Application of ICA to realistically simulated (1)H-MRS data.

INTRODUCTION: (1)H-MRS signals from brain tissues capture information on in vivo brain metabolism and neuronal biomarkers. This study aims to advance the use of independent component analysis (ICA) for spectroscopy data by objectively comparing the performance of ICA and LCModel in analyzing realistic data that mimics many of the known properties of in vivo data. METHODS: This work identifies key features of in vivo (1)H-MRS signals and presents methods to simulate realistic data, using a basis set of 12 metabolites typically found in the human brain. The realistic simulations provide a much needed ground truth to evaluate performances of various MRS analysis methods. ICA is applied to collectively analyze multiple realistic spectra and independent components identified with our generative model to obtain ICA estimates. These same data are also analyzed using LCModel and the comparisons between the ground-truth and the analysis estimates are presented. The study also investigates the potential impact of modeling inaccuracies by incorporating two sets of model resonances in simulations. RESULTS: The simulated fid signals incorporating line broadening, noise, and residual water signal closely resemble the in vivo signals. Simulation analyses show that the resolution performances of both LCModel and ICA are not consistent across metabolites and that while ICA resolution can be improved for certain resonances, ICA is as effective as, or better than, LCModel in resolving most model resonances. CONCLUSION: The results show that ICA can be an effective tool in comparing multiple spectra and complements existing approaches for providing quantified estimates.

A three-way parallel ICA approach to analyze links among genetics, brain structure and brain function.

Multi-modal data analysis techniques, such as the Parallel Independent Component Analysis (pICA), are essential in neuroscience, medical imaging and genetic studies. The pICA algorithm allows the simultaneous decomposition of up to two data modalities achieving better performance than separate ICA decompositions and enabling the discovery of links between modalities. However, advances in data acquisition techniques facilitate the collection of more than two data modalities from each subject. Examples of commonly measured modalities include genetic information, structural magnetic resonance imaging (MRI) and functional MRI. In order to take full advantage of the available data, this work extends the pICA approach to incorporate three modalities in one comprehensive analysis. Simulations demonstrate the three-way pICA performance in identifying pairwise links between modalities and estimating independent components which more closely resemble the true sources than components found by pICA or separate ICA analyses. In addition, the three-way pICA algorithm is applied to real experimental data obtained from a study that investigate genetic effects on alcohol dependence. Considered data modalities include functional MRI (contrast images during alcohol exposure paradigm), gray matter concentration images from structural MRI and genetic single nucleotide polymorphism (SNP). The three-way pICA approach identified links between a SNP component (pointing to brain function and mental disorder associated genes, including BDNF, GRIN2B and NRG1), a functional component related to increased activation in the precuneus area, and a gray matter component comprising part of the default mode network and the caudate. Although such findings need further verification, the simulation and in-vivo results validate the three-way pICA algorithm presented here as a useful tool in biomedical data fusion applications.

Group independent component analysis of MR spectra.

This study investigates the potential of independent component analysis (ICA) to provide a data-driven approach for group level analysis of magnetic resonance (MR) spectra. ICA collectively analyzes data to identify maximally independent components, each of which captures covarying resonances, including those from different metabolic sources. A comparative evaluation of the ICA approach with the more established LCModel method in analyzing two different noise-free, artifact-free, simulated data sets of known compositions is presented. The results from such ideal simulations demonstrate the ability of data-driven ICA to decompose data and accurately extract components resembling modeled basis spectra from both data sets, whereas the LCModel results suffer when the underlying model deviates from assumptions, thus highlighting the sensitivity of model-based approaches to modeling inaccuracies. Analyses with simulated data show that independent component weights are good estimates of concentrations, even of metabolites with low intensity singlet peaks, such as scyllo-inositol. ICA is also applied to single voxel spectra from 193 subjects, without correcting for baseline variations, line-width broadening or noise. The results provide evidence that, despite the presence of confounding artifacts, ICA can be used to analyze in vivo spectra and extract resonances of interest. ICA is a promising technique for decomposing MR spectral data into components resembling metabolite resonances, and therefore has the potential to provide a data-driven alternative to the use of metabolite concentrations derived from curve-fitting individual spectra in making group comparisons.

Association of genetic copy number variations at 11 q14.2 with brain regional volume differences in an alcohol use disorder population.

