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    Detecting variable responses in time-series using repeated measures ANOVA: Application to physiologic challenges (2016)

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    Type of Content
    Journal Article
    UC Permalink
    http://hdl.handle.net/10092/17362
    
    Publisher's DOI/URI
    https://doi.org/10.12688/f1000research.8252.2
    
    Publisher
    University of Canterbury. School of Health Sciences
    Collections
    • Health: Journal Articles [151]
    Authors
    Macey, P.M.
    Schluter, P.J.
    Macey, K.E.
    Harper, R.M.
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    Abstract

    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.

    Citation
    Macey, P.M., Schluter, P.J., Macey, K.E., Harper, R.M. (2016) Detecting variable responses in time-series using repeated measures ANOVA: Application to physiologic challenges. F1000Research, 5, pp. 563.
    This citation is automatically generated and may be unreliable. Use as a guide only.
    Keywords
    mixed effect models; regression analysis; statistical models; physiological responses; functional magnetic resonance imaging
    ANZSRC Fields of Research
    11 - Medical and Health Sciences::1106 - Human Movement and Sports Science::110699 - Human Movement and Sports Science not elsewhere classified
    32 - Biomedical and clinical sciences::3208 - Medical physiology::320899 - Medical physiology not elsewhere classified
    Rights
    Copyright: © 2016 Macey PM et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
    https://hdl.handle.net/10092/17651

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