Empirical and substantive models, the Bayesian paradigm, and meta-analysis in functional brain imaging.
Functional neuroimaging research is currently rediscovering and adapting established statistical methods for its use, including design of experiments, the general linear model, contrasts, random field theory, longitudinal models, Fourier analysis, and general signal and image processing methods. This brief paper gives an example of comparative performance of five different statistical models applied to the same set of data generated in an fMRI study of motor cortex. These methods include a two-sample t-statistic, a Kolmogorov-Smirnov statistic, a principal component/canonical variates approach, a pruned feed-forward artificial neural network with one hidden layer, and a frequency domain regression convolution model allowing for spatially varying hemodynamic responses. Produced by essentially empirical statistical models, there appear to be more similarities than differences in these spatial activation patterns, yet all lack explicit incorporation of substantive prior information. Distinctions are drawn between exploratory models for hypothesis generation and confirmatory models for hypothesis testing. In addition, the Bayesian paradigm helps to combine empirical and substantive models, and meta-analysis provides a rational means by which to combine information over a range of similar results affected minimally by publication bias.
Lange
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Consolidated Department of Psychiatry, Harvard Medical School and Brain Imaging Center, McLean Hospital, Belmont, Massachusetts 02178-9106, USA. lange@mclean.harvard.edu