A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets.

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.

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.

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.

Neurometabolite concentration and clinical features of chronic alcohol use: a proton magnetic resonance spectroscopy study.

Chronic, heavy alcohol consumption may affect the concentration of neurometabolites assessed with proton magnetic resonance spectroscopy ((1)H-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.

Rare copy number deletions predict individual variation in human brain metabolite concentrations in individuals with alcohol use disorders.

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).

A baseline for the multivariate comparison of resting-state networks.

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.

Perturbation of the glutamate-glutamine system in alcohol dependence and remission.

As acute ethanol exposure inhibits N-methyl-D-aspartate glutamate (Glu) receptors, sudden withdrawal from chronic alcohol use may lead to an increased activation of these receptors with excitotoxic effects. In the longer term, brain levels of Glu and its metabolites, such as glutamine (Gln), are likely to be chronically altered by alcohol, possibly providing a measure of overall abnormal Glu-Gln cycling. However, few studies have assessed concentrations of these metabolites in clinical populations of individuals with alcohol use disorders.

Mining the mind research network: a novel framework for exploring large scale, heterogeneous translational neuroscience research data sources.

A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult.