Biomedical subjects

William S Rayens

Publications and source records attributed to William S Rayens.

4 recordsLinked to original sources

PubMed2004

Structure-seeking multilinear methods for the analysis of fMRI data.

In comprehensive fMRI studies of brain function, the data structures often contain higher-order ways such as trial, task condition, subject, and group in addition to the intrinsic dimensions of time and space. While multivariate bilinear methods such as principal component analysis (PCA) have been used successfully for extracting information about spatial and temporal features in data from a single fMRI run, the need to unfold higher-order data sets into bilinear arrays has led to decompositions that are nonunique and to the loss of multiway linkages and interactions present in the data. These additional dimensions or ways can be retained in multilinear models to produce structures that are unique and which admit interpretations that are neurophysiologically meaningful. Multiway analysis of fMRI data from multiple runs of a bilateral finger-tapping paradigm was performed using the parallel factor (PARAFAC) model. A trilinear model was fitted to a data cube of dimensions voxels by time by run. Similarly, a quadrilinear model was fitted to a higher-way structure of dimensions voxels by time by trial by run. The spatial and temporal response components were extracted and validated by comparison to results from traditional SVD/PCA analyses based on scenarios of unfolding into lower-order bilinear structures.

Analysis of Variance↗

PubMed2004

Machine learning based pattern recognition applied to microarray data.

MOTIVATION: Microarrays have allowed the expression level of thousands of genes or proteins to be measured simultaneously. Data sets generated by these arrays consist of a small number of observations (e.g., 20-100 samples) on a very large number of variables (e.g., 10,000 genes or proteins). The observations in these data sets often have other attributes associated with them such as a class label denoting the pathology of the subject. Finding the genes or proteins that are correlated to these attributes is often a difficult task since most of the variables do not contain information about the pathology and as such can mask the identity of the relevant features. We describe a genetic algorithm (GA) that employs both supervised and unsupervised learning to mine gene expression and proteomic data. The pattern recognition GA selects features that increase clustering, while simultaneously searching for features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the features chosen by the GA contain information primarily about differences between classes in the data set. The principal component analysis routine embedded in the fitness function of the GA acts as an information filter, significantly reducing the size of the search space since it restricts the search to feature sets whose principal component plots show clustering on the basis of class. The algorithm integrates aspects of artificial intelligence and evolutionary computations to yield a smart one pass procedure for feature selection, clustering, classification, and prediction.

Algorithms↗

PubMed2002

Functional MRI studies in awake rhesus monkeys: methodological and analytical strategies.

Functional imaging of the non-human primate brain in awake animals is now feasible because of recent methodological advances. Here we detail our procedures for conducting functional MRI (fMRI) studies in rhesus monkeys. Our emphasis has been on analyzing drug-evoked responses within and across test groups, meaning that techniques have had to be developed for training and testing relatively large groups of animals. Group size is important as unbiased estimates are best derived from analyzing responses in multiple animals with replicate scans per animal due to partial volume errors in evaluating small brain regions and motion artifacts during scanning. While the procedures presented here were developed for mapping responses obtained from stimulating dopaminergic systems, much of the methodology is generally applicable for non-human primate fMRI studies and addresses specific problems encountered in imaging awake animals. These are (1) adapting animals to an MRI environment, (2) minimizing head movements, (3) reducing ambient scanning noise levels, and (4) developing multivariate methods of image data analysis suitable for eliciting the dynamic brain response while (5) detecting and deleting outlying observations due to motion artifacts. Procedures are demonstrated for first pre-processing and analyzing responses in a voxel-based approach in a single animal and then proceeding to analyze responses across animals and replicate scans for regions of interest. Collectively, the procedures described provide an approach for fMRI mapping of elicited responses using conventional 1.5T MR scanners.

Animals↗

PubMed2002

Structural differences in the NOE-derived structure of G-T mismatched DNA relative to normal DNA are correlated with differences in (13)C relaxation-based internal dynamics.

Detailed description of the characteristics of mismatched DNA that are distinct from normal DNA is vital to the understanding of how mismatch repair proteins are able to recognize and repair these DNA lesions. To this end, we have used nuclear Overhauser effect spectroscopy (NOESY)-based distance restraints and (13)C relaxation measurements to solve the solution structures and measure some of the internal dynamics of the G-T mismatched DNA oligomer d(CCATGCGTGG)(2) (GT) and its parent DNA sequence d(CCACGCGTGG)(2) (GC). In GT, the mismatched G7 is structurally perturbed much more than the mismatched T4 relative to their corresponding bases in GC. The degree of G7 displacement differs from previous high-resolution structures of G-T mismatch-containing B-DNA, suggesting a dependence of G-T mismatch-induced structural perturbation on sequence context. The internal dynamics of GC and GT differ on multiple timescales. The mismatched G7 of GT contains spins that decrease significantly in order in GT compared to GC, while spins in C6, T8, and A3 have significantly higher order in GT compared to GC. Linear correlations between helical parameters of GC and GT and the order of C-1' and aromatic methine carbon atoms relate differences in internal dynamics to the structures quantitatively. The dynamic differences between the normal and mismatched DNA signify changes in local flexibility that may be exploited by the mismatch repair system to bind mismatched DNA preferentially while ignoring normal DNA.

Base Pair Mismatch↗