Detecting variable responses in time-series using repeated measures ANOVA: Application to physiologic challenges.
We present an approach to analyzing physiologic timetrends recorded during a stimulus by comparing means at each time point using repeated measures analysis of variance (RMANOVA). The approach allows temporal patterns to be examined without an a priori model of expected timing or pattern of response. The approach was originally applied to signals recorded from functional magnetic resonance imaging (fMRI) volumes-of-interest (VOI) during a physiologic challenge, but we have used the same technique to analyze continuous recordings of other physiological signals such as heart rate, breathing rate, and pulse oximetry. For fMRI, the method serves as a complement to whole-brain voxel-based analyses, and is useful for detecting complex responses within pre-determined brain regions, or as a post-hoc analysis of regions of interest identified by whole-brain assessments. We illustrate an implementation of the technique in the statistical software packages R and SAS. VOI timetrends are extracted from conventionally preprocessed fMRI images. A timetrend of average signal intensity across the VOI during the scanning period is calculated for each subject. The values are scaled relative to baseline periods, and time points are binned. In SAS, the procedure PROC MIXED implements the RMANOVA in a single step. In R, we present one option for implementing RMANOVA with the mixed model function "lme". Model diagnostics, and predicted means and differences are best performed with additional libraries and commands in R; we present one example. The ensuing results allow determination of significant overall effects, and time-point specific within- and between-group responses relative to baseline. We illustrate the technique using fMRI data from two groups of subjects who underwent a respiratory challenge. RMANOVA allows insight into the timing of responses and response differences between groups, and so is suited to physiologic testing paradigms eliciting complex response patterns.
UCLA School of Nursing, LA, CA, USA; Brain Research Institute, Department of Neurobiology, David Geffen School of Medicine, UCLA, LA, CA, USA.
Paul M
PM
Macey
School of Health Sciences, University of Canterbury, Christchurch, New Zealand.
Philip J
PJ
Schluter
UCLA School of Nursing, LA, CA, USA.
Katherine E
KE
Macey
Department of Neurobiology, David Geffen School of Medicine, UCLA, LA, CA, USA.
Ronald M
RM
Harper
functional magnetic resonance imaging; mixed effect models; physiological responses; regression analysis; statistical models