The Quantitative Evaluation of Functional Neuroimaging Experiments: The NPAIRS Data Analysis Framework
Wednesday, October 11, 2000 - 11:30am - 12:15pm
Stephen Strother (Minneapolis VA Medical Center)
We introduce the NPAIRS (Non-parametric Prediction, Activation, Influence and Reproducibility reSampling) data analysis framework for evaluating the interaction between activation tasks and the methodological choices involved in data acquisition, preprocessing, data-analysis model selection and associated software tools. NPAIRS provides a real-data driven alternative to simulations and ROC curves by examining the relationship between model prediction accuracy and activation image signal-to-noise ratios (SNR)--we plot training-test set predictions of the experimental design variables (e.g., scan state labels, covariates etc.; Morch et al.,1997, Hansen et al.,1999) versus the reproducibility SNR metrics in Strother et al. (1997). For a given spatial scale we propose that methodological choices should be optimized by maximizing the prediction accuracy and the reproducibility SNR of the extracted activation images. NPAIRS also provides a Z-score activation-image incorporating random subject effects for any data-analysis model and a measure of each subject's relative influence (Strother et al., 1998, 1999). We demonstrate NPAIRS using the wide range of activation signal-to-noise ratios and associated PAIR statistics obtained from [O-15]water PET studies of twelve age and sex matched groups performing different motor tasks (8 subjects/group). If time permits preliminary results using NPAIRS to rank the importance of within-subject alignment, temporal detrending and spatial smoothing in a fMRI task will also be presented.