteworthy, that the patients revealed an impaired retention largely for the 45° movements, while demonstrating an improved adaptation during recall for 90° movements (increase in AI values from the 5th to the 6th bin). In a similar force adaptation paradigm, long-term recall resulted in a shift of activations from prefrontal regions to dorsal premotor and posterior parietal structures demonstrating that extracerebellar regions are engaged in the retention of force adaptation. However, this activation shift was accompanied by activations within the anterior cerebellar cortex indicating that the retention of force adaptation relies on a network of cerebellar and extracerebellar structures (Nezafat et al. 2001; Shadmehr and Holcomb 1997). Following this line of thinking, retention might have been mediated by parts of the cerebellum that store limb dynamics for 90° but not for 45° movements, although our data cannot speak conclusively to such claim. In addition, one may argue that movements to the 90° target resembled more single-joint movements given that subjects mainly had to rotate their shoulder to reach the target, whereas 45° movements required both shoulder rotation and elbow extension. Furthermore, previous studies that employed a similar force-adaptation paradigm have revealed that healthy humans generally exhibit accelerated learning to the 90° target (Shadmehr and Mussa-Ivaldi 1994). This might indicate that this phenomenon is not particular to the cerebellar group and that the difference in adaptation of movements to 45° and 90° observed in cerebellar patients cannot be solely understood as a motor deficit due to cerebellar dysfunction. Knowing that cerebellar patients have difficulties in compensating the interaction torques arising during multi-joint motion (Bastian et al. 1996, 2000; Goodkin et al. 1993; Topka et al. 1998a), the seemingly simpler 90° movement might have required lower computational demands in motor control mechanisms resulting in an enhanced opportunity for adaptation and retention.
Collectively, results of the present study provide strong evidence that motor adaptation to unknown external forces relies on the integrity of the cerebellum. The decrease in adaptive ability was nearly linearly related to the extent of cerebellar degeneration.
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ACKNOWLEDGMENTS |
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GRANTS
This study was supported by grants from the Deutsche Forschungsgemeinschaft (DFG MA 2209/3-1) to M. Maschke and from the National Institute of Neurological Disorders and Stroke (RO1 NS-37211) to C. M. Gomez.
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FOOTNOTES |
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Address for reprint requests and other correspondence: M. Maschke, Dept. of Neurology, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany (E-mail: matthias.maschke{at}uni-essen.de).
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REFERENCES |
|---|
|
Baizer JS, Kralj-Hans I, and Glickstein M. Cerebellar lesions and prism adaptation in macaque monkeys. J Neurophysiol 81: 1960–1965, 1999.
Bastian AJ, Zackowski KM, and Thach WT. Cerebellar ataxia: torque deficiency or torque mismatch between joints? J Neurophysiol 83: 3019–3030, 2000.
Bastian AJ, Martin TA, Keating JG, and Thach WT. Cerebellar ataxia: abnormal control of interaction torques across multiple joints. J Neurophysiol 76: 492–509, 1996.
Bloedel JR and Courville J. Cerebellar afferent systems. In: Handbook of Physiology, The Nervous System. Motor Control. Bethesda, MD: Am. Physiol. Soc., 1982, sect. 1, vol. II, part 2, p. 735–829.
Bosco G and Poppele RE. Proprioception from a spinocerebellar perspective. Physiol Rev 81: 539–568, 2001.
Criscimagna-Hemminger SE, Donchin O, Gazzaniga MS, and Shadmehr R. Learned dynamics of reaching movements generalize from dominant to nondominant arm. J Neurophysiol 89: 168–76, 2003.
Day JW, Schut LJ, Moseley ML, Durand AC, and Ranum LP. Spinocerebellar ataxia type 8: clinical features in a large family. Neurology 55: 649–657, 2000.
Deuschl G, Toro C, Zeffiro T, Massaquoi S, and Hallett M. Adaptation motor learning of arm movements in patients with cerebellar disease. J Neurol Neurosurg Psychiatry 60: 515–519, 1996.
Doyon J, Song AW, Karni A, Lalonde F, Adams MM, and Ungerleider LG. Experience-dependent changes in cerebellar contributions to motor sequence learning. Proc Natl Acad Sci USA 99: 1017–1022, 2002.
