Abstract
We analyze data on the impact of testosterone on the dynamics of Mn2+ accumulation measured by magnetic resonance imaging in three songbird brain areas: the nucleus robustus arcopallii (RA), area X, and the high vocal center (HVC). Birds with and without testosterone were included in the experiment, and repeated measurements were available in both a preand post-drug administration period. We formulate a nonlinear modeling strategy, allowing for the incorporation of (1) within-bird correlation, (2) the nonlinearity of the profiles, and (3) the effect of treatment. For two of the outcomes (RA and area X), biological theory suggests a parametric form, but for HVC this is not the case. Because the HVC outcome bears some resemblance with the two-compartment model known from pharmacokinetics, this model was considered a sensible choice. We use a different model, based on fractional polynomials, as a sensitivity analysis for the latter. All methods used provide good fits to the data, confirm results from previous, simple analyses undertaken in the literature, but were able to detect additional effects of treatment that had so far gone undetected. The fractional polynomial and two-compartment models provide similar substantive conclusions; the two together can be seen as a form of sensitivity analysis.
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References
Brenowitz, E. A., Margoliash, D., and Nordeen, K. W. (1997), “An Introduction to Birdsong and the Avian Song System,” Journal of Neurobiology, 33, 495–500.
Davidian, M., and Giltinan, D. M. (1995), Nonlinear Models for Repeated Measurement Data, London: Chapman & Hall.
Fahrmeir, L., and Tutz, G. (2001), Multivariate Statistical Modelling Based on Generalized Linear Models, Heidelberg: Springer-Verlag.
Laird, N. M., and Ware, J. H. (1982), “Random Effects Models for Longitudinal Data,” Biometrics, 38, 963–974.
Pinheiro, J. C., and Bates, D. M. (1995), “Approximations to the Log-Likelihood Function in the Nonlinear Mixed-Effects Model,” Journal of Computational and Graphical Statistics, 4, 12–35.
Royston, P., and Altman, D. G. (1994), “Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling,” Applied Statistics, 43, 429–468.
Stram, D. O., and Lee, J. W. (1994), “Variance Components Testing in the Longitudinal Mixed Effects Model,” Biometrics, 50, 1171–1177.
Van der Linden, A., Verhoye, A., Van Meir, V., Tindemans, I., Eens, M., Absil, P., and Balthazart, J. (2002), “In Vivo Manganese-Enhanced Magnetic Resonance Imaging Reveals Connections and Functional Properties of the Songbird Vocal Control System,” Neuroscience, 112, 467–474.
Van Meir, V., Verhoye, M., Absil, P., Eens, M., Balthazart, J., and Van der Linden, A. (2004), “Differential Effects of Testosterone on Neuronal Populations and Their Connections in a Sensorimotor Brain Nucleus Controlling Song Production in Songbirds: A Manganese Enhanced-Magnetic Resonance Imaging Study,” Neuroimage, 21, 914–923.
Verbeke, G., and Molenberghs, G. (2000), Linear Mixed Models for Longitudinal Data, New York: Springer-Verlag.
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Serroyen, J., Molenberghs, G., Verhoye, M. et al. Dynamic manganese-enhanced MRI signal intensity processing based on nonlinear mixed modeling to study changes in neuronal activity. JABES 10, 170–183 (2005). https://doi.org/10.1198/108571105X46426
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DOI: https://doi.org/10.1198/108571105X46426