Comparison of brain structural variables, neuropsychological factors, and treatment outcome in early-onset versus late-onset late-life depression.

OBJECTIVE: To compare differences in gray matter volumes, white matter and subcortical gray matter hyperintensities, neuropsychological factors, and treatment outcome between early- and late-onset late-life depressed (LLD) subjects. METHODS: We conducted a prospective, nonrandomized, controlled trial at the outpatient clinics at Washington University and Duke University on 126 subjects, aged 60 years or older, who met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for major depression, scored 20 or more on the Montgomery-Asberg Depression Rating Scale (MADRS), and received neuropsychological testing and magnetic resonance imaging. Subjects were excluded for cognitive impairment or severe medical disorders. After 12 weeks of sertraline treatment, subjects' MADRS scores over time and neuropsychological factors were studied. RESULTS: Left anterior cingulate thickness was significantly smaller in the late-onset depressed group than in the early-onset LLD subjects. The late-onset group also had more hyperintensities than the early-onset LLD subjects. No differences were found in neuropsychological factor scores or treatment outcome between early-onset and late-onset LLD subjects. CONCLUSION: Age at onset of depressive symptoms in LLD subjects are associated with differences in cortical thickness and white matter and subcortical gray matter hyperintensities, but age at onset did not affect neuropsychological factors or treatment outcome.

Quantitative analysis of PiB-PET with FreeSurfer ROIs.

In vivo quantification of ß-amyloid deposition using positron emission tomography is emerging as an important procedure for the early diagnosis of the Alzheimer's disease and is likely to play an important role in upcoming clinical trials of disease modifying agents. However, many groups use manually defined regions, which are non-standard across imaging centers. Analyses often are limited to a handful of regions because of the labor-intensive nature of manual region drawing. In this study, we developed an automatic image quantification protocol based on FreeSurfer, an automated whole brain segmentation tool, for quantitative analysis of amyloid images. Standard manual tracing and FreeSurfer-based analyses were performed in 77 participants including 67 cognitively normal individuals and 10 individuals with early Alzheimer's disease. The manual and FreeSurfer approaches yielded nearly identical estimates of amyloid burden (intraclass correlation = 0.98) as assessed by the mean cortical binding potential. An MRI test-retest study demonstrated excellent reliability of FreeSurfer based regional amyloid burden measurements. The FreeSurfer-based analysis also revealed that the majority of cerebral cortical regions accumulate amyloid in parallel, with slope of accumulation being the primary difference between regions.

Application of copulas to improve covariance estimation for partial least squares.

Dimension reduction techniques, such as partial least squares, are useful for computing summary measures and examining relationships in complex settings. Partial least squares requires an estimate of the covariance matrix as a first step in the analysis, making this estimate critical to the results. In addition, the covariance matrix also forms the basis for other techniques in multivariate analysis, such as principal component analysis and independent component analysis. This paper has been motivated by an example from an imaging study in Alzheimer's disease where there is complete separation between Alzheimer's and control subjects for one of the imaging modalities. This separation occurs in one block of variables and does not occur with the second block of variables resulting in inaccurate estimates of the covariance. We propose the use of a copula to obtain estimates of the covariance in this setting, where one set of variables comes from a mixture distribution. Simulation studies show that the proposed estimator is an improvement over the standard estimators of covariance. We illustrate the methods from the motivating example from a study in the area of Alzheimer's disease.

Bootstrapping GEE models for fMRI regional connectivity.

An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations during rest between groups. We apply generalized estimating equation (GEE) models to test for differences in regional correlations across groups. Both the GEE marginal model and GEE transition model are evaluated and compared to the standard pooling Fisher-z approach using simulation studies. Standard errors of all methods are estimated both theoretically (model-based) and empirically (bootstrap). Of all the methods, we find that the transition models have the best statistical properties. Overall, the model-based standard errors and bootstrap standard errors perform about the same. We also demonstrate the methods with a functional connectivity study in a healthy cognitively normal population of ApoE4+ participants and ApoE4- participants who are recruited from the Adult Children's Study conducted at the Washington University Knight Alzheimer's Disease Research Center.

Missing Data Methods for Partial Correlations.

In the dementia area it is often of interest to study relationships among regional brain measures; however, it is often necessary to adjust for covariates. Partial correlations are frequently used to correlate two variables while adjusting for other variables. Complete case analysis is typically the analysis of choice for partial correlations with missing data. However, complete case analysis will lead to biased and inefficient results when the data are missing at random. We have extended the partial correlation coefficient in the presence of missing data using the expectation-maximization (EM) algorithm, and compared it with a multiple imputation method and complete case analysis using simulation studies. The EM approach performed the best of all methods with multiple imputation performing almost as well. These methods were illustrated with regional imaging data from an Alzheimer's disease study.

A generalized estimating equations approach for resting-state functional MRI group analysis.

An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations between groups. The overall objective is to assess inter-regional correlations at a resting-state with no stimulus or task. We propose using a generalized estimating equation (GEE) transition model and a GEE marginal model to model the within-subject correlation for each region. Residuals calculated from the GEE models are used to correlate brain regions and assess between group differences. The standard pooling approach of group averages of the Fisher-z transformation assuming temporal independence is a typical approach used to compare group correlations. The GEE approaches and standard Fisher-z pooling approach are demonstrated with an Alzheimer's disease (AD) connectivity study in a population of AD subjects and healthy control subjects. We also compare these methods using simulation studies and show that the transition model may have better statistical properties.

A Likelihood-Based Approach for Missing Genotype Data.

Missing genotype data in a candidate gene association study can make it difficult to model the effects of multiple genetic variants simultaneously. In particular, when regression models are used to model phenotype as a function of SNP genotypes in several different genes, the most common approach is a complete case analysis, in which only individuals with no missing genotypes are included. But this can lead to substantial reduction in sample size and thus potential bias and loss in efficiency. A number of other methods for handling missing data are applicable, but have rarely been used in this context. The purpose of this paper is to describe how several standard methods for handling missing data can be applied or adapted to this problem, and to compare their performance using a simulation study. We demonstrate these techniques using an Alzheimer's disease association study. We show that the expectation-maximization algorithm and multiple imputation with a bootstrapped expectation-maximization sampling algorithm have the best properties of all the estimators studied.

Combining least absolute shrinkage and selection operator (LASSO) and principal-components analysis for detection of gene-gene interactions in genome-wide association studies.

Variable selection in genome-wide association studies can be a daunting task and statistically challenging because there are more variables than subjects. We propose an approach that uses principal-component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) to identify gene-gene interaction in genome-wide association studies. A PCA was used to first reduce the dimension of the single-nucleotide polymorphisms (SNPs) within each gene. The interaction of the gene PCA scores were placed into LASSO to determine whether any gene-gene signals exist. We have extended the PCA-LASSO approach using the bootstrap to estimate the standard errors and confidence intervals of the LASSO coefficient estimates. This method was compared to placing the raw SNP values into the LASSO and the logistic model with individual gene-gene interaction. We demonstrated these methods with the Genetic Analysis Workshop 16 rheumatoid arthritis genome-wide association study data and our results identified a few gene-gene signals. Based on our results, the PCA-LASSO method shows promise in identifying gene-gene interactions, and, at this time we suggest using it with other conventional approaches, such as generalized linear models, to narrow down genetic signals.