One-dimensional (1D) 1 H nuclear magnetic resonance (NMR) spectroscopy is used extensively for high-throughput analysis of metabolites in biological fluids and tissue extracts. Typically, such spectra are treated as multivariate statistical objects rather than as collections of quantifiable metabolites. We report here a two-dimensional (2D) 1 H-13 C NMR strategy (Fast Metabolite Quantification, FMQ by NMR) for identifying and quantifying the ∼40 most abundant metabolites in biological samples. To validate this technique, we prepared mixtures of synthetic compounds and extracts from Arabidopsis thaliana, Saccharomyces cerevisiae and Medicago sativa. We show that accurate (technical error 2.7%) molar concentrations can be determined in 12 minutes using our quantitative 2D 1 H-13 C NMR strategy. In contrast, traditional 1D 1 H NMR analysis resulted in 16.2% technical error under nearly ideal conditions. We propose FMQ by NMR as a practical alternative to 1D 1 H NMR for metabolomics studies in which 200-400 mg (preextraction dry weight) samples can be obtained.One-dimensional (1D) 1 H NMR spectroscopy has been used for decades as an analytical tool for identifying small molecules and measuring their concentrations. 1, 2 Traditionally, quantitative analysis by NMR has been restricted to relatively simple mixtures with minimal peak overlap. In these applications, 1D 1 H NMR is a natural choice, because its peaks scale linearly with concentration and its analytical precision is usually independent of the chemical properties of target molecules. Recently, interest has surged in using NMR for high-throughput analysis of complex biological processes at the metabolic level. 3, 4 These studies, defined as "metabolomics" or "metabonomics", place an emphasis on biomarker discovery or disease classification and are typically centered on unfractionated biological fluids and tissue extracts. 1D 1 H NMR spectra of these samples typically contain hundreds of overlapping resonances (Figure 1) that make traditional NMR-based analytical practices, such as resonance assignment and accurate peak integration, a challenging prospect. As a result, sophisticated statistical tools have been developed to translate spectral data into biologically meaningful information. 4, 5All statistical tools used for analyzing complex spectra face the same fundamental barrier: overlapped peaks do not scale in the discrete linear fashion that typifies well-isolated peaks. They scale as the sum of the total overlapped resonance. Consequently, multivariate and correlation statistics are reporters of overlapped spectral density, not concentrations of specific compounds. Although peak overlap does not interfere with the reproducibility of traditional analyses, 6 it does prevent accurate quantification. Two approaches can be used to overcome this barrier, one mathematical the other experimental. The mathematical approach is to fit overlapped 1 D NMR spectra with modeled peaks. This approach has been successfully applied by Weljie and co-workers. 7 The e...

Background This study aimed to extend previous work on decision-making deficits in anorexia nervosa (AN) by using a longitudinal design to examine decision-making before and after weight restoration. Methods Participants were 22 women with AN and 20 healthy comparison participants who completed the Iowa Gambling Task (IGT). Decision-making was assessed both before and after weight restoration in a subset of 14 AN patients. Self-report and interview assessments were used to measure psychological correlates of decision-making performance including depression, anxiety, and eating disorder symptoms, and magnetic resonance imaging (MRI) scans were conducted to explore associations between brain volume in the orbitofrontal cortex (OFC) and decision-making in individuals with AN. Results Currently ill AN patients performed worse on the IGT compared to the control group. Although decision-making performance did not improve significantly with weight restoration in the full AN sample, AN patients who were poor performers at baseline did improve task performance with weight-restoration. When actively ill, lower body mass index (BMI) and decreased left medial OFC volume were significantly associated with worse IGT performance, and these associations were no longer significant after weight restoration. Conclusions Findings suggest that decision-making deficits in AN in the acute phase of illness are associated with low weight and decreased left medial OFC volume, but increases in brain volume and BMI may not have been sufficient to improve decision-making in all patients. Findings contribute to a model for understanding how some patients may sustain self-starvation, and future work should examine whether decision-making deficits predict relapse.

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The current study examined the relationship between white matter integrity as indexed by diffusion tensor imaging and negative symptom severity in schizophrenia. The current study included statistical controls for age effects on the relationship of interest, a major weakness of the existing literature on the subject. Participants included 59 chronic schizophrenia patients, and 31 first-episode schizophrenia patients. Diffusion-weighted neuroimaging was used to calculate fractional anisotropy (FA) in each major brain region (frontal, temporal, parietal, and occipital lobes). Negative symptoms were measured using the Scale for the Assessment of Negative Symptoms (SANS) in all schizophrenia patients. Significant bivariate correlations were observed between global SANS scores and global FA, as well as in most brain regions. These relationships appeared to be driven by SANS items measuring facial expressiveness, poor eye contact, affective flattening , inappropriate affect, poverty of speech, poverty of speech content, alogia, and avolition. However, upon addition of age as a covariate, the observed relationships became non-significant. Further analysis revealed very strong age effects on both FA and SANS scores in the current sample. The findings of this study refute previous reports of significant relationships between DTI variables and negative symptoms in schizophrenia, and they suggest an important confounding variable to be considered in future studies in this population.

BackgroundPsychotic depression is arguably the most diagnostically stable subtype of major depressive disorder, and an attractive target of study in a famously heterogeneous mental illness. Previous imaging studies have identified abnormal volumes of the hippocampus, amygdala, and subcallosal region of the anterior cingulate cortex (scACC) in psychotic depression, though studies have not yet examined the role of family history of depression in these relationships.Methods20 participants with psychotic depression preparing to undergo electroconvulsive therapy and 20 healthy comparison participants (13 women and 7 men in each group) underwent structural brain imaging in a 1.5 T MRI scanner. 15 of the psychotic depression group had a first-degree relative with diagnosed affective disorders, while the healthy control group had no first-degree relatives with affective disorders. Depression severity was assessed with the Hamilton Depression Rating Scale and duration of illness was assessed in all patients. Automated neural nets were used to isolate the hippocampi and amygdalae in each scan, and an established manual method was used to parcellate the anterior cingulate cortex into dorsal, rostral, subcallosal, and subgenual regions. The volumes of these regions were compared between groups. Effects of laterality and family history of affective disorders were examined as well.ResultsPatients with psychotic depression had significantly smaller left scACC and bilateral hippocampal volumes, while no group differences in other anterior cingulate cortex subregions or amygdala volumes were present. Hippocampal atrophy was found in all patients with psychotic depression, but reduced left scACC volume was found only in the patients with a family history of depression.ConclusionsPatients with psychotic depression showed significant reduction in hippocampal volume bilaterally, perhaps due to high cortisol states associated with this illness. Reduced left scACC volume may be a vulnerability factor related to family history of depression.