Flament D, Ellermann JM, Kim S-G, Ugurbil K, and Ebner TJ. Functional magnetic resonance imaging of cerebellar activation during learning of a visuomotor dissociation task. Hum Brain Map 4: 210–226, 1996.[CrossRef]
Friston KJ, Frith CD, Passingham RE, Liddle PF, and Frackowiak RS. Motor practice and neurophysiological adaptation in the cerebellum: a positron tomography study. Proc R Soc Lond B Biol Sci 248: 223–228, 1992.[Medline]
Gandolfo F, Mussa-Ivaldi FA, and Bizzi E. Motor learning by field approximation. Proc Natl Acad Sci USA 93: 3843–3846, 1996.
Gerwig M, Dimitrova A, Kolb FP, Maschke M, Brol B, Kunnel A, Boring D, Thilmann AF, Forsting M, Diener HC, and Timmann D. Comparison of eyeblink conditioning in patients with superior and posterior inferior cerebellar lesions. Brain 126: 71–94, 2003.
Gomez CM, Thompson RM, Gammack JT, Perlman SL, Dobyns WB, Truwit CL, Zee DS, Clark HB, and Anderson JH. Spinocerebellar ataxia type 6: gaze-evoked and vertical nystagmus, Purkinje cell degeneration, and variable age of onset. Ann Neurol 42: 933–950, 1997.[CrossRef][Web of Science][Medline]
Goodkin HP, Keating JG, Martin TA, and Thach WT. Preserved simple and impaired compound movement after infarction in the territory of the superior cerebellar artery. Can J Neurol Sci 3: S93–104, 1993.
Gribble PL, Mullin LI, Cothros N, and Mattar A. Role of cocontraction in arm movement accuracy. J Neurophysiol 89: 2396–405, 2003.
Gribble PL and Ostry DJ. Independent coactivation of shoulder and elbow muscles. Exp Brain Res 123: 355–60, 1998.[CrossRef][Web of Science][Medline]
Imamizu H, Miyauchi S, Tamada T, Sasaki Y, Takino R, Putz B, Yoshioka T, and Kawato M. Human cerebellar activity reflecting an acquired internal model of a new tool. Nature 403: 192–195, 2000.[CrossRef][Medline]
Imamizu H, Uno Y, and Kawato M. Internal representations of the motor apparatus: implications from generalization in visuomotor learning. J Exp Psychol Hum Percept Perform 21: 1174–1198, 1995.[CrossRef][Web of Science][Medline]
Kalveram KT. A neural network model rapidly learning gains and gating of reflexes necessary to adapt to an arm's dynamics. Biol Cybern 68: 183–191, 1992.[CrossRef][Web of Science][Medline]
Kawato M, Furukawa K, and Suzuki R. A hierarchical neural-network model for control and learning of voluntary movement. Biol Cybern 57: 169–185, 1987.[CrossRef][Web of Science][Medline]
Kawato M and Gomi H. A computational model of four regions of the cerebellum based on feedback-error learning. Biol Cybern 68: 95–103, 1992.[CrossRef][Web of Science][Medline]
Kitazawa S, Kimura T, and Yin PB. Cerebellar complex spikes encode both destinations and errors in arm movements. Nature 392: 494–497, 1998.[CrossRef][Medline]
Lackner JR and Dizio P. Rapid adaptation to Coriolis force perturbations of arm trajectory. J Neurophysiol 72: 299–313, 1994.
Martin TA, Keating JG, Goodkin HP, Bastian AJ, and Thach WT. Throwing while looking through prisms. I. Focal olivocerebellar lesions impair adaptation. Brain 119: 1183–1198, 1996.
