Functional Methods for Reaching Trajectory Experiments

• Activity Number: 474
• Type: Topic Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Jeff Goldsmith*+ and Tomoko Kitago and John Krakauer and Ciprian M. Crainiceanu
• Companies: Columbia University and Columbia University and Johns Hopkins University and The Johns Hopkins University
• Keywords: Bias ; Variance ; Mahalanobis distance ; Permutation testing

Abstract

Motor control is fundamentally important for patient function following neurological damage, for instance due to moderate or severe stroke. Commonly used experiments designed to understand motor control record high-density measurements of hand position while a subject reaches toward a target. Although an entire trajectory is available, often analyses focus only on simply summaries such as endpoint position. Functional approaches are useful in this setting, allowing the analysis of complete trajectories as well as providing a framework for understanding the distribution of motions. A functional principal components analysis reduces the dimension of the observed trajectories; and further analysis of subject-level scores leads to the novel scientific finding that stroke patients and healthy controls are deeply similar with respect to motor control.