It has been suggested that the brain and in particular the cerebellum and motor cortex adapt to represent the environment during reaching movements under various visuomotor perturbations. It is well known that significant delay is present in neural conductance and processing; however, the possible representation of delay and adaptation to delayed visual feedback has been largely overlooked. Here we investigated the control of reaching movements in human subjects during an imposed visuomotor delay in a virtual reality environment. In the first experiment, when visual feedback was unexpectedly delayed, the hand movement overshot the end-point target, indicating a vision-based feedback control. Over the ensuing trials, movements gradually adapted and became accurate. When the delay was removed unexpectedly, movements systematically undershot the target, demonstrating that adaptation occurred within the vision-based feedback control mechanism. In a second experiment designed to broaden our understanding of the underlying mechanisms, we revealed similar after-effects for rhythmic reversal (out-and-back) movements. We present a computational model accounting for these results based on two adapted forward models, each tuned for a specific modality delay (proprioception or vision), and a third feedforward controller. The computational model, along with the experimental results, refutes delay representation in a pure forward vision-based predictor and suggests that adaptation occurred in the forward vision-based predictor, and concurrently in the state-based feedforward controller. Understanding how the brain compensates for conductance and processing delays is essential for understanding certain impairments concerning these neural delays as well as for the development of brain-machine interfaces.
BackgroundProprioception plays important roles in planning and control of limb posture and movement. The impact of proprioceptive deficits on motor function post-stroke has been difficult to elucidate due to limitations in current tests of arm proprioception. Common clinical tests only provide ordinal assessment of proprioceptive integrity (eg. intact, impaired or absent). We introduce a standardized, quantitative method for evaluating proprioception within the arm on a continuous, ratio scale. We demonstrate the approach, which is based on signal detection theory of sensory psychophysics, in two tasks used to characterize motor function after stroke.MethodsHemiparetic stroke survivors and neurologically intact participants attempted to detect displacement- or force-perturbations robotically applied to their arm in a two-interval, two-alternative forced-choice test. A logistic psychometric function parameterized detection of limb perturbations. The shape of this function is determined by two parameters: one corresponds to a signal detection threshold and the other to variability of responses about that threshold. These two parameters define a space in which proprioceptive sensation post-stroke can be compared to that of neurologically-intact people. We used an auditory tone discrimination task to control for potential comprehension, attention and memory deficits.ResultsAll but one stroke survivor demonstrated competence in performing two-alternative discrimination in the auditory training test. For the remaining stroke survivors, those with clinically identified proprioceptive deficits in the hemiparetic arm or hand had higher detection thresholds and exhibited greater response variability than individuals without proprioceptive deficits. We then identified a normative parameter space determined by the threshold and response variability data collected from neurologically intact participants. By plotting displacement detection performance within this normative space, stroke survivors with and without intact proprioception could be discriminated on a continuous scale that was sensitive to small performance variations, e.g. practice effects across days.ConclusionsThe proposed method uses robotic perturbations similar to those used in ongoing studies of motor function post-stroke. The approach is sensitive to small changes in the proprioceptive detection of hand motions. We expect this new robotic assessment will empower future studies to characterize how proprioceptive deficits compromise limb posture and movement control in stroke survivors.
Stroke often results in both motor and sensory deficits, which may interact in the manifested functional impairment. Proprioception is known to play important roles in the planning and control of limb posture and movement; however, the impact of proprioceptive deficits on motor function has been difficult to elucidate due in part to the qualitative nature of available clinical tests. We present a quantitative and standardized method for evaluating proprioception in tasks directly relevant to those used to assess motor function. Using a robotic manipulandum that exerted controlled displacements of the hand, stroke participants were evaluated, and compared with a control group, in their ability to detect such displacements in a 2-alternative, forced-choice paradigm. A psychometric function parameterized the decision process underlying the detection of the hand displacements. The shape of this function was determined by a signal detection threshold and by the variability of the response about this threshold. Our automatic procedure differentiates between participants with and without proprioceptive deficits and quantifies functional proprioceptive sensation on a magnitude scale that is meaningful for ongoing studies of degraded motor function in comparable horizontal movements.
BackgroundWe examined the validity and reliability of a short robotic test of upper limb proprioception, the Arm Movement Detection (AMD) test, which yields a ratio-scaled, objective outcome measure to be used for evaluating the impact of sensory deficits on impairments of motor control, motor adaptation and functional recovery in stroke survivors.MethodsSubjects grasped the handle of a horizontal planar robot, with their arm and the robot hidden from view. The robot applied graded force perturbations, which produced small displacements of the handle. The AMD test required subjects to respond verbally to queries regarding whether or not they detected arm motions. Each participant completed ten, 60s trials; in five of the trials, force perturbations were increased in small increments until the participant detected motion while in the others, perturbations were decreased until the participant could no longer detect motion. The mean and standard deviation of the 10 movement detection thresholds were used to compute a Proprioceptive Acuity Score (PAS). Based on the sensitivity and consistency of the estimated thresholds, the PAS quantifies the likelihood that proprioception is intact. Lower PAS scores correspond to higher proprioceptive acuity. Thirty-nine participants completed the AMD test, consisting of 25 neurologically intact control participants (NIC), seven survivors of stroke with intact proprioception in the more affected limb (HSS+P), and seven survivors of stroke with impaired or absent proprioception in the more affected limb (HSS-P).ResultsSignificant group differences were found, with the NIC and HSS+P groups having lower (i.e., better) PAS scores than the HSS-P group. A subset of the participants completed the AMD test multiple times and the AMD test was found to be reliable across repetitions.ConclusionsThe AMD test required less than 15 min to complete and provided an objective, ratio-scaled measure of proprioceptive acuity in the upper limb. In the future, this test could be utilized to evaluate the contributions of sensory deficits to motor recovery following stroke.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-017-0269-3) contains supplementary material, which is available to authorized users.
Motor control neuroscientists measure limb trajectories and extract the onset of the movement for a variety of purposes. Such trajectories are often aligned relative to the onset of individual movement before the features of that movement are extracted and their properties are inspected. Onset detection is performed either manually or automatically, typically by selecting a velocity threshold. Here, we present a simple onset detection algorithm that is more accurate than the conventional velocity threshold technique. The proposed method is based on a simple regression and follows the minimum acceleration with constraints model, in which the initial phase of the bell-shaped movement is modeled by a cubic power of the time. We demonstrate the performance of the suggested method and compare it to the velocity threshold technique and to manual onset detection by a group of motor control experts. The database for this comparison consists of simulated minimum jerk trajectories and recorded reaching movements.
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