Assessing the Effects of Pharmacological Agents on Respiratory Dynamics Using Time-Series Modeling
Wong, K.F.; Gong, J. J; Cotten, J. F.; Solt, K.; Brown, E. N.
Volume: 60, Issue 4, Page:1118-1125
Developing quantitative descriptions of how stimulant and depressant drugs affect the respiratory system is an important focus in medical research. Respiratory variables such as breathing rate, the volume of each breath and the amount of expired carbon dioxide, have prominent temporal dynamics in their normal patterns. A standard approach for these studies is to measure the respiratory variables pre- and post-treatment and to compare the average difference across the typically small sample of study subjects. This approach ignores the highly detailed data collected on each subject, the serial dependence in the measurements and the possibility of making highly precise statements about the respiratory change within each animal as opposed to simply across the group. To analyze continuously recorded respiratory variables, we present a signal plus correlated noise model in which the signal is represented as a polynomial and the noise is represented as an autoregressive model. We use an approximate maximum likelihood procedure to estimate the model parameters and the corrected Akaike’s Information Criterion to choose simultaneously the orders of the polynomial and the autoregressive models. In an analysis of respiratory rates recorded from anesthetized rats before and after administration of the respiratory stimulant methylphenidate (MPH) we use the model to construct within-animal z-tests of the drug effect that take account of the time-varying nature of the mean respiratory rate and the serial dependence in rate measurements. We correct for the effect of model lack-of-fit on our inferences by also computing bootstrap confidence intervals for the average difference in respiratory rate pre- and post-methylphenidate treatment. Our time-series modeling quantifies explicitly within each animal the substantial increase that occurs in mean respiratory rate and respiratory dynamics following methylphenidate administration. This paradigm can be readily adapted to analyze the dynamics of other respiratory variables before and after pharmacologic treatments. Read More…