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- Bayesian Inferences for Beta Semiparametric-Mixed Models to Analyze Longitudinal Neuroimaging Data
- Xiao-Feng Wang, Yingxing Li
- Biometrical Journal 56 (2014) 4, 662–677
- #002308 20160221 () Views:6
- Diffusion tensor imaging (DTI) is a quantitative magnetic resonance imaging technique that measures
the three-dimensional diffusion of water molecules within tissue through the application of multiple
diffusion gradients. This technique is rapidly increasing in popularity for studying white matter properties
and structural connectivity in the living human brain. One of the major outcomes derived from the
DTI process is known as fractional anisotropy, a continuous measure restricted on the interval (0,1).
Motivated from a longitudinal DTI study of multiple sclerosis, we use a beta semiparametric-mixed
regression model for the neuroimaging data. This work extends the generalized additive modelmethodology
with beta distribution family and random effects. We describe two estimation methods with
penalized splines, which are formalized under a Bayesian inferential perspective. The first one is carried
out by Markov chain Monte Carlo (MCMC) simulations while the second one uses a relatively new
technique called integrated nested Laplace approximation (INLA). Simulations and the neuroimaging
data analysis show that the estimates obtained from both approaches are stable and similar, while the
INLA method provides an efficient alternative to the computationally expensive MCMC method.
- Keywords: Beta distribution; Diffusion tensor imaging; Generalized semiparametric-mixed model; Integrated nested Laplace approximation; Markov chain Monte Carlo; Penalized splines.
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