Regional approach to fMRI data analysis using hemodynamic response modeling

Abstract

Analysis of functional magnetic resonance imaging (fMRI) data has been performed using both model-driven (parametric) methods and data-driven methods. An advantage of model-driven methods is incorporation of prior knowledge of spatial and temporal properties of the hemodynamic response (HDR). A novel analytical framework for fMRI data has been developed that identifies multi-voxel regions of activation through iterative segmentation-based optimization over HDR estimates for both individual voxels and regional groupings. Simulations using synthetic activation embedded in autoregressive integrated moving average (ARIMA) noise reveal the proposed procedure to be more sensitive and selective than conventional fMRI analysis methods (reference set: principle component analysis, PCA; independent component analysis, ICA; k-means clustering, k=100; univariate t-test) in identification of active regions over the range of average contrast-to-noise ratios of 0.5 to 4.0. Results of analysis of extant human data (for which the average contrast-to-noise ratio is unknown) are further suggestive of greater statistical detection power. Refinement of this new procedure is expected to reduce both false positive and negative rates, without resorting to filtering that can reduce the effective spatial resolution.