Showing papers by "John W. Krakauer published in 2010"

Error Correction, Sensory Prediction, and Adaptation in Motor Control

Abstract: Motor control is the study of how organisms make accurate goal-directed movements. Here we consider two problems that the motor system must solve in order to achieve such control. The first problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the movement it produces is variable, as the body and the environment can both change. A solution is to build adaptive internal models of the body and the world. The predictions of these internal models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it. Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.

Improvement in aphasia scores after stroke is well predicted by initial severity.

Abstract: Background and Purpose— Most improvement from poststroke aphasia occurs within the first 3 months, but there remains unexplained variability in recovery. Recently, we reported a strong correlation ...

Compensatory Motor Control After Stroke: An Alternative Joint Strategy for Object-Dependent Shaping of Hand Posture

Abstract: Efficient grasping requires planned and accurate coordination of finger movements to approximate the shape of an object before contact. In healthy subjects, hand shaping is known to occur early in reach under predominantly feedforward control. In patients with hemiparesis after stroke, execution of coordinated digit motion during grasping is impaired as a result of damage to the corticospinal tract. The question addressed here is whether patients with hemiparesis are able to compensate for their execution deficit with a qualitatively different grasp strategy that still allows them to differentiate hand posture to object shape. Subjects grasped a rectangular, concave, and convex object while wearing an instrumented glove. Reach-to-grasp was divided into three phases based on wrist kinematics: reach acceleration (reach onset to peak horizontal wrist velocity), reach deceleration (peak horizontal wrist velocity to reach offset), and grasp (reach offset to lift-off). Patients showed reduced finger abduction, proximal interphalangeal joint (PIP) flexion, and metacarpophalangeal joint (MCP) extension at object grasp across all three shapes compared with controls; however, they were able to partially differentiate hand posture for the convex and concave shapes using a compensatory strategy that involved increased MCP flexion rather than the PIP flexion seen in controls. Interestingly, shape-specific hand postures did not unfold initially during reach acceleration as seen in controls, but instead evolved later during reach deceleration, which suggests increased reliance on sensory feedback. These results indicate that kinematic analysis can identify and quantify within-limb compensatory motor control strategies after stroke. From a clinical perspective, quantitative study of compensation is important to better understand the process of recovery from brain injury. From a motor control perspective, compensation can be considered a model for how joint redundancy is exploited to accomplish the task goal through redistribution of work across effectors.

Losing control: Brain vs spinal cord

Abstract: Neurologic examination after focal motor injury tends to focus on weakness rather than control. One reason for this may be the implicit assumption that weakness precludes control. Most neurologists, however, are familiar with the common bedside finding in patients with hemiparesis after stroke: they can squeeze your hand with surprising force but cannot make individuated finger movements. This dissociation is also seen when comparing the effect of a unilateral hemispheric stroke on motor performance in the ipsilateral arm; strength is unaffected but skilled movements are impaired.1 The separation between control of movement and of isometric force has a long tradition in the design of robot arms2 and psychophysical evidence suggests that these 2 types of control may be partitioned in the brain.3 In this issue of Neurology ®, van Hedel et al.4 shed further anatomic light on the strength/skill dissociation by comparing leg motor deficits in patients with incomplete spinal cord injury (iSCI) and patients with unilateral hemispheric stroke. The main hypothesis was that these 2 patient groups would show differential skill levels despite comparable degrees of weakness. The hypothesis was based on the idea that in iSCI, all descending pathways from the brain to the spinal cord segments below the lesion are affected in the …

Learning not to generalize: modular adaptation of visuomotor gain.

Abstract: When a new sensorimotor mapping is learned through practice, learning commonly transfers to unpracticed regions of task space, that is, generalization ensues. Does generalization reflect fixed properties of movement representations in the nervous system and thereby limit what visuomotor mappings can and cannot be learned? Or does what needs to be learned determine the shape of generalization? We used the broad generalization properties of visuomotor gain adaptation to address these questions. Adaptation to a single gain for reaching movements is known to generalize broadly across movement directions. By training subjects on two different gains in two directions, we set up a potential conflict between generalization patterns: if generalization of gain adaptation indicates fixed properties of movement amplitude encoding, then learning two different gains in different directions should not be possible. Conversely, if generalization is flexible, then it should be possible to learn two gains. We found that subjects were able to learn two gains simultaneously, although more slowly than when they adapted to a single gain. Analysis of the resulting double-gain generalization patterns, however, unexpectedly revealed that generalization around each training direction did not arise de novo, but could be explained by a weighted combination of single-gain generalization patterns, in which the weighting takes into account the relative angular separation between training directions. Our findings therefore demonstrate that the mappings to each training target can be fully learned through reweighting of single-gain generalization patterns and not through a categorical alteration of these functions. These results are consistent with a modular decomposition approach to visuomotor adaptation, in which a complex mapping results from a combination of simpler mappings in a "mixture-of-experts" architecture.