Abstract:
Hoffman-reflex (H-reflex) recruitment curves. Smoothed and interpolated recruitment curves from 38
participants were used for analysis. Standard methods were used to calculate three discrete variables
(i.e., H[subscript max]/M[subscript max] ratio, H[subscript th], H[subscript slp]). FPCA was then used to extract principal component functions (PCFs)
from the processed recruitment curves. PCF scores were calculated to determine how much each PCF
contributed to an individuals’ recruitment curve. The analysis extracted three PCFs, and three sets of PCF
scores. Correlation analyses and systematic variation in the PCF scores indicated that the scores for the
first PCF were primarily correlated to H-reflex threshold (H[subscript th]) and that the scores for the second and third
PCFs were correlated to H-reflex magnitude (H[subscript max]/M[subscript max] ratio) and slope (H[subscript slp]), respectively. In addition,
results from the FPCA indicated that the first PCF explained 56.0% of the variance between all H-reflex
recruitment curves, whereas the second and third PCFs explained 24.1% and 13.0%, respectively. The
high correlations indicate FPCA-derived PCFs capture similar physiological information as the standard
discrete variables and suggest that application of FPCA to H-reflex recruitment curves could be used in
future studies to complement traditional analyses that investigate excitability of the motoneuron pool.
