Altered Perceptual Sensitivity to Kinematic Invariants in Parkinson's Disease

Ample evidence exists for coupling between action and perception in neurologically healthy individuals, yet the precise nature of the internal representations shared between these domains remains unclear. One experimentally derived view is that the invariant properties and constraints characterizing movement generation are also manifested during motion perception. One prominent motor invariant is the “two-third power law,” describing the strong relation between the kinematics of motion and the geometrical features of the path followed by the hand during planar drawing movements. The two-thirds power law not only characterizes various movement generation tasks but also seems to constrain visual perception of motion. The present study aimed to assess whether motor invariants, such as the two thirds power law also constrain motion perception in patients with Parkinson's disease (PD). Patients with PD and age-matched controls were asked to observe the movement of a light spot rotating on an elliptical path and to modify its velocity until it appeared to move most uniformly. As in previous reports controls tended to choose those movements close to obeying the two-thirds power law as most uniform. Patients with PD displayed a more variable behavior, choosing on average, movements closer but not equal to a constant velocity. Our results thus demonstrate impairments in how the two-thirds power law constrains motion perception in patients with PD, where this relationship between velocity and curvature appears to be preserved but scaled down. Recent hypotheses on the role of the basal ganglia in motor timing may explain these irregularities. Alternatively, these impairments in perception of movement may reflect similar deficits in motor production

A Decision Tree for Automatic Diagnosis of Parkinson’s Disease from Offline Drawing Samples: Experiments and Findings

We address the problem of designing a machine learning tool for the automatic diagnosis of Parkinson's disease that is capable of providing an explanation of its behavior in terms that are easy to understand by clinicians. For this purpose, we consider as machine learning tool the decision tree, because it provides the decision criteria in terms of both the features which are actually useful for the purpose among the available ones and how their values are used to reach the final decision, thus favouring its acceptance by clinicians. On the other side, we consider the random forest and the support vector machine, which are among the top performing machine learning tool that have been proposed in the literature, but whose decision criteria are hidden into their internal structures. We have evaluated the effectiveness of different approaches on a public dataset, and the results show that the system based on the decision tree achieves comparable or better results that state-of-the-art solutions, being the only one able to provide a plain description of the decision criteria it adopts in terms of the observed features and their values

Music and musical sonification for the rehabilitation of Parkinsonian dysgraphia: Conceptual framework

International audienceMusic has been shown to enhance motor control in patients with Parkinson's disease (PD). Notably, musical rhythm is perceived as an external auditory cue that helps PD patients to better control movements. The rationale of such effects is that motor control based on auditory guidance would activate a compensatory brain network that minimizes the recruitment of the defective pathway involving the basal ganglia. Would associating music to movement improve its perception and control in PD? Musical sonification consists in modifying in real-time the playback of a preselected music according to some movement parameters. The validation of such a method is underway for handwriting in PD patients. When confirmed, this study will strengthen the clinical interest of musical sonification in motor control and (re)learning in PD