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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.

Chronic, heavy alcohol consumption may affect the concentration of neurometabolites assessed with proton magnetic resonance spectroscopy (1H-MRS). We investigated the largest sample reported to date (N = 213) with the primary goal of determining how specific clinical features impact neurometabolite concentrations in an anterior cingulate gray matter voxel. This community dwelling sample included both treatment seeking and non-treatment seeking individuals. A healthy control group (N = 66) was matched for age and education. In multivariate analyses predicting neurometabolite concentrations, the heavy drinking group had greater concentrations overall. An age by group interaction was noted, as group difference across neurometabolites increased with age. More years drinking, but not more drinks per drinking day (DPDD) predicted greater concentrations of choline-containing compounds (Cho), creatine-phosphocreatine (Cre), glutamate-glutamine (Glx), and n-acetyl-aspartate (NAA). The effects of other clinical variables (depression, cigarette smoking, marijuana use) were negligible. After controlling for DPDD and years drinking, treatment-seeking status had no impact on neurometabolites. In the very oldest portion of the sample (mean age = 50), however, a negative relationship was seen between NAA and years drinking. These results suggest that the nature of neurometabolite abnormalities in chronic heavy drinkers may vary as a function of duration of abuse.

In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.

Introduction 1H-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 1H-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.

Background Although variations in neurometabolite concentrations occur in diverse neuropsychiatric and neurodegenerative disorders, little is known about the nature of underlying genetic influences. The current study investigated the importance of a specific type of genetic mutation, copy number variation (CNV), for neurometabolite concentrations in a bilateral anterior cingulate voxel. Methods These neurometabolic signals were quantified using proton magnetic resonance spectroscopy ( 1 H-MRS): N-acetylaspartate (NAA), creatine-phosphocreatine (Cre), glutamate/glutamine (Glx), myoinositol (mI), and phosphorylcholine-glycerol phosphorylcholine (Cho). Genetic data were collected using the Illumina 1MDuoBeadChip Array from a sample adults with alcohol use disorders ( n = 146). Results The number of base pairs lost through rare copy number deletions (occurring in less than 5% of our sample) predicted lower NAA, Cre, mI, and Glx. More total rare deletions also predicted lower NAA, Cre, and Glx. Principal components analyses of the five neurometabolites identified two correlated components, the first comprised of NAA, Glx, and Cre, and the second comprised of Cho, mI, and to a lesser extent, Cre. The number and length of rare deletions were correlated with the first component, capturing approximately 10% of phenotypic variance, but not the second component. Conclusions These results suggest that mutation load affects neurometabolite concentrations, potentially increasing risk for neuropsychiatric disorders. The greater effect of CVNs on NAA, Glx, and Cre may reflect a greater sensitivity to the effects of mutations (i.e., reduced canalization) for neurometabolites related to metabolic activity and cellular energetics, due to extensive recent selection pressure on these phenotypes in the human lineage.