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1.  T2 measurement of J-coupled metabolites in the human brain at 3T 
NMR in biomedicine  2011;25(4):10.1002/nbm.1767.
The proton T2 relaxation times of metabolites in the human brain were measured using point-resolved spectroscopy at 3T in vivo. Four echo times (54, 112, 246 and 374 ms) were selected from numerical and phantom analyses for effective detection of the glutamate multiplet at ~2.35 ppm. In vivo data were obtained from medial occipital and left occipital cortices of five healthy volunteers, which contained predominantly gray and white matter, respectively. Spectra were analyzed with LCModel software using volume-localized calculated spectra of brain metabolites. The estimate of the signal strength vs. TE was fitted to a monoexponential function for estimation of apparent T2 (T2†). The T2† was estimated to be similar between the brain regions for creatine, choline, glutamate and myo-inositol, but significantly different for the N-acetylaspartate singlet and multiplet. The T2†s of glutamate and myo-inositol were measured as 181±16 and 197±14 ms (mean±SD, N = 5) for medial occipital, and 180±12 and 196±17 ms for left occipital, respectively.
doi:10.1002/nbm.1767
PMCID: PMC3852663  PMID: 21845738
1H-MRS; Relaxation time (T2); J-coupled metabolites; 3T; Human brain; Gray matter; White matter
2.  Regional changes of cortical mean diffusivities with aging after correction of partial volume effects 
NeuroImage  2012;62(3):1705-1716.
Accurately measuring the cortical mean diffusivity (MD) derived from diffusion tensor imaging (DTI) at the comprehensive lobe, gyral and voxel level of young, elderly healthy brains and those with Alzheimer's disease (AD) may provide insights on heterogeneous cortical microstructural changes caused by aging and AD. Due to partial volume effects (PVE), the measurement of cortical MD is overestimated with contamination of cerebrospinal fluid (CSF). The bias is especially severe for aging and AD brains because of significant cortical thinning of these brains. In this study, we aimed to quantitatively characterize the unbiased regional cortical MD changes due to aging and AD and delineate the effects of cortical thinning of elderly healthy and AD groups on MD measurements. DTI and T1-weighted images of 14 young, 15 elderly healthy subjects and 17 AD patients were acquired. With the parcellated cortical gyri and lobes from T1 weighted image transformed to DTI, regional cortical MD of all subjects before and after PVE correction were measured. CSF contamination model was used to correct bias of MD caused by PVE. Compared to cortical MD of young group, significant increases of corrected MD for elderly healthy and AD groups were found only in frontal and limbic regions, respectively, while there were significant increases of uncorrected MD all over the cortex. Uncorrected MD are significantly higher in limbic and temporal gyri in AD group, compared to those in elderly healthy group but higher MD only remained in limbic gyri after PVE correction. Cortical thickness was also measured for all groups. The correlation slopes between cortical MD and thickness for elderly healthy and AD groups were significantly decreased after PVE correction compared to before correction while no significant change of correlation slope was detected for young group. It suggests that the cortical thinning in elderly healthy and AD groups is a significant contributor to the bias of uncorrected cortical MD measurement. The established comprehensive unbiased cortical MD profiles of young, elderly healthy subjects and AD patients at the lobe, gyral and voxel level may serve as clinical references for cortical microstructure.
doi:10.1016/j.neuroimage.2012.05.082
PMCID: PMC3574164  PMID: 22683383
DTI; Cortex; Mean diffusivity; Aging; Alzheimer's disease; Unbiased; Partial volume effects
3.  Adjusting wheal size measures to correct atopy misclassification 
Purpose:
Skin prick testing (SPT) is fundamental to the practice of clinical allergy identifying relevant allergens and predicting the clinical expression of disease. Wheal sizes on SPT are used to identify atopic cases, and the cut-off value for a positive test is commonly set at 3 mm. However, the measured wheal sizes do not solely reflect the magnitude of skin reaction to allergens, but also skin reactivity (reflected in the size of histamine reaction) and other random or non-random factors. We sought to estimate wheal sizes exclusively due to skin response to allergens and propose gender-specific cutoff points of atopy.
Methods:
We developed a Bayesian method to adjust observed wheal sizes by excluding histamine and other factor effects, based on which revised cutoff points are proposed for males and females, respectively. The method is then applied to and intensively evaluated using a study population aged 18, at a location on the Isle of Wight in the United Kingdom. To evaluate the proposed approach, two sample t-tests for population means and proportion tests are applied.
Results:
Four common aeroallergens, house dust mite (HDM), grass pollen, dog dander, and alternaria are considered in the study. Based on 3 mm cutoff, males tend to be more atopic than females (P-values are between 0.00087 and 0.062). After applying the proposed methods to adjust wheal sizes, our findings suggest that misclassifications of atopy occur more often in males. Revised allergen-specific cutoff values are proposed for each gender.
Conclusion:
To reduce the gender discrepancy, we may have two potentially convenient solutions. One way is to apply allergen-specific and gender-specific cutoff values following the proposed method. Alternatively, we can revise the concentration of allergens in the SPT solutions but keep the cutoff values unchanged, which may be more convenient to clinicians.
doi:10.2147/IJGM.S22193
PMCID: PMC3160870  PMID: 21887114
SPT; atopy; Bayesian method; joint modeling; misclassification
4.  Modelling the random effects covariance matrix in longitudinal data 
Statistics in medicine  2003;22(10):1631-1647.
SUMMARY
A common class of models for longitudinal data are random effects (mixed) models. In these models, the random effects covariance matrix is typically assumed constant across subject. However, in many situations this matrix may differ by measured covariates. In this paper, we propose an approach to model the random effects covariance matrix by using a special Cholesky decomposition of the matrix. In particular, we will allow the parameters that result from this decomposition to depend on subject-specific covariates and also explore ways to parsimoniously model these parameters. An advantage of this parameterization is that there is no concern about the positive definiteness of the resulting estimator of the covariance matrix. In addition, the parameters resulting from this decomposition have a sensible interpretation. We propose fully Bayesian modelling for which a simple Gibbs sampler can be implemented to sample from the posterior distribution of the parameters. We illustrate these models on data from depression studies and examine the impact of heterogeneity in the covariance matrix on estimation of both fixed and random effects.
doi:10.1002/sim.1470
PMCID: PMC2747645  PMID: 12720301
Cholesky decomposition; heterogeneity; mixed models

Results 1-4 (4)