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Padhraic Smyth

Publications and source records attributed to Padhraic Smyth.

3 recordsLinked to original sources

Imaging phenotypes and genotypes in schizophrenia.

Schizophrenia is associated with subtle structural and functional brain abnormalities. Both recent and classical data suggest that it is a heterogeneous disorder that is clearly heritable. The cause and course of schizophrenia are poorly understood, and classical categories of clinical symptoms have not been particularly useful in identifying its pathophysiology or predicting its treatment. The possible genetic risk factors for schizophrenia are numerous; however, the connection between the genotype and the time-course, or the multifaceted symptoms of the disease, has yet to be established. Brain imaging methods that study the structure or function of the cortical and subcortical regions have also identified distinct patterns that distinguish schizophrenics from controls, and that may identify meaningful subtypes of schizophrenia. The predictive relationship between these imaging phenotypes and disease characteristics such as treatment response is only beginning to be revealed. The emergence of the field of imaging genetics, combining genetic, and neuroimaging data, holds much promise for the deeper understanding and improved treatment of diseases such as schizophrenia. In this article we review some of the key findings in imaging phenotyping and genotyping of schizophrenia, and the initial endeavors at their combination into more meaningful and predictive patterns, or endophenotypes identifying the relationships among clinical symptoms, course, genes, and the underlying pathophysiology.

Brain↗

Parametric response surface models for analysis of multi-site fMRI data.

Analyses of fMRI brain data are often based on statistical tests applied to each voxel or use summary statistics within a region of interest (such as mean or peak activation). These approaches do not explicitly take into account spatial patterns in the activation signal. In this paper, we develop a response surface model with parameters that directly describe the spatial shapes of activation patterns. We present a stochastic search algorithm for parameter estimation. We apply our method to data from a multi-site fMRI study, and show how the estimated parameters can be used to analyze different sources of variability in image generation, both qualitatively and quantitatively, based on spatial activation patterns.

Algorithms↗

Identification of hair cycle-associated genes from time-course gene expression profile data by using replicate variance.

The hair-growth cycle is an example of a cyclic process that is well characterized morphologically but understood incompletely at the molecular level. As an initial step in discovering regulators in hair-follicle morphogenesis and cycling, we used DNA microarrays to profile mRNA expression in mouse back skin from eight representative time points. We developed a statistical algorithm to identify the set of genes expressed within skin that are associated specifically with the hair-growth cycle. The methodology takes advantage of higher replicate variance during asynchronous hair cycles in comparison with synchronous cycles. More than one-third of genes with detectable skin expression showed hair-cycle-related changes in expression, suggesting that many more genes may be associated with the hair-growth cycle than have been identified in the literature. By using a probabilistic clustering algorithm for replicated measurements, these genes were grouped into 30 time-course profile clusters, which fall into four major classes. Distinct genetic pathways were characteristic for the different time-course profile clusters, providing insights into the regulation of hair-follicle cycling and suggesting that this approach is useful for identifying hair follicle regulators. In addition to revealing known hair-related genes, we identified genes that were not previously known to be hair cycle-associated and confirmed their temporal and spatial expression patterns during the hair-growth cycle by quantitative real-time PCR and in situ hybridization. The same computational approach should be generally useful for identifying genes associated with cyclic processes from complex tissues.

Algorithms↗