Maschke M, Drepper J, Kindsvater K, Kolb FP, Diener HC, and Timmann D. Involvement of the human medial cerebellum in long-term habituation of the acoustic startle response. Exp Brain Res 133: 359–67, 2000a.[CrossRef][Web of Science][Medline]
Maschke M, Kolb FP, Drepper J, Peper M, Lachauer S, Muller SP, Diener HC, and Timmann D. A possible role of the human cerebellum in conditioning of the jaw-opening reflex. Neurosci Lett 285: 213–217, 2000b.[CrossRef][Web of Science][Medline]
Miall RC, Reckess GZ, and Imamizu H. The cerebellum coordinates eye and hand-tracking movements. Nat Neurosci 4: 638–644, 2001.[CrossRef][Web of Science][Medline]
Nezafat R, Shadmehr R, and Holcomb HH. Long-term adaptation to dynamics of reaching movements: a PET study. Exp Brain Res 140: 66–76, 2001.[CrossRef][Web of Science][Medline]
Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9: 97–113, 1971.[CrossRef][Web of Science][Medline]
Penhune VB and Doyon J. Dynamic cortical and subcortical networks in learning and delayed recall of timed motor sequences. J Neurosci 22: 1397–1406, 2002.
Seidler RD Purushotham A, Kim SG, Ugurbil K, Willingham D, and Ashe J. Cerebellum activation associated with performance change but not motor learning. Science 296: 2043–2046, 2002.
Seitz RJ Canavan AG, Yaguez L, Herzog H, Tellmann L, Knorr U, Huang Y, and Homberg V. Successive roles of the cerebellum and premotor cortices in trajectorial learning. Neuroreport 5: 2541–2544, 1994.[Web of Science][Medline]
Seitz RJ and Roland PE. Learning of sequential finger movements in man: a combined kinematic and positron emission tomography (PET) study. Eur J Neurosci 4: 154–165, 1992.[CrossRef][Web of Science][Medline]
Shadmehr R and Holcomb HH. Neural correlates of motor memory consolidation. Science 277: 821–825, 1997.
Shadmehr R and Mussa-Ivaldi FA. Adaptive representation of dynamics during learning of a motor task. J Neurosci 14: 3208–3224, 1994.[Abstract]
Shidara M, Kawano K, Gomi H, and Kawato M. Inverse-dynamics model eye movement control by Purkinje cells in the cerebellum. Nature 365: 50–52, 1993.[CrossRef][Medline]
Thoroughman KA and Shadmehr R. Electromyographic correlates of learning an internal model of reaching movements. J Neurosci 19: 8573–88, 1999.
Timmann D, Baier PC, Diener HC, and Kolb FP. Classically conditioned withdrawal reflex in cerebellar patients. I. Impaired conditioned responses. Exp Brain Res 130: 453–470, 2000.[CrossRef][Web of Science][Medline]
Topka H, Konczak J, Schneider K, Boose A, and Dichgans J. Multijoint arm movements in cerebellar ataxia: abnormal control of movement dynamics. Exp Brain Res 119: 493–503, 1998a.[CrossRef][Web of Science][Medline]
Topka H, Massaquoi SG, Benda N, and Hallett M. Motor skill learning in patients with cerebellar degeneration. J Neurol Sci 158: 164–172, 1998b.[CrossRef][Web of Science][Medline]
Trouillas P, Takayanagi T, Hallett M, Currier RD, Subramony SH, Wessel K, Bryer A, Diener HC, Massaquoi S, Gomez CM, Coutinho P, Ben Hamida M, Campanella G, Filla A, Schut L, Timann D, Honnorat J, Nighoghossian N, and Manyam B. International Cooperative Ataxia Rating Scale for pharmacological assessment of the cerebellar syndrome. The Ataxia Neuropharmacology Committee of the World Federation of Neurology. J Neurol Sci 145: 205–211, 1997.[CrossRef][Web of Science][Medline]
Weiner MJ, Hallett M, and Funkenstein HH. Adaptation to lateral displacement of vision in patients with lesions of the central nervous system. Neurology 33: 766–772, 1983.
Wolpert DM, Ghahramani Z, and Jordan MI. An internal model for sensorimotor integration. Science 269: 1880–1882, 1995.
Wolpert DM and Kawato M. Multiple paired forward and inverse models for motor control. Neural Network 11: 1317–1329, 1998.
Wolpert DM, Miall CR, and Kawato M. Internal models in the cerebellum. Trends Neurosci 21: 338–347, 1998.[CrossRef]
Woodruf-Pak DS. Classical conditioning. Int Rev Neurobiol 41: 342–366, 1997.
