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Frost H.R.,Institute for Quantitative Biomedical science
Computational and Mathematical Methods in Medicine | Year: 2015

Despite significant improvements in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. In this study, a data driven approach is proposed for modeling this neurovascular coupling relationship from simultaneously acquired electroencephalographic (EEG) and near-infrared spectroscopic (NIRS) data. The approach uses gamma transfer functions to map EEG spectral envelopes that reflect time-varying power variations in neural rhythms to hemodynamics measured with NIRS during median nerve stimulation. The approach is evaluated first with simulated EEG-NIRS data and then by applying the method to experimental EEG-NIRS data measured from 3 human subjects. Results from the experimental data indicate that the neurovascular coupling relationship can be modeled using multiple sets of gamma transfer functions. By applying cluster analysis, statistically significant parameter sets were found to predict NIRS hemodynamics from EEG spectral envelopes. All subjects were found to have significant clustered parameters (P<0.05) for EEG-NIRS data fitted using gamma transfer functions. These results suggest that the use of gamma transfer functions followed by cluster analysis of the resulting parameter sets may provide insights into neurovascular coupling in human neuroimaging data. © 2015 M. Tanveer Talukdar et al.

Moore J.H.,Quantitative Medicine | Lari R.C.S.,Institute for Quantitative Biomedical science | Hill D.,Quantitative Medicine | Hibberd P.L.,Massachusetts General Hospital | Madan J.C.,Dartmouth Hitchcock Medical Center
Pacific Symposium on Biocomputing 2011, PSB 2011 | Year: 2011

High-throughput sequencing technology has opened the door to the study of the human microbiome and its relationship with health and disease. This is both an opportunity and a significant biocomputing challenge. We present here a 3D visualization methodology and freely-available software package for facilitating the exploration and analysis of high-dimensional human microbiome data. Our visualization approach harnesses the power of commercial video game development engines to provide an interactive medium in the form of a 3D heat map for exploration of microbial species and their relative abundance in different patients. The advantage of this approach is that the third dimension provides additional layers of information that cannot be visualized using a traditional 2D heat map. We demonstrate the usefulness of this visualization approach using microbiome data collected from a sample of premature babies with and without sepsis. © 2011 World Scientific Publishing Co. Pte. Ltd.

Tragante V.,University Utrecht | Moore J.H.,Norris Cotton Cancer Center | Moore J.H.,Institute for Quantitative Biomedical science | Asselbergs F.W.,University Utrecht | And 2 more authors.
Genetic Epidemiology | Year: 2014

The recently completed ENCODE project is a new source of information on metabolic activity, unveiling knowledge about evolution and similarities among species, refuting the myth that most DNA is "junk" and has no actual function. With this expansive resource comes a challenge: integrating these new layers of information into our current knowledge of single-nucleotide polymorphisms and previously described metabolic pathways with the aim of discovering new genes and pathways related to human diseases and traits. Further, we must determine which computational methods will be most useful in this pursuit. In this paper, we speculate over the possible methods that will emerge in this new, challenging field. © 2014 WILEY PERIODICALS, INC.

Kim N.C.,Institute for Quantitative Biomedical science | Andrews P.C.,Institute for Quantitative Biomedical science | Asselbergs F.W.,University Utrecht | Frost H.R.,Institute for Quantitative Biomedical science | And 6 more authors.
BioData Mining | Year: 2012

Background: It is increasingly clear that common human diseases have a complex genetic architecture characterized by both additive and nonadditive genetic effects. The goal of the present study was to determine whether patterns of both additive and nonadditive genetic associations aggregate in specific functional groups as defined by the Gene Ontology (GO). Results: We first estimated all pairwise additive and nonadditive genetic effects using the multifactor dimensionality reduction (MDR) method that makes few assumptions about the underlying genetic model. Statistical significance was evaluated using permutation testing in two genome-wide association studies of ALS. The detection data consisted of 276 subjects with ALS and 271 healthy controls while the replication data consisted of 221 subjects with ALS and 211 healthy controls. Both studies included genotypes from approximately 550,000 singlenucleotide polymorphisms (SNPs). Each SNP was mapped to a gene if it was within 500 kb of the start or end. Each SNP was assigned a p-value based on its strongest joint effect with the other SNPs. We then used the Exploratory Visual Analysis (EVA) method and software to assign a p-value to each gene based on the overabundance of significant SNPs at the ? = 0.05 level in the gene. We also used EVA to assign p-values to each GO group based on the overabundance of significant genes at the α = 0.05 level. A GO ategory was determined to replicate if that category was significant at the α = 0.05 level in both studies. We ound two GO categories that replicated in both studies. The first, Regulation of Cellular Component Organization and Biogenesis, a GO Biological Process, had p-values of 0.010 and 0.014 in the detection and replication studies, respectively. The second, Actin Cytoskeleton, a GO Cellular Component, had p- alues of 0.040 and 0.046 in the detection and replication studies, respectively. Conclusions: Pathway nalysis of pairwise genetic associations in two GWAS of sporadic ALS revealed a set of genes involved in cellular component organization and actin cytoskeleton, more specifically, that were not reported by prior GWAS. However, prior biological studies have implicated actin cytoskeleton in ALS and other motor neuron diseases. This study supports the idea that pathway-level analysis of GWAS data may discover important associations not revealed using conventional one-SNP-at-a-time pproaches. © 2012 Kim et al.; licensee BioMed Central Ltd.

Cowper-Sallari R.,Norris Cotton Cancer Center | Cowper-Sallari R.,Institute for Quantitative Biomedical science | Zhang X.,Norris Cotton Cancer Center | Zhang X.,Institute for Quantitative Biomedical science | And 10 more authors.
Nature Genetics | Year: 2012

Genome-wide association studies (GWAS) have identified thousands of SNPs that are associated with human traits and diseases. But, because the vast majority of these SNPs are located in non-coding regions of the genome, the mechanisms by which they promote disease risk have remained elusive. Employing a new methodology that combines cistromics, epigenomics and genotype imputation, we annotate the non-coding regions of the genome in breast cancer cells and systematically identify the functional nature of SNPs associated with breast cancer risk. Our results show that breast cancer risk-associated SNPs are enriched in the cistromes of FOXA1 and ESR1 and the epigenome of histone H3 lysine 4 monomethylation (H3K4me1) in a cancer-and cell type-specific manner. Furthermore, the majority of the risk-associated SNPs modulate the affinity of chromatin for FOXA1 at distal regulatory elements, thereby resulting in allele-specific gene expression, which is exemplified by the effect of the rs4784227 SNP on the TOX3 gene within the 16q12.1 risk locus. © 2012 Nature America, Inc. All rights reserved.

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