Armin Schwartzman

Clinical trial of a farnesyltransferase inhibitor in children with Hutchinson–Gilford progeria syndrome

Hutchinson–Gilford progeria syndrome (HGPS) is an extremely rare, fatal, segmental premature aging syndrome caused by a mutation in LMNA that produces the farnesylated aberrant lamin A protein, progerin. This multisystem disorder causes failure to thrive and accelerated atherosclerosis leading to early death. Farnesyltransferase inhibitors have ameliorated disease phenotypes in preclinical studies. Twenty-five patients with HGPS received the farnesyltransferase inhibitor lonafarnib for a minimum of 2 y. Primary outcome success was predefined as a 50% increase over pretherapy in estimated annual rate of weight gain, or change from pretherapy weight loss to statistically significant on-study weight gain. Nine patients experienced a ≥50% increase, six experienced a ≥50% decrease, and 10 remained stable with respect to rate of weight gain. Secondary outcomes included decreases in arterial pulse wave velocity and carotid artery echodensity and increases in skeletal rigidity and sensorineural hearing within patient subgroups. All patients improved in one or more of these outcomes. Results from this clinical treatment trial for children with HGPS provide preliminary evidence that lonafarnib may improve vascular stiffness, bone structure, and audiological status.

Cross‐subject comparison of principal diffusion direction maps

Diffusion tensor imaging (DTI) data differ fundamentally from most brain imaging data in that values at each voxel are not scalars but 3 × 3 positive definite matrices also called diffusion tensors. Frequently, investigators simplify the data analysis by reducing the tensor to a scalar, such as fractional anisotropy (FA). New statistical methods are needed for analyzing vector and tensor valued imaging data. A statistical model is proposed for the principal eigenvector of the diffusion tensor based on the bipolar Watson distribution. Methods are presented for computing mean direction and dispersion of a sample of directions and for testing whether two samples of directions (e.g., same voxel across two groups of subjects) have the same mean. False discovery rate theory is used to identify voxels for which the two‐sample test is significant. These methods are illustrated in a DTI data set collected to study reading ability. It is shown that comparison of directions reveals differences in gross anatomic structure that are invisible to FA. Magn Reson Med 53:1423–1431, 2005.

Mechanisms of Premature Vascular Aging in Children With Hutchinson-Gilford Progeria Syndrome

Hutchinson-Gilford progeria syndrome is a rare, segmental premature aging syndrome of accelerated atherosclerosis and early death from myocardial infarction or stroke. This study sought to establish comprehensive characterization of the fatal vasculopathy in Hutchinson-Gilford progeria syndrome and its relevance to normal aging. We performed cardiovascular assessments at a single clinical site on the largest prospectively studied cohort to date. Carotid-femoral pulse wave velocity was dramatically elevated (mean: 13.00±3.83 m/s). Carotid duplex ultrasound echobrightness, assessed in predefined tissue sites as a measure of arterial wall density, was significantly greater than age- and sex-matched controls in the intima-media (P<0.02), near adventitia (P<0.003), and deep adventitia (P<0.01), as was internal carotid artery mean flow velocity (P<0.0001). Ankle-brachial indices were abnormal in 78% of patients. Effective disease treatments may be heralded by normalizing trends of these noninvasive cardiovascular measures. The data demonstrate that, along with peripheral vascular occlusive disease, accelerated vascular stiffening is an early and pervasive mechanism of vascular disease in Hutchinson-Gilford progeria syndrome. There is considerable overlap with cardiovascular changes of normal aging, which reinforces the view that defining mechanisms of cardiovascular disease in Hutchinson-Gilford progeria syndrome provides a unique opportunity to isolate a subset of factors influencing cardiovascular disease in the general aging population.

FALSE DISCOVERY RATE ANALYSIS OF BRAIN DIFFUSION DIRECTION MAPS

Diffusion tensor imaging (DTI) is a novel modality of magnetic resonance imaging that allows noninvasive mapping of the brains white matter. A particular map derived from DTI measurements is a map of water principal diffusion directions, which are proxies for neural fiber directions. We consider a study in which diffusion direction maps were acquired for two groups of subjects. The objective of the analysis is to find regions of the brain in which the corresponding diffusion directions differ between the groups. This is attained by first computing a test statistic for the difference in direction at every brain location using a Watson model for directional data. Interesting locations are subsequently selected with control of the false discovery rate. More accurate modeling of the null distribution is obtained using an empirical null density based on the empirical distribution of the test statistics across the brain. Further, substantial improvements in power are achieved by local spatial averaging of the test statistic map. Although the focus is on one particular study and imaging technology, the proposed inference methods can be applied to other large scale simultaneous hypothesis testing problems with a continuous underlying spatial structure.

Empirical null and false discovery rate analysis in neuroimaging.

