Dopamine transporters are markedly reduced in Lesch-Nyhan disease in vivo.

Dopamine (DA) deficiency has been implicated in Lesch-Nyhan disease (LND), a genetic disorder that is characterized by hyperuricemia, choreoathetosis, dystonia, and compulsive self-injury. To establish that DA deficiency is present in LND, the ligand WIN-35,428, which binds to DA transporters, was used to estimate the density of DA-containing neurons in the caudate and putamen of six patients with classic LND. Comparisons were made with 10 control subjects and 3 patients with Rett syndrome. Three methods were used to quantify the binding of the DA transporter so that its density could be estimated by a single dynamic positron emission tomography study. These approaches included the caudate- or putamen-to-cerebellum ratio of ligand at 80-90 min postinjection, kinetic analysis of the binding potential [Bmax/(Kd x Vd)] using the assumption of equal partition coefficients in the striatum and the cerebellum, and graphical analysis of the binding potential. Depending on the method of analysis, a 50-63% reduction of the binding to DA transporters in the caudate, and a 64-75% reduction in the putamen of the LND patients was observed compared to the normal control group. When LND patients were compared to Rett syndrome patients, similar reductions were found in the caudate (53-61%) and putamen (67-72%) in LND patients. Transporter binding in Rett syndrome patients was not significantly different from the normal controls. Finally, volumetric magnetic resonance imaging studies detected a 30% reduction in the caudate volume of LND patients. To ensure that a reduction in the caudate volume would not confound the results, a rigorous partial volume correction of the caudate time activity curve was performed. This correction resulted in an even greater decrease in the caudate-cerebellar ratio in LND patients when contrasted to controls. To our knowledge, these findings provide the first in vivo documentation of a dopaminergic reduction in LND and illustrate the role of positron emission tomography imaging in investigating neurodevelopmental disorders.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.