The NPAIRS Computational Statistics Framework for Data Analysis in Neuroimaging

摘要

We introduce the role of resampling and prediction (p) metrics for flexible discriminant modeling in neuroimaging, and highlight the importance of combining these with measurements of the reproducibility (r) of extracted brain activation patterns. Using the NPAIRS resampling framework we illustrate the use of (p, r) plots as a function of the size of the principal component subspace (Q) for a penalized discriminant analysis (PDA) to: optimize processing pipelines in functional magnetic resonance imaging (fMRI), and measure the global SNR (gSNR) and dimensionality of fMRI data sets. We show that the gSNRs of typical fMRI data sets cause the optimal Q for a PDA to often lie in a phase transition region between gSNR ? 1 with large optimal Q versus SNR ? 1 with small optimal Q.