Model Based Principal Component Analysis with Application to Functional Magnetic Resonance Imaging.

Functional Magnetic Resonance Imaging (fMRI) has allowed better understanding of human brain organization and function by making it possible to record either autonomous or stimulus induced brain activity. After appropriate preprocessing fMRI produces a large spatio-temporal data set, which requires sophisticated signal processing. The aim of the signal processing is usually to produce spatial maps of statistics that capture the effects of interest, e.g., brain activation, time delay between stimulation and activation, or connectivity between brain regions. Two broad signal processing approaches have been pursued; univoxel methods and multivoxel methods. This proposal will focus on multivoxel methods and review Principal Component Analysis (PCA), and other closely related methods, and describe their advantages and disadvantages in fMRI research. These existing multivoxel methods have in common that they are exploratory, i.e., they are not based on a statistical model. A crucial observation which is central to this thesis, is that there is in fact an underlying model behind PCA, which we call noisy PCA (nPCA). In the main part of this thesis, we use nPCA to develop methods that solve three important problems in fMRI. 1) We introduce a novel nPCA based spatio-temporal model that combines the standard univoxel regression model with nPCA and automatically recognizes the temporal smoothness of the fMRI data. Furthermore, unlike standard univoxel methods, it can handle non-stationary noise. 2) We introduce a novel sparse variable PCA (svPCA) method that automatically excludes whole voxel timeseries, and yields sparse eigenimages. This is achieved by a novel nonlinear penalized likelihood function which is optimized. An iterative estimation algorithm is proposed that makes use of geodesic descent methods. 3) We introduce a novel method based on Stein’s Unbiased Risk Estimator (SURE) and Random Matrix Theory (RMT) to select the number of principal components for the increasingly important case where the number of observations is of similar order as the number of variables.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57638/2/mulfarss_1.pd

Model-based demosaicking for acquisitions by a RGBW color filter array

Microsatellites and drones are often equipped with digital cameras whose sensing system is based on color filter arrays (CFAs), which define a pattern of color filter overlaid over the focal plane. Recent commercial cameras have started implementing RGBW patterns, which include some filters with a wideband spectral response together with the more classical RGB ones. This allows for additional light energy to be captured by the relevant pixels and increases the overall SNR of the acquisition. Demosaicking defines reconstructing a multi-spectral image from the raw image and recovering the full color components for all pixels. However, this operation is often tailored for the most widespread patterns, such as the Bayer pattern. Consequently, less common patterns that are still employed in commercial cameras are often neglected. In this work, we present a generalized framework to represent the image formation model of such cameras. This model is then exploited by our proposed demosaicking algorithm to reconstruct the datacube of interest with a Bayesian approach, using a total variation regularizer as prior. Some preliminary experimental results are also presented, which apply to the reconstruction of acquisitions of various RGBW cameras

Time‐ and Frequency‐Domain Characteristics of Atrial Electrograms During Sinus Rhythm and Atrial Fibrillation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87064/1/j.1540-8167.2011.02022.x.pd

Neuroimaging findings in neurodevelopmental copy number variants: identifying molecular pathways to convergent phenotypes

Genomic copy number variants (CNVs) are associated with a high risk of neurodevelopmental disorders. A growing body of genetic studies suggests that these high-risk genetic variants converge in common molecular pathways, and that common pathways also exist across clinically distinct disorders, such as schizophrenia and autism spectrum disorder. A key question is how common molecular mechanisms converge into similar clinical outcomes. We review emerging evidence for convergent cognitive and brain phenotypes across distinct CNVs. Multiple CNVs were shown to have similar effects on core sensory, cognitive and motor traits. Emerging data from multi-site neuroimaging studies have provided valuable information on how these CNVs affect brain structure and function. However, most of these studies examined one CNV at a time, making it difficult to fully understand the proportion of shared brain effects. Recent studies have started to combine neuroimaging data from multiple CNV carriers and identified similar brain effects across CNVs. Some early findings also support convergence in CNV animal models. Systems biology, through integration of multi-level data, provides new insights into convergent molecular mechanisms across genetic risk variants (e.g., altered synaptic activity). However, the link between such key molecular mechanisms and convergent psychiatric phenotypes is still unknown. In order to better understand this link, we need new approaches that integrate human molecular data with neuroimaging, cognitive, and animal models data, while taking into account critical developmental timepoints. Identifying risk mechanisms across genetic loci can elucidate the pathophysiology of neurodevelopmental disorders and identify new therapeutic targets for cross-disorder applications

Complex Electrograms Within the Coronary Sinus: Time- and Frequency-Domain Characteristics, Effects of Antral Pulmonary Vein Isolation, and Relationship to Clinical Outcome in Patients with Paroxysmal and Persistent Atrial Fibrillation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73243/1/j.1540-8167.2008.01175.x.pd

Reciprocal white matter changes associated with copy number variation at 15q11.2 BP1-BP2: A diffusion tensor imaging study

Background The 15q11.2 BP1-BP2 cytogenetic region has been associated with learning and motor delays, autism, and schizophrenia. This region includes a gene that codes for the cytoplasmic FMR1 interacting protein 1 (CYFIP1). The CYFIP1 protein is involved in actin cytoskeletal dynamics and interacts with the fragile X mental retardation protein. Absence of fragile X mental retardation protein causes fragile X syndrome. Because abnormal white matter microstructure has been reported in both fragile X syndrome and psychiatric disorders, we looked at the impact of 15q11.2 BP1-BP2 dosage on white matter microstructure. Methods Combining a brain-wide voxel-based approach and a regional-based analysis, we analyzed diffusion tensor imaging data from healthy individuals with the deletion (n = 30), healthy individuals with the reciprocal duplication (n = 27), and IQ-matched control subjects with no large copy number variants (n = 19), recruited from a large genotyped population sample. Results We found global mirror effects (deletion > control > duplication) on fractional anisotropy. The deletion group showed widespread increased fractional anisotropy when compared with duplication. Regional analyses revealed a greater effect size in the posterior limb of the internal capsule and a tendency for decreased fractional anisotropy in duplication. Conclusions These results show a reciprocal effect of 15q11.2 BP1-BP2 on white matter microstructure, suggesting that reciprocal chromosomal imbalances may lead to opposite changes in brain structure. Findings in the deletion overlap with previous white matter differences reported in fragile X syndrome patients, suggesting common pathogenic mechanisms derived from disruptions of cytoplasmic CYFIP1-fragile X mental retardation protein complexes. Our data begin to identify specific components of the 15q11.2 BP1-BP2 phenotype and neurobiological mechanisms of potential relevance to the increased risk for disorder

Large-scale plasma proteomics comparisons through genetics and disease associations

Publisher Copyright: © 2023, The Author(s).High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project 1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people 2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.Peer reviewe