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W. Ilg, M. A. Giese, E. R. Gizewski, B. Schoch, and D. Timmann The influence of focal cerebellar lesions on the control and adaptation of gait Brain, November 1, 2008; 131(11): 2913 - 2927. [Abstract] [Full Text] [PDF]
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 M. Casadio, V. Sanguineti, P. Morasso, and C. Solaro Abnormal sensorimotor control, but intact force field adaptation, in multiple sclerosis subjects with no clinical disability Multiple Sclerosis, April 1, 2008; 14(3): 330 - 342. [Abstract] [PDF]
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 J. Izawa, T. Rane, O. Donchin, and R. Shadmehr Motor Adaptation as a Process of Reoptimization J. Neurosci., March 12, 2008; 28(11): 2883 - 2891. [Abstract] [Full Text] [PDF]
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Y.-w. Tseng, J. Diedrichsen, J. W. Krakauer, R. Shadmehr, and A. J. Bastian Sensory Prediction Errors Drive Cerebellum-Dependent Adaptation of Reaching J Neurophysiol, July 1, 2007; 98(1): 54 - 62. [Abstract] [Full Text] [PDF]
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W. Ilg, H. Golla, P. Thier, and M. A. Giese Specific influences of cerebellar dysfunctions on gait Brain, March 1, 2007; 130(3): 786 - 798. [Abstract] [Full Text] [PDF]
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A. G. Richardson, S. A. Overduin, A. Valero-Cabre, C. Padoa-Schioppa, A. Pascual-Leone, E. Bizzi, and D. Z. Press Disruption of Primary Motor Cortex before Learning Impairs Memory of Movement Dynamics J. Neurosci., November 29, 2006; 26(48): 12466 - 12470. [Abstract] [Full Text] [PDF]
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 T. D. Sanger, D. Chen, M. R. Delgado, D. Gaebler-Spira, M. Hallett, J. W. Mink, and the Taskforce on Childhood Motor Disorders Definition and Classification of Negative Motor Signs in Childhood Pediatrics, November 1, 2006; 118(5): 2159 - 2167. [Abstract] [Full Text] [PDF]
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 H. Chen, S. E. Hua, M. A. Smith, F. A. Lenz, and R. Shadmehr Effects of Human Cerebellar Thalamus Disruption on Adaptive Control of Reaching Cereb Cortex, October 1, 2006; 16(10): 1462 - 1473. [Abstract] [Full Text] [PDF]
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S. M. Morton and A. J. Bastian Cerebellar Contributions to Locomotor Adaptations during Splitbelt Treadmill Walking J. Neurosci., September 6, 2006; 26(36): 9107 - 9116. [Abstract] [Full Text] [PDF]
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T. E. Milner, D. W. Franklin, H. Imamizu, and M. Kawato Central Representation of Dynamics When Manipulating Handheld Objects J Neurophysiol, February 1, 2006; 95(2): 893 - 901. [Abstract] [Full Text] [PDF]
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J. Diedrichsen, Y. Hashambhoy, T. Rane, and R. Shadmehr Neural Correlates of Reach Errors J. Neurosci., October 26, 2005; 25(43): 9919 - 9931. [Abstract] [Full Text] [PDF]
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D. S. Reisman, H. J. Block, and A. J. Bastian Interlimb Coordination During Locomotion: What Can be Adapted and Stored? J Neurophysiol, October 1, 2005; 94(4): 2403 - 2415. [Abstract] [Full Text] [PDF]
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 T. E. Milner and D. W. Franklin Impedance control and internal model use during the initial stage of adaptation to novel dynamics in humans J. Physiol., September 1, 2005; 567(2): 651 - 664. [Abstract] [Full Text] [PDF]
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M. A. Smith and R. Shadmehr Intact Ability to Learn Internal Models of Arm Dynamics in Huntington's Disease But Not Cerebellar Degeneration J Neurophysiol, May 1, 2005; 93(5): 2809 - 2821. [Abstract] [Full Text] [PDF]
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J. Diedrichsen, T. Verstynen, S. L. Lehman, and R. B. Ivry Cerebellar Involvement in Anticipating the Consequences of Self-Produced Actions During Bimanual Movements J Neurophysiol, February 1, 2005; 93(2): 801 - 812. [Abstract] [Full Text] [PDF]
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ABSTRACT
INTRODUCTION
