Nonlinear Estimation and Modeling of fMRI Data using
Spatio-Temporal Support Vector Regression
Yongmei Michelle Wang, Robert T. Schultz,
R. Todd Constable and
Lawrence H. Staib
Yale University School of Medicine
Information Processing in Medical Imaging, July 2003
Abstract
This paper presents a new and general nonlinear framework for fMRI data
analysis based on statistical learning methodology: support vector machines.
Unlike most current methods which assume a linear model for simplicity,
the estimation and analysis of fMRI signal within the proposed framework
is nonlinear, which matches recent findings on the dynamics underlying
neural activity and hemodynamic physiology. The approach utilizes
spatio-temporal support vector regression (SVR), within which the intrinsic
spatio-temporal autocorrelations in fMRI data are reflected. The novel
formulation of the problem allows merging model-driven with data-driven
methods, and therefore unifies these two currently separate modes of fMRI
analysis. In addition, multiresolution signal analysis is achieved and
developed. Other advantages of the approach are: avoidance of interpolation
after motion estimation, embedded removal of low-frequency noise components,
and easy incorporation of multi-run, multi-subject, and multi-task studies
into the framework.
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