Parkinson's disease (PD) is a neurodegenerative disorder that leads to a progressive decline in motor function. Growing evidence indicates that PD patients also experience an array of sensory problems that negatively impact motor function. This is especially true for proprioceptive deficits, which profoundly degrade motor performance. This review specifically address the relation between proprioception and motor impairments in PD. It is structured around 4 themes: (a) It examines whether the sensitivity of kinaesthetic perception, which is based on proprioceptive inputs, is actually altered in PD. (b) It discusses whether failed processes of proprioceptive-motor integration are central to the motor problems in PD. (c) It presents recent findings focusing on the link between the proprioception and the balance problems in PD. And (d) it discusses the current state of knowledge of how levodopa medication and deep brain stimulation affect proprioceptive and motor function in PD. The authors conclude that a failure to evaluate and to map proprioceptive information onto voluntary and reflexive motor commands is an integral part of the observed motor symptoms in PD.

Mobile phones with built-in accelerometers promise a convenient, objective way to quantify everyday movements and classify those movements into activities. Using accelerometer data we estimate the following activities of 18 healthy subjects and eight patients with Parkinson’s disease: walking, standing, sitting, holding, or not wearing the phone. We use standard machine learning classifiers (support vector machines, regularized logistic regression) to automatically select, weigh, and combine a large set of standard features for time series analysis. Using cross validation across all samples we are able to correctly identify 96.1% of the activities of healthy subjects and 92.2% of the activities of Parkinson’s patients. However, when applying the classification parameters derived from the set of healthy subjects to Parkinson’s patients, the percent correct lowers to 60.3%, due to different characteristics of movement. For a fairer comparison across populations we also applied subject-wise cross validation, identifying healthy subject activities with 86.0% accuracy and 75.1% accuracy for patients. We discuss the key differences between these populations, and why algorithms designed for and trained with healthy subject data are not reliable for activity recognition in populations with motor disabilities.

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We quantified the effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN) and medication on Parkinsonian rigidity using an objective measure of work about the elbow joint during a complete cycle of imposed 1-Hz sinusoidal oscillations. Resting and activated rigidity were analyzed in four experimental conditions: 1) off treatment; 2) on DBS; 3) on medication; and 4) on DBS plus medication. Rigidity at the elbow joint was also assessed using the Unified Parkinson's Disease Rating Scale (UPDRS). We tested ten patients who received STN DBS and ten age-matched neurologically healthy control subjects. The activated rigidity condition increased work in both Parkinson's disease (PD) patients and control subjects. In PD patients, STN DBS reduced both resting and activated rigidity as indicated by work and the UPDRS rigidity score. This is the first demonstration that STN stimulation reduces rigidity using an objective measure such as work. In contrast, the presurgery dose of antiparkinsonian medication did not significantly improve the UPDRS rigidity score and reduced work only in the activated rigidity condition. Our results suggest that STN DBS may be more effective in alleviating rigidity in the upper limb of PD patients than medications administered at presurgery dosage level.

Learning of a motor task, such as making accurate goal-directed movements, is associated with a number of changes in limb kinematics and in the EMG activity that produces the movement. Some of these changes include increases in movement velocity, improvements in end-point accuracy, and the development of a biphasic/triphasic EMG pattern for fast movements. One question that has remained unanswered is whether the time course of the learning-related changes in movement parameters is similar for all parameters. The present paper focuses on this question and presents evidence that different parameters evolve with a specific temporal order. Neurologically normal subjects were trained to make horizontal, planar movements of the elbow that were both fast and accurate. The performance of the subjects was monitored over the course of 400 movements made during experiments lasting approximately 1.5 h. We measured time-related parameters (duration of acceleration, duration of deceleration, and movement duration) and amplitude-related parameters (peak acceleration, peak deceleration, peak velocity), as well as movement distance. In addition, each subject's reaction time and EMG activity was monitored. We found that reaction time was the parameter that changed the fastest and that reached a steady baseline earliest. Time-related parameters decreased at a somewhat slower rate and plateaued next. Amplitude-related parameters were slowest in reaching steady-state values. In subjects making the fastest movements, a triphasic EMG patterns was observed to develop. Our findings reveal that movement parameters change with different time courses during the process of motor learning. The results are discussed in terms of the neural substrates that may be responsible for the differences in this aspect of motor learning and skill acquisition.