Gender dependent rate of metabolism of the opioid receptor-PET ligand [18F]fluoroethyldiprenorphine.

AIM: The morphinane-derivate 6-O-(2-[(18)F]fluoroethyl)-6-O-desmethyldiprenorphine ([(18)F]FDPN) is a nonselective opioid receptor ligand currently used in positron emission tomography (PET). Correction for plasma metabolites of the arterial input function is necessary for quantitative measurements of [(18)F]FDPN binding. A study was undertaken to investigate if there are gender dependent differences in the rate of metabolism of [(18)F]FDPN. METHODS: The rate of metabolism of [(18)F]FDPN was mathematically quantified by fitting a bi-exponential function to each individual's dynamic metabolite data. RESULTS: No statistically significant gender differences were found for age, weight, body mass index or dose. However, significant differences (p < 0.01) in two of the four kinetic parameters describing the rate of metabolism were found between the two groups, with women metabolizing [(18)F]FDPN faster than men. These differences were found in the contribution of the fast and slow kinetic components of the model describing the distribution of radioactive species in plasma, indicating a higher rate of enzyme-dependent degradation of [(18)F]FDPN in women than in men. CONCLUSION: The findings reinforce the need for individualized metabolite correction during [(18)F]FDPN-PET scans and also indicate that in certain cases, grouping according to gender could be performed in order to minimize methodological errors of the input function prior to kinetic analyses.

Opioidergic changes in the pineal gland and hypothalamus in cluster headache: a ligand PET study.

Using PET with the opioidergic ligand [11C]diprenorphine, the authors demonstrate decreased tracer binding in the pineal gland of cluster headache patients vs healthy volunteers. Opioid receptor availability in the hypothalamus and cingulate cortex depended on the duration of the headache disorder. Therefore, the pathophysiology of cluster headache may relate to opioidergic dysfunction in circuitries generating the biologic clock.

Force level independent representations of predictive grip force-load force coupling: a PET activation study.

The existence of forward internal models is a fundamental principle in theories of predictive motor control. There are indications that internal models are represented in the cerebellum. So far, no conclusive data exist on automated procedures involving predictive motor behavior. In particular, it is unknown whether single or multiple task-specific internal models handle the broad range of behavioral situations in which they occur. Using H2(15)O PET in eight subjects, we examined predictive motor control in an automated grip force-load force coupling task at three differing load force levels. In the experimental condition, subjects pulled a grasped object against an isometric resistance while simultaneously producing anticipatory grip forces. There were three control conditions (pull force isolated; grip force isolated; motor rest). A 2 x 2 factorial design was chosen to reveal the interaction effect of grip force-pull force coupling. The factors were pull force (with/without) and grip force (with/without). Grip and load forces were well matched between experimental and control conditions. Conjunction inference and interaction analyses identified force coupling related activity in the ipsilateral posterior cerebellum that was independent of force levels. Interaction effects were also identified in the anterior cingulate and frontal association regions, the right caudate nucleus, and the left lingual gyrus. These data demonstrate the existence of modular representations for predictive force coupling, with the ipsilateral cerebellum playing a major role. Moreover, the data implicate that the representations for predictive force control are applicable to a range of different environmental affordances.

An evaluation of extended vs weighted least squares for parameter estimation in physiological modeling.

Weighted least squares (WLS) is the technique of choice for parameter estimation from noisy data in physiological modeling. WLS can be derived from maximum likelihood theory, provided that the measurement error variance is known and independent of the model parameters and the weights are calculated as the inverse of the measurement error variance. However, using measured values in lieu of predicted values to quantify the measurement error variance is approximately valid only when the noise in the data is relatively low. This practice may thus introduce sampling variation in the resulting estimates, as weights can be seriously mis-specified. To avoid this, extended least squares (ELS) has been used, especially in pharmacokinetics. ELS uses an augmented objective function where the measurement error variance depends explicitly on the model parameters. Although it is more complex, ELS accounts for the Gaussian maximum likelihood statistical model of the data better than WLS, yet its usage is not as widespread. The use of ELS in high data noise situations will result in more accurate parameter estimates than WLS (when the underlying model is correct). To support this claim, we have undertaken a simulation study using four different models with varying amounts of noise in the data and further assuming that the measurement error standard deviation is proportional to the model prediction. We also motivate this in terms of maximum likelihood and comment on the practical consequences of using WLS and ELS as well as give practical guidelines for choosing one method over the other.