Robert Todd Ogden

A method to identify differential expression profiles of time-course gene data with Fourier transformation

BackgroundTime course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis.ResultsThis work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization.The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles.ConclusionsUsing this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics.

Quantitative wavelet domain image processing of dynamic PET data

Neuroreceptor PET studies consisting of long dynamic data acquisitions result in data with low signal-to-noise ratio and limited spatial resolution. To address these problems we have developed a 3D wavelet-based image processing tool (wavelet filter, WF), containing both denoising and enhancement functionality. The filter is based on multi-scale thresholding and cross-scale regularization. These operations are data-driven, which may lead to non-linearity effects and hamper quantification of dynamic PET data. The aim of the present study was to investigate these effects using both phantom and human PET data. A phantom study was performed with a cylindrical phantom, filled with 18F, containing a number of spherical inserts filled with 11C. Human studies were performed on 9 healthy volunteers after injection of the serotonine transporter tracer [11C]DASB. Images from both phantom and human studies were reconstructed with filtered backprojection and post-processed by WF with a series of different denoising and enhancement parameter values. The phantom study was analyzed by computing the insert-to-background ratio as a function of time. The human study was analyzed with a 1-tissue compartment model for a series of brain regions. For the phantom study, linear relations were found between unprocessed and WF processed data for positive contrasts. However, for negative contrast, non-linearity effects were observed. For the human data, good correlation was obtained between results from unprocessed and WF processed data. Our results showed that, although non-linear effects may appear in low-contrast areas, it is possible to achieve accurate quantification with wavelet-based image processing

A hybrid deconvolution approach for estimation of in vivo non-displaceable binding for brain PET targets without a reference region

Background and aim Estimation of a PET tracer’s non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study. Methods HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region’s impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region. Results For [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of VND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a VND determined from a non-ideal reference region, when considering the binding potentials BPP and BPND. Conclusions HYDECA can provide subject-specific estimates of VND without requiring a blocking study for tracers and targets for which a valid reference region does not exist.

Robust fitting of [11C]-WAY-100635 PET data.

Fitting of a positron emission tomography (PET) time–activity curve is typically accomplished according to the least squares (LS) criterion, which is optimal for data having Gaussian distributed errors, but not robust in the presence of outliers. Conversely, quantile regression (QR) provides robust estimates not heavily influenced by outliers, sacrificing a little efficiency relative to LS when no outliers are present. Given these considerations, we hypothesized that QR would improve parameter estimate accuracy as measured by reduced intersubject variance in distribution volume (V T ) compared with LS in PET modeling. We compare V T values after applying QR with those using LS on 49 controls studied with [11C]-WAY-100635. QR decreases the standard deviation of the V T estimates (relative improvement range: 0.08% to 3.24%), while keeping the within-group average V T values almost unchanged. QR variance reduction results in fewer subjects required to maintain the same statistical power in group analysis without additional hardware and/or image registration to correct head motion.

Development and evaluation of a multimodal marker of major depressive disorder

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage‐derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques—penalized logistic regression, random forest, and support vector machine (SVM)—were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image‐based features contributed to accuracy across all models and analyses—two dMRI‐based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI‐based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.