Strong-meter and weak-meter rhythm identification in spina bifida meningomyelocele and volumetric parcellation of rhythm-relevant cerebellar regions.

Children with spina bifida meningomyelocele (SBM) are impaired relative to controls in terms of discriminating strong-meter and weak-meter rhythms, so congenital cerebellar dysmorphologies that affect rhythmic movements also disrupt rhythm perception. Cerebellar parcellations in children with SBM showed an abnormal configuration of volume fractions in cerebellar regions important for rhythm function: a smaller inferior-posterior lobe, and larger anterior and superior-posterior lobes.

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^