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J A Aston

Publications and source records attributed to J A Aston.

4 recordsLinked to original sources

Reduced dopamine D1 receptor binding in the ventral striatum of cigarette smokers.

Several drugs of abuse, including nicotine, are thought to exert their reinforcing effects through actions on the mesolimbic dopamine system. Animal and human studies suggest that chronic administration of addictive drugs may lead to impaired dopamine neurotransmission in the nucleus accumbens. We measured D1 receptor density in 11 smokers and 18 nonsmokers using positron emission tomography and the D1 receptor ligand [11C]SCH 23390. Ten of the smokers were scanned twice, once after overnight abstinence from cigarettes, and once while smoking at their usual rate, to account for possible acute effects of cigarette smoking on D1 receptor binding. In addition, eight control subjects were scanned twice to assess the reproducibility of the method. We used compartmental modeling to measure [11C]SCH 23390 binding potential, a measure of D1 receptor density. There were no differences in binding between abstinent and nonabstinent scans in smokers or in the two scans in controls. However, there was a significant reduction in [11C]SCH 23390 binding potential in smokers compared to nonsmokers in the striatum, most prominently in the ventral striatum. This suggests that there is a reduction in dopamine D1 receptor density in the ventral striatum of human cigarette smokers relative to nonsmokers, which implies that the postsynaptic mesolimbic dopamine system may be chronically underactive in smokers, either as an antecedent or consequence of addiction to cigarettes. Such a hypodopaminergic state may play an important role in sustaining nicotine-seeking behavior. Alternatively, an inherited reduction in dopamine receptors in the striatum may be associated with an increased risk of addictive behavior.

Adolescent↗

A statistical method for the analysis of positron emission tomography neuroreceptor ligand data.

A method for voxel by voxel statistical inference of PET radioligand receptor studies is presented. This method is aimed at detecting differences in radioligand binding between baseline and activation scans. It uses nonlinear least squares theory to estimate the ligand-receptor model parameters and utilizes the residuals to calculate their associated variance. The approach both increases the degrees of freedom for statistical testing and produces more accurate estimates of the standard deviation of the parameters. This technique is applicable to any ligand with a validated compartmental model, whether reversibly or irreversibly bound. The method was investigated and compared with a simple voxel-wise t test. Both simulated and real PET data for the dopamine D(1) receptor ligand [(11)C]SCH 23390 were used to assess the method. The assumptions implicit in the residuals methods were validated. The residuals method was found to be more sensitive than a simple t test, while not producing false-positive results. In addition, we showed that this method reliably differentiates changes in radioligand binding from the effects of changes in cerebral blood flow.

Adult↗

Modeling dynamic PET-SPECT studies in the wavelet domain.

This work develops a theoretical framework and corresponding algorithms for the modeling of dynamic PET-SPECT studies both in time and space. The problem of estimating the spatial dimension is solved by applying the wavelet transform to each scan of the dynamic sequence and then performing the kinetic modeling and statistical analysis in the wavelet domain. On reconstruction through the inverse wavelet transform, one obtains parametric images that are consistent estimates of the spatial patterns of the kinetic parameter of interest. The theoretical setup allows the use of linear techniques currently used in PET-SPECT for kinetic analysis. The method is applied to artificial and real data sets. The application to dynamic PET-SPECT studies was performed both for validation purposes, when the spatial patterns are known, and for illustration of the advantages offered by the technique in case of tracers with an unknown pattern of distribution.

Benzamides↗

Statistical modeling of positron emission tomography images in wavelet space.

A new method is introduced for the analysis of multiple studies measured with emission tomography. Traditional models of statistical analysis (ANOVA, ANCOVA and other linear models) are applied not directly on images but on their correspondent wavelet transforms. Maps of model effects estimated from these models are filtered using a thresholding procedure based on a simple Bonferroni correction and then reconstructed. This procedure inherently represents a complete modeling approach and therefore obtains estimates of the effects of interest (condition effect, difference between conditions, covariate of interest, and so on) under the specified statistical risk. By performing the statistical modeling step in wavelet space. the procedure allows the direct estimation of the error for each wavelet coefficient; hence, the local noise characteristics are accounted for in the subsequent filtering. The method was validated by use of a null dataset and then applied to typical examples of neuroimaging studies to highlight conceptual and practical differences from existing statistical parametric mapping approaches.

Artifacts↗