342 – Novel Statistical Testing and Activation-Detection Methods for Imaging Data
Improved Activation Detection via Rice-Distributed fMRI Time Series Modeling
Daniel W. Adrian
Department of Statistics, Grand Valley State University
Ranjan Maitra
Department of Statistics and Statistical Laboratory, Iowa State University
Daniel B. Rowe
Department of Mathematics, Statistics and Computer Science
Two developments in fMRI magnitude time series modeling, namely, the incorporation of temporal dependence and the Ricean distribution, have been separated by a distributional "mismatch": such time series modeling is largely based upon Gaussian-distributional-based extensions to the general linear model, which precludes its use under Ricean modeling. We bridge this gap by applying AR(p) errors to the latent, Gaussian-distributed real and imaginary components from which the Ricean-distributed magnitudes are computed by augmenting the observed magnitude data by missing phase data in an EM algorithm framework. We use the EM algorithm for parameter estimation and extend it to compute approximate standard errors and test statistics for activation and AR order detection. When compared to the standard Gaussian AR(p) model, this "AR(p) Ricean model" produces less-biased parameter estimates and similar performance on a real fMRI dataset.