Biomedical subjects

Rita Goldstein

Publications and source records attributed to Rita Goldstein.

3 recordsLinked to original sources

PubMed2006

Evidence that brain MAO A activity does not correspond to MAO A genotype in healthy male subjects.

BACKGROUND: A functional polymorphism in the promoter region of the monoamine oxidase A (MAO A) gene has two common alleles that are referred to as the high and low MAO A genotypes. We report the first in vivo human study to determine whether there is an association between MAO A genotype and brain MAO A activity in healthy male subjects. METHODS: Brain MAO A activity was measured with positron emission tomography and [(11)C]clorgyline in 38 healthy adult male nonsmokers genotyped for MAO A polymorphism. RESULTS: There was no significant difference in brain MAO A activity between the high (n = 26) and low (n = 12) MAO A genotypes. CONCLUSIONS: The lack of an association between the high and low MAO A genotype and brain MAO A activity suggests that this polymorphism by itself does not contribute to differences in brain MAO A activity in healthy adult male subjects.

Adult↗

PubMed2006

High levels of dopamine D2 receptors in unaffected members of alcoholic families: possible protective factors.

CONTEXT: Predisposition to alcoholism is likely an interaction between genetic and environmental factors that confer vulnerability and protection. Alcoholic subjects have low levels of dopamine D(2) receptors in striatum, and increasing D(2) receptor levels in laboratory animals reduces alcohol consumption. OBJECTIVES: To test whether high levels of D(2) receptors may be protective against alcoholism and whether this is mediated by their modulation of activity in orbitofrontal cortex and cingulate gyrus (regions involved in salience attribution, emotional reactivity, and inhibitory control). DESIGN: Research (nonalcoholic subjects with a family history of alcoholism) and comparison (nonalcoholic subjects with a negative family history) sample. SETTING: Outpatient setting. PARTICIPANTS: Fifteen nonalcoholic subjects who had an alcoholic father and at least 2 other first- or second-degree relatives who were alcoholics (family-positive group) and 16 nonalcoholic controls with no family history of alcoholism (family-negative group). MAIN OUTCOME MEASURES: Results of positron emission tomography with raclopride C 11 to assess D(2) receptors and with fludeoxyglucose F 18 to assess brain glucose metabolism (marker of brain function). Personality measures were obtained with the Multidimensional Personality Questionnaire. RESULTS: Availability of D(2) receptors was significantly higher in caudate and ventral striatum in family-positive than family-negative subjects. In family-positive but not family-negative subjects, striatal D(2) receptors were associated with metabolism in anterior cingulate (Brodmann area 24/25) and orbitofrontal (Brodmann area 11) and prefrontal (Brodmann area 9/10) cortices, and with personality scores of positive emotionality. CONCLUSIONS: The higher-than-normal D(2) receptor availability in nonalcoholic members of alcoholic families supports the hypothesis that high levels of D(2) receptors may protect against alcoholism. The significant associations between D(2) receptors and metabolism in frontal regions involved with emotional reactivity and executive control suggest that high levels of D(2) receptors could protect against alcoholism by regulating circuits involved in inhibiting behavioral responses and in controlling emotions.

Adult↗

PubMed2005

Exploiting temporal information in functional magnetic resonance imaging brain data.

Functional Magnetic Resonance Imaging(fMRI) has enabled scientists to look into the active human brain, leading to a flood of new data, thus encouraging the development of new data analysis methods. In this paper, we contribute a comprehensive framework for spatial and temporal exploration of fMRI data, and apply it to a challenging case study: separating drug addicted subjects from healthy non-drug-using controls. To our knowledge, this is the first time that learning on fMRI data is performed explicitly on temporal information for classification in such applications. Experimental results demonstrate that, by selecting discriminative features, group classification can be successfully performed on our case study although training data are exceptionally high dimensional, sparse and noisy fMRI sequences. The classification performance can be significantly improved by incorporating temporal information into machine learning. Both statistical and neuroscientific validation of the method's generalization ability are provided. We demonstrate that incorporation of computer science principles into functional neuroimaging clinical studies, facilitates deduction about the behavioral probes from the brain activation data, thus providing a valid tool that incorporates objective brain imaging data into clinical classification of psychopathologies and identification of genetic vulnerabilities.

Algorithms↗