A Reference Tissue Implementation of Simultaneous Multifactor Bayesian Analysis (SiMBA) of PET Time Activity Curve Data.

PET analysis is conventionally performed as a two-stage process of quantification followed by analysis. We recently introduced SiMBA (Simultaneous Multifactor Bayesian Analysis), a hierarchical model that performs quantification and analysis for all brain regions of all individuals at once, and in so doing improves both the accuracy of parameter estimation as well as inferential efficiency. However until now, SiMBA has only been implemented for the two-tissue compartment model.

Altered Serotonin 1B Receptor Binding After Escitalopram for Depression Is Correlated With Treatment Effect.

Background: Major depressive disorder (MDD) is commonly treated with selective serotonin reuptake inhibitors (SSRIs). SSRIs inhibit the serotonin transporter (5-HTT), but the downstream antidepressant mechanism of action of these drugs is poorly understood. The serotonin 1B (5-HT1B) receptor is functionally linked to 5-HTT and 5-HT1B receptor binding and 5-HT1B receptor mRNA is reduced in the raphe nuclei after SSRI administration in primates and rodents, respectively.

PET Imaging of the Serotonin 1A Receptor in Major Depressive Disorder: Hierarchical Multivariate Analysis of [C]WAY100635 Overcomes Outcome Measure Discrepancies.

The serotonin 1A receptor has been linked to both the pathophysiology of major depressive disorder (MDD) and the antidepressant action of serotonin reuptake inhibitors. Most PET studies of the serotonin 1A receptor in MDD used the receptor antagonist radioligand, [carbonyl-C]WAY100635; however the interpretation of the combined results has been contentious owing to reports of higher or lower binding in MDD with different outcome measures. The reasons for these divergent results originate from several sources, including properties of the radiotracer itself, which complicate its quantification and interpretation; as well as from previously reported differences between MDD and healthy volunteers in both reference tissue binding and plasma free fraction, which are typically assumed not to differ.

Parametric and non-parametric Poisson regression for modelling of the arterial input function in positron emission tomography.

Full quantification of Positron Emission Tomography (PET) requires an arterial input function (AIF) for measurement of certain targets, or using particular radiotracers, or for the quantification of specific outcome measures. The AIF represents the measurement of radiotracer concentrations in the arterial blood plasma over the course of the PET examination. Measurement of the AIF is prone to error as it is a composite measure created from the combination of multiple measurements of different samples with different equipment, each of which can be sources of measurement error.