Elevated brain cannabinoid CB1 receptor availability in post-traumatic stress disorder: a positron emission tomography study.

Endocannabinoids and their attending cannabinoid type 1 (CB1) receptor have been implicated in animal models of post-traumatic stress disorder (PTSD). However, their specific role has not been studied in people with PTSD. Herein, we present an in vivo imaging study using positron emission tomography (PET) and the CB1-selective radioligand [(11)C]OMAR in individuals with PTSD, and healthy controls with lifetime histories of trauma (trauma-exposed controls (TC)) and those without such histories (healthy controls (HC)). Untreated individuals with PTSD (N=25) with non-combat trauma histories, and TC (N=12) and HC (N=23) participated in a magnetic resonance imaging scan and a resting PET scan with the CB1 receptor antagonist radiotracer [(11)C]OMAR, which measures the volume of distribution (VT) linearly related to CB1 receptor availability. Peripheral levels of anandamide, 2-arachidonoylglycerol, oleoylethanolamide, palmitoylethanolamide and cortisol were also assessed. In the PTSD group, relative to the HC and TC groups, we found elevated brain-wide [(11)C]OMAR VT values (F(2,53)=7.96, P=0.001; 19.5% and 14.5% higher, respectively), which were most pronounced in women (F(1,53)=5.52, P=0.023). Anandamide concentrations were reduced in the PTSD relative to the TC (53.1% lower) and HC (58.2% lower) groups. Cortisol levels were lower in the PTSD and TC groups relative to the HC group. Three biomarkers examined collectively--OMAR VT, anandamide and cortisol--correctly classified nearly 85% of PTSD cases. These results suggest that abnormal CB1 receptor-mediated anandamide signaling is implicated in the etiology of PTSD, and provide a promising neurobiological model to develop novel, evidence-based pharmacotherapies for this disorder.

Noninvasive visualization of human dopamine dynamics from PET images.

We recently introduced strategies for extracting temporal patterns of brain dopamine fluctuations from dynamic positron emission tomography (PET) data using the tracer [11C]-raclopride. Each of our methods yields a collection of time-concentration curves for endogenous dopamine. Given a spatially dense collection of curves (i.e., one at every voxel in a region of interest), we produce image volumes of dopamine (DA) concentration, DA(X, t), at multiple voxel locations and each time-frame. The volume over time-frames constitutes a 4D dataset that can be thought of as a DA "movie". There are a number of ways to visualize such data. Viewing cine loops of a slice through the DA volume is one way. Creating images of dopamine peak-time, Tpeak(X), derived from a movie, is another. Each visualization may reveal spatio-temporal patterns of neurotransmitter activity heretofore unobservable. We conducted an initial validation experiment in which identical DA responses were induced by an identical task, initiated at different times by the same subject, in two separate PET scans. A comparison of the resulting Tpeak(X) images revealed a large contiguous cluster of striatal voxels, on each side, whose DA timing was consistent with the relative timing of the tasks. Hence, the DA movies and their respective peak-time images were shown to be new types of functional images that contain bonafide timing information about a neurotransmitter's response to a stimulus.

Estimation from PET data of transient changes in dopamine concentration induced by alcohol: support for a non-parametric signal estimation method.

We previously developed a model-independent technique (non-parametric ntPET) for extracting the transient changes in neurotransmitter concentration from paired (rest & activation) PET studies with a receptor ligand. To provide support for our method, we introduced three hypotheses of validation based on work by Endres and Carson (1998 J. Cereb. Blood Flow Metab. 18 1196-210) and Yoder et al (2004 J. Nucl. Med. 45 903-11), and tested them on experimental data. All three hypotheses describe relationships between the estimated free (synaptic) dopamine curves (FDA(t)) and the change in binding potential (DeltaBP). The veracity of the FDA(t) curves recovered by nonparametric ntPET is supported when the data adhere to the following hypothesized behaviors: (1) DeltaBP should decline with increasing DA peak time, (2) DeltaBP should increase as the strength of the temporal correlation between FDA(t) and the free raclopride (FRAC(t)) curve increases, (3) DeltaBP should decline linearly with the effective weighted availability of the receptor sites. We analyzed regional brain data from 8 healthy subjects who received two [11C]raclopride scans: one at rest, and one during which unanticipated IV alcohol was administered to stimulate dopamine release. For several striatal regions, nonparametric ntPET was applied to recover FDA(t), and binding potential values were determined. Kendall rank-correlation analysis confirmed that the FDA(t) data followed the expected trends for all three validation hypotheses. Our findings lend credence to our model-independent estimates of FDA(t). Application of nonparametric ntPET may yield important insights into how alterations in timing of dopaminergic neurotransmission are involved in the pathologies of addiction and other psychiatric disorders.

A Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic positron emission tomography.

PURPOSE: Estimation of parametric maps is challenging for kinetic models in dynamic positron emission tomography. Since voxel kinetics tend to be spatially contiguous, the authors consider groups of homogeneous voxels together. The authors propose a novel algorithm to identify the groups and estimate kinetic parameters simultaneously. Uncertainty estimates for kinetic parameters are also obtained. METHODS: Mixture models were used to fit the time activity curves. In order to borrow information from spatially nearby voxels, the Potts model was adopted. A spatial temporal model was built incorporating both spatial and temporal information in the data. Markov chain Monte Carlo was used to carry out parameter estimation. Evaluation and comparisons with existing methods were carried out on cardiac studies using both simulated data sets and a pig study data. One-compartment kinetic modeling was used, in which K1 is the parameter of interest, providing a measure of local perfusion. RESULTS: Based on simulation experiments, the median standard deviation across all image voxels, of K1 estimates were 0, 0.13, and 0.16 for the proposed spatial mixture models (SMMs), standard curve fitting, and spatial K-means methods, respectively. The corresponding median mean squared biases for K1 were 0.04, 0.06, and 0.06 for abnormal region of interest (ROI); 0.03, 0.03, and 0.04 for normal ROI; and 0.007, 0.02, and 0.05 for the noise region. CONCLUSIONS: SMM is a fully Bayesian algorithm which determines the optimal number of homogeneous voxel groups, voxel group membership, parameter estimation, and parameter uncertainty estimation simultaneously. The voxel membership can also be used for classification purposes. By borrowing information from spatially nearby voxels, SMM substantially reduces the variability of parameter estimates. In some ROIs, SMM also reduces mean squared bias.

Selection of weighting factors for quantification of PET radioligand binding using simplified reference tissue models with noisy input functions.

Input function noise contributes to model-predicted values and should be accounted for during parameter estimation. This problem has been examined in the context of PET data analysis using a noisy image-derived arterial input function. Huesman and Mazoyer (1987 Phys. Med. Biol 32 1569-79) incorporated the effect of error in the measured input function into the objective function and observed a subsequent improvement in the accuracy of parameters estimated from a kinetic model of cardiac blood flow. Such a treatment has not been applied to the reference region models commonly used to analyze dynamic positron emission tomography data with receptor-ligand tracers. Here, we propose a strategy for selection of weighting factors that accounts for noise in the reference region input function and test the method on two common formulations of the simplified reference tissue model (SRTM). We present a simulation study which demonstrates that the proposed weighting approach improves the accuracy of estimated binding potential at high noise levels and when the reference tissue and target regions of interest are of comparable size. In the second simulation experiment, we show that using a small, homogeneous reference tissue with our weighting technique may have advantages over input functions derived from a larger (and thus less noisy), heterogeneous region with conventional weighting. A comparative analysis of clinical [(11)C]flumazenil data found a small but significant increase in estimated binding potential when using the proposed weighting method, consistent with the finding of reduced negative bias in our simulation study. The weighting strategy described here accounts for noise in the reference region input function and may improve the performance of the SRTM in applications where data are noisy and the reference region is relatively small. This technique may offer similar benefits to other models using reference region inputs, particularly those derived from the SRTM.