415 – Statistical Methods for Improved Processing and Analysis of fMRI Data
Improved Activation Detection via Complex-Valued AR(p) Modeling of fMRI Voxel Time Series
Daniel Adrian
Grand Valley State University
Ranjan Maitra
Iowa State University
Daniel B. Rowe
Marquette University/Medical College of Wisconsin
A complex-valued model with AR(p) errors is proposed as an alternative to the more common Gaussian-assumed magnitude-only AR(p) model for fMRI time series. Likelihood-ratio-test-based activation statistics are derived for both models and are compared in terms of activation detection and false discovery rates for simulated and experimental data. For simulated data, the complex-valued AR(p) model likelihood-ratio activation statistic shows superior power of activation detection at low signal-to-noise ratios and lower false discovery rates. Also, when applied to an experimental data set, the activation map produced by the complex-valued AR(p) model more clearly identifies the primary activation regions. Our results advocate the use of the complex-valued data and the Gaussian AR(p) model as a more efficient and reliable tool in fMRI experiments over the current practice of using only the magnitude dataset.