Abstract #301902

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JSM 2003 Abstract #301902
Activity Number: 251
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301902
Title: Multilevel Nonlinear Mixed-Effects Models in Functional Brain Imaging
Author(s): Hong Pan*+ and Qiang Chen and Emily Stern and David Silbersweig
Companies: Weill Medical College and Weill Medical College and Weill Medical College and Weill Medical College
Address: Department Psychiatry, Box 140, New York, NY, 10021-0012,
Keywords: nonlinear mixed-effects model ; time series ; functional magnetic resonance imaging (fMRI) ; brain ; neuroimaging

It is well known that the temporal patterns of fMRI signal may vary substantially across trials, sessions, and brain regions for the same experimental stimuli and the same group of subjects. In this work, it was shown that more flexible yet parsimonious parametric models such as multilevel nonlinear mixed-effects models are needed to address the heteroscedasticity in variance-covariance structure due to nested factors in fMRI experiments. Along with nested factors such as the side of the brain, the trial and the subject, a Gaussian function of four parameters (gain, lag, dispersion and baseline) was used to characterize the shape of hemodynamic signal from a brain region of interest. Nested random-effects terms are warranted to model: interactions between a fixed-effects factor (such as the shape parameters) and a random-effects factor (such as the subject), and nested classification factors (such as the side and the trial effects nested within subject level). The trial factor is only significant as a random effect nested within the subject factor, as is the side factor. There are highly significant random effects in terms of the shape of the peak in fMRI signal within subject level.

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