Current strategies for thresholding statistical parametric maps in neuroimaging include control of the family-wise error rate, control of the false discovery rate (FDR) and thresholding of the posterior probability of a voxel being active given the data, the latter derived from a mixture model of active and inactive voxels. Correct inference using any of these criteria depends crucially on the specification of the null distribution of the test statistics. In this article we show examples from fMRI and DTI data where the theoretical null distribution does not match well the observed distribution of the test statistics. As a solution, we introduce the use of an empirical null, a null distribution empirically estimated from the data itself, allowing for global corrections of theoretical null assumptions. The theoretical null distributions considered are normal, t, chi(2) and F, all commonly encountered in neuroimaging. The empirical null estimate is accompanied by an estimate of the proportion of non-active voxels in the data. Based on the two-class mixture model, we present the equivalence between the strategies of controlling FDR and thresholding posterior probabilities in the context of neuroimaging and show that the FDR estimates derived from the empirical null can be seen as empirical Bayes estimates.

Gene expression profiling of human breast tissue samples using SAGE-Seq

We present a powerful application of ultra high-throughput sequencing, SAGE-Seq, for the accurate quantification of normal and neoplastic mammary epithelial cell transcriptomes. We develop data analysis pipelines that allow the mapping of sense and antisense strands of mitochondrial and RefSeq genes, the normalization between libraries, and the identification of differentially expressed genes. We find that the diversity of cancer transcriptomes is significantly higher than that of normal cells. Our analysis indicates that transcript discovery plateaus at 10 million reads/sample, and suggests a minimum desired sequencing depth around five million reads. Comparison of SAGE-Seq and traditional SAGE on normal and cancerous breast tissues reveals higher sensitivity of SAGE-Seq to detect less-abundant genes, including those encoding for known breast cancer-related transcription factors and G protein-coupled receptors (GPCRs). SAGE-Seq is able to identify genes and pathways abnormally activated in breast cancer that traditional SAGE failed to call. SAGE-Seq is a powerful method for the identification of biomarkers and therapeutic targets in human disease.

Complex data analysis in high‐resolution SSFP fMRI

In transition‐band steady‐state free precession (SSFP) functional MRI (fMRI), functional contrast originates from a bulk frequency shift induced by a deoxygenated hemoglobin concentration change in the activated brain regions. This frequency shift causes a magnitude and/or phase‐signal change depending on the off‐resonance distribution of a voxel in the balanced‐SSFP (bSSFP) profile. However, in early low‐resolution studies, only the magnitude signal activations were shown. In this paper the task‐correlated phase‐signal change is presented in a high‐resolution (1 × 1 × 1 mm3) study. To include this phase activation in a functional analysis, a new complex domain data analysis method is proposed. The results show statistically significant phase‐signal changes in a large number of voxels comparable to that of the magnitude‐activated voxels. The complex‐data analysis method successfully includes these phase activations in the activation map and thus provides wider coverage compared to magnitude‐data analysis results. Magn Reson Med 57:905–917, 2007.

Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND)

To develop a statistical model for the tridimensional diffusion MRI signal at each voxel that describes the signal arising from each tissue compartment in each voxel.

Biomarker discovery for arsenic exposure using functional data. Analysis and feature learning of mass spectrometry proteomic data

Plasma biomarkers of exposure to environmental contaminants play an important role in early detection of disease. The emerging field of proteomics presents an attractive opportunity for candidate biomarker discovery, as it simultaneously measures and analyzes a large number of proteins. This article presents a case study for measuring arsenic concentrations in a population residing in an As-endemic region of Bangladesh using plasma protein expressions measured by SELDI-TOF mass spectrometry. We analyze the data using a unified statistical method based on functional learning to preprocess mass spectra and extract mass spectrometry (MS) features and to associate the selected MS features with arsenic exposure measurements. The task is challenging due to several factors, the high dimensionality of mass spectrometry data, complicated error structures, and a multiple comparison problem. We use nonparametric functional regression techniques for MS modeling, peak detection based on the significant zero-downcrossing method, and peak alignment using a warping algorithm. Our results show significant associations of arsenic exposure to either under- or overexpressions of 20 proteins.

MULTIPLE TESTING OF LOCAL MAXIMA FOR DETECTION OF PEAKS IN 1D

A topological multiple testing scheme for one-dimensional domains is proposed where, rather than testing every spatial or temporal location for the presence of a signal, tests are performed only at the local maxima of the smoothed observed sequence. Assuming unimodal true peaks with finite support and Gaussian stationary ergodic noise, it is shown that the algorithm with Bonferroni or Benjamini-Hochberg correction provides asymptotic strong control of the family wise error rate and false discovery rate, and is power consistent, as the search space and the signal strength get large, where the search space may grow exponentially faster than the signal strength. Simulations show that error levels are maintained for nonasymptotic conditions, and that power is maximized when the smoothing kernel is close in shape and bandwidth to the signal peaks, akin to the matched filter theorem in signal processing. The methods are illustrated in an analysis of electrical recordings of neuronal cell activity.