Detecting variable responses within fMRI time-series of volumes-of-interest using repeated measures ANOVA

Abstract

We present an approach to analyzing fMRI timetrends from volumes-of-interest (VOI) within and between subject groups using repeated measures analysis of variance (RMANOVA), which allows temporal patterns to be examined without an a priori model of expected timing or pattern of response. 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 package 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, imported into SAS, and the procedure PROC MIXED implements the RMANOVA. 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 fMRI paradigms eliciting complex response patterns.