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Schizophrenia is a devastating psychiatric illness. Its pathophysiology is not fully clarified. Animal data, in vitro and indirect in vivo imaging support glutamatergic N-methyl-D-aspartate (NMDA) receptor hypofunction in the disorder. A lack of suitable ligands has obstructed direct evaluation of the NMDA receptor hypofunction hypothesis of schizophrenia. Many research groups are working towards developing appropriate single-photon emission tomography and positron emission tomography ligands for the NMDA receptor. This paper briefly presents evidence for links between glutamatergic system dysfunction and schizophrenia. It reviews the radioligands to evaluate glutamatergic receptors in vivo and discusses issues in developing novel ligands for the glutamatergic system.

The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D2/D3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [123I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r2>0.99, P<0.0001). Similarly, FCM gave segmentation of the [123I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r2=0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner.

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D2 receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P<0.001) improvements in image registration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other methods failed completely to register, particularly 123I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the chi2 of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences.

This paper describes the application of a multivariate statistical technique to investigate striatal dopamine D2 receptor concentrations measured by iodine-123 iodobenzamide (123I-IBZM) single-photon emission tomography (SPET). This technique enables the automatic segmentation of dynamic nuclear medicine images based on the underlying time-activity curves present in the data. Once the time-activity curves have been extracted, each pixel can be mapped back on to the underlying distribution, considerably reducing image noise. Cluster analysis has been verified using computer simulations and phantom studies. The technique has been applied to SPET images of dopamine D2 receptors in a total of 20 healthy and 20 schizophrenic volunteers (22 male, 18 female), using the ligand 123I-IBZM. Following automatic image segmentation, the concentration of striatal dopamine D2 receptors shows a significant left-sided asymmetry in male schizophrenics compared with male controls. The mean left-minus-right laterality index for controls is -1.52 (95% CI -3.72-0.66) and for patients 4.04 (95% CI 1.07-7.01). Analysis of variance shows a case-by-sex-by-side interaction, with F=10.01, P=0. 005. We can now demonstrate that the previously observed male sex-specific D2 receptor asymmetry in schizophrenia, which had failed to attain statistical significance, is valid. Cluster analysis of dynamic nuclear medicine studies provides a powerful tool for automatic segmentation and noise reduction of the images, removing much of the subjectivity inherent in region-of-interest analysis. The observed striatal D2 asymmetry could reflect long hypothesized disruptions in dopamine-rich cortico-striatal-limbic circuits in schizophrenic males.

The mapping of 5-HT2 receptors in the brain using functional imaging techniques has been limited by a relative lack of selective radioligands. Iodine-123 labelled 4-amino-N-[1-[3-(4-fluorophenoxy)propyl]-4-methyl-4-piperidinyl]-5-io do-2-methoxybenzamide (123I-5-I-R91150 or 123I-R93274) is a new ligand for single-photon emission tomography (SPET), with high affinity and selectivity for 5-HT2A receptors. This study reports on preliminary 123I-5-I-R91150 SPET, whole-body and blood distribution findings in five healthy human volunteers. Maximal brain uptake was approximately 2% of total body counts at 180 min post injection (p.i. ). Dynamic SPET sequences were acquired with the brain-dedicated, single-slice multi-detector system SME-810 over 200 min p.i. Early peak uptake (at 5 min p.i.) was seen in the cerebellum, a region free from 5HT2A receptors. In contrast, radioligand binding in the frontal cortex increased steadily over time, up to a peak at approximately 100-120 min p.i. Frontal cortex-cerebellum activity ratios reached values of 1.4, and remained stable from approximately 100 min p.i. onwards. Multi-slice SPET sequences showed a pattern of regional variation of binding compatible with the autoradiographic data on the distribution of 5-HT2A receptors in humans (cerebral cortex>striatum>cerebellum). These findings suggest that 123I-5-I-R91150 may be used for the imaging of 5-HT2A receptors in the living human brain with SPET.

The mathematical, biological discipline of receptor pharmacology and the graphical methods of describing receptor behaviour evolved over a century of painstaking observation and model testing. Laws regarding in vitro theories are classically considered inoperative in vivo. Nevertheless, functional imaging techniques have rapidly evolved to allow receptor measurement and rules of thumb have been developed which clearly prove valid receptor parameters can be derived from functional imaging studies. The field is evolving so rapidly now that nuclear medicine researchers are in danger in applying these techniques without recourse to an understanding of the orthodox discipline of receptor pharmacology. This review attempts to document the basis of receptor pharmacology and to give an account of the theoretical and practical basis on which this can be applied in vivo. The review is targeted towards single-photon emission tomography because of the rapid growth in the area, but many parts draw on the literature relating to positron emission tompgraphy since the first translation of in vitro to in vivo measurement was performed with this technique.

Using a brain-dedicated triple-headed single-photon emission tomography (SPET) system, a sequential whole-volume imaging protocol has been devised to evaluate the regional distribution of iodine-123 iomazenil binding to GABAA receptors in the entire brain. The protocol was piloted in eight normal volunteers (seven males and one female; mean age, 24.8 +/- 3.9 years). The patterns obtained were largely compatible with the known distribution of GABAA receptors in the brain as reported in autoradiographic studies, with cerebral cortical regions, particularly the occipital and frontal cortices, displaying the highest 123I-iomazenil uptake. Measures of time to peak uptake and tracer washout rates presented with the same pattern of regional variation, with later times to peak and slower washout rates in cortical regions compared to other brain areas. Semiquantitative analysis of the data using white matter/ventricle regions as reference demonstrated a plateau of specific 123I-iomazenil binding in neocortical and cerebellar regions from 60-75 min onwards. These data demonstrate the feasibility of sequential, dynamic whole-volume 123I-iomazenil SPET imaging. The protocol may be particularly useful in the investigation of neuropsychiatric conditions which are likely to involve more than one focus of GABA abnormalities, such as anxiety disorders and schizophrenia.