Principal Component Analysis of gait in Parkinson's disease: relevance of gait velocity.

Principal Component Analysis (PCA) is a method to estimate the relation between data points. We used PCA to analyse movements of the upper and lower extremities during treadmill walking in healthy subjects and two groups of Parkinsonian patients. Healthy subjects (n=35) showed a typical pattern with high values of PC1 and low values in a descending order of PC2-PC4. Increase of speed resulted in a significant increase of PC1 and a significant decrease of the following PC's. In more severely affected patients (n=19, UPDRS>20), PC1 was significantly decreased and PC2-PC4 were significantly increased compared to healthy subjects. Speed could be increased only within a small range without corresponding changes of the PC's. In less severely affected patients (n=17), significant differences of the PC's were only found with fast pace. Separate analysis of arms and legs revealed that these changes are only due to altered movements of the arm. Analysis of the pattern of PC's in response to changes of gait velocities reveal alterations even in less severely affected Parkinsonian patients. The changes of the PC's with higher gait velocities in healthy subjects are suggestive of an increase of intersegmental coordination. This is impaired even in less severely affected Parkinsonian patients.