This study investigates the relationship between genetic copy number variations and brain volume differences in an alcohol use disorder (AUD) population. We hypothesized that copy number variations may influence subject's risk for alcohol use disorders through variations in regional gray and white matter brain volumes. Since genetic influences upon behavior are the result of many complicated interactions we focus on differences in brain volume as a putative intermediate phenotype between genetic variation and behavior. Copy number variation, alcohol use assessments and brain structural magnetic resonance images from 283 subjects, 199 male and 84 females who were enrolled in two AUD studies were obtained and analyzed using a combination of the Freesurfer image analysis suite and independent component analysis. Because brain volume varies by age we compared participant's volume variation with that derived from a control cohort of 75 subjects. In addition we also regressed out the possible brain volume changes induced by long term alcohol consumption. Small cerebral cortex, cerebellar and caudate along with large cerebral white matter and 5th ventricle volumes are shown to be significantly associated with increased AUD severity. When these volume variations are compared with control subject volumes; the variations seen in subjects with AUD are markedly different from normal aging effects. CNVs at 11 q14.2 are marginally (p < 0.05 uncorrected) correlated with such brain volume variations and the correlation holds true after controlling for long-term alcohol consumption; deletion carriers have smaller cerebral cortex, cerebellar, caudate and larger cerebral white matter and 5th ventricle volumes than insertion carriers or subjects with no variation in this region. Similarly, deletion carriers also demonstrate higher AUD severity scores than insertion carriers or subjects with no variation. The results presented here suggest that copy number variation and in particular the variation at chromosome 11 q14.2 may have an impact in brain volume variation, potentially influencing AUD behavior.

An ICA with reference approach in identification of genetic variation and associated brain networks.

To address the statistical challenges associated with genome-wide association studies, we present an independent component analysis (ICA) with reference approach to target a specific genetic variation and associated brain networks. First, a small set of single nucleotide polymorphisms (SNPs) are empirically chosen to reflect a feature of interest and these SNPs are used as a reference when applying ICA to a full genomic SNP array. After extracting the genetic component maximally representing the characteristics of the reference, we test its association with brain networks in functional magnetic resonance imaging (fMRI) data. The method was evaluated on both real and simulated datasets. Simulation demonstrates that ICA with reference can extract a specific genetic factor, even when the variance accounted for by such a factor is so small that a regular ICA fails. Our real data application from 48 schizophrenia patients (SZs) and 40 healthy controls (HCs) include 300K SNPs and fMRI images in an auditory oddball task. Using SNPs with allelic frequency difference in two groups as a reference, we extracted a genetic component that maximally differentiates patients from controls (p < 4 × 10(-17)), and discovered a brain functional network that was significantly associated with this genetic component (p < 1 × 10(-4)). The regions in the functional network mainly locate in the thalamus, anterior and posterior cingulate gyri. The contributing SNPs in the genetic factor mainly fall into two clusters centered at chromosome 7q21 and chromosome 5q35. The findings from the schizophrenia application are in concordance with previous knowledge about brain regions and gene function. All together, the results suggest that the ICA with reference can be particularly useful to explore the whole genome to find a specific factor of interest and further study its effect on brain.

A pipeline for copy number variation detection based on principal component analysis.

DNA copy number variation (CNV), an important structural variation, is known to be pervasive in the human genome and the determination of CNVs is essential to understanding their potential effects on the susceptibility to diseases. However, CNV detection using SNP array data is challenging due to the low signal-to-noise ratio. In this study, we propose a principal component analysis (PCA) based approach for data correction, and present a novel processing pipeline for reliable CNV detection. Tested data include both simulated and real SNP array datasets. Simulations demonstrate a substantial reduction in the false positive rate of CNV detection after PCA-correction. And we also observe a significant improvement in data quality in real SNP array data after correction.

Sparse Canonical Correlation Analysis Applied to fMRI and Genetic Data Fusion.

Fusion of functional magnetic resonance imaging (fMRI) and genetic information is becoming increasingly important in biomarker discovery. These studies can contain vastly different types of information occupying different measurement spaces and in order to draw significant inferences and make meaningful predictions about genetic influence on brain activity; methodologies need to be developed that can accommodate the acute differences in data structures. One powerful, and occasionally overlooked, method of data fusion is canonical correlation analysis (CCA). Since the data modalities in question potentially contain millions of variables in each measurement, conventional CCA is not suitable for this task. This paper explores applying a sparse CCA algorithm to fMRI and genetic data fusion.