Parametric response surface models for analysis of multi-site fMRI data.

Seyoung Kim; Padhraic Smyth; Hal Stern; Jessica Turner

doi:10.1007/11566465_44

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Parametric response surface models for analysis of multi-site fMRI data.

Seyoung Kim·Padhraic Smyth·Hal Stern·Jessica Turner

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Abstract

Analyses of fMRI brain data are often based on statistical tests applied to each voxel or use summary statistics within a region of interest (such as mean or peak activation). These approaches do not explicitly take into account spatial patterns in the activation signal. In this paper, we develop a response surface model with parameters that directly describe the spatial shapes of activation patterns. We present a stochastic search algorithm for parameter estimation. We apply our method to data from a multi-site fMRI study, and show how the estimated parameters can be used to analyze different sources of variability in image generation, both qualitatively and quantitatively, based on spatial activation patterns.

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Algorithms Artificial Intelligence Brain Brain Mapping Computer Simulation Humans Image Enhancement Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Magnetic Resonance Imaging Models, Biological Models, Statistical Reproducibility of Results Sensitivity and Specificity Evaluation Study Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

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Seyoung Kim, Padhraic Smyth, Hal Stern, Jessica Turner. 2005. Parametric response surface models for analysis of multi-site fMRI data.. https://doi.org/10.1007/11566465_44

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