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Major depressive disorder is associated with lowered mood, anxiety, anhedonia, sleep problems, and cognitive impairments. Many of these functions are regulated by μ-opioid receptor (MOR) system. Preclinical, in vivo, and post-mortem studies have however yielded inconclusive results regarding the role of the MOR in depression and anxiety. Moreover, it is not known whether alterations in MOR are already present in subclinical depression and anxiety. In a large-scale retrospective cross-sectional study we pooled data from 135 (113 males and 22 females) healthy subjects whose brain’s MOR availability was measured with positron emission tomography (PET) using an agonist radioligand [ 11 C]carfentanil that has high affinity for MORs. Depressive and anxious symptomology was addressed with BDI-II and STAI-X questionnaires, respectively. Both anxiety and depression scores in the subclinical range were negatively associated with MOR availability in cortical and subcortical areas, notably in amygdala, hippocampus, ventral striatum, and orbitofrontal and cingulate cortices. We conclude that dysregulated MOR availability is involved in altered mood and pathophysiology of depression and anxiety disorders.

Background Obesity is a pressing public health concern worldwide. Novel pharmacological means are urgently needed to combat the increase of obesity and accompanying type 2 diabetes (T2D). Although fully established obesity is associated with neuromolecular alterations and insulin resistance in the brain, potential obesity-promoting mechanisms in the central nervous system have remained elusive. In this triple-tracer positron emission tomography study, we investigated whether brain insulin signaling, μ-opioid receptors (MORs) and cannabinoid CB1 receptors (CB1Rs) are associated with risk for developing obesity. Methods Subjects were 41 young non-obese males with variable obesity risk profiles. Obesity risk was assessed by subjects’ physical exercise habits, body mass index and familial risk factors, including parental obesity and T2D. Brain glucose uptake was quantified with [18F]FDG during hyperinsulinemic euglycemic clamp, MORs were quantified with [11C]carfentanil and CB1Rs with [18F]FMPEP-d2. Results Subjects with higher obesity risk had globally increased insulin-stimulated brain glucose uptake (19 high-risk subjects versus 19 low-risk subjects), and familial obesity risk factors were associated with increased brain glucose uptake (38 subjects) but decreased availability of MORs (41 subjects) and CB1Rs (36 subjects). Conclusions These results suggest that the hereditary mechanisms promoting obesity may be partly mediated via insulin, opioid and endocannabinoid messaging systems in the brain.

AbstractThe brain’s µ-opioid receptors (MORs) are involved in analgesia, reward and mood regulation. Several neuropsychiatric diseases have been associated with dysfunctional MOR system, and there is also considerable variation in receptor density among healthy individuals. Sex, age, body mass and smoking have been proposed to influence the MOR system, but due to small sample sizes the magnitude of their influence remains inconclusive. Here we quantified in vivo MOR availability in the brains of 204 individuals with no neurologic or psychiatric disorders using positron emission tomography (PET) with tracer [11C]carfentanil. We then used Bayesian hierarchical modeling to estimate the effects of sex, age, body mass index (BMI) and smoking on [11C]carfentanil binding potential. We also examined hemispheric lateralization of MOR availability. Age had regionally specific effects on MOR availability, with age-dependent increase in frontotemporal areas but decrease in amygdala, thalamus, and nucleus accumbens. The age-dependent increase was stronger in males. MOR availability was globally lowered in smokers but independent of BMI. Finally, MOR availability was higher in the right versus the left hemisphere. The presently observed variation in MOR availability may explain why some individuals are prone to develop MOR-linked pathological states, such as chronic pain or psychiatric disorders.

Processing of positron emission tomography (PET) data typically involves manual work, causing inter-operator variance. Here we introduce the Magia toolbox that enables processing of brain PET data with minimal user intervention. We investigated the accuracy of Magia with four tracers: [ 11 C]carfentanil, [ 11 C]raclopride, [ 11 C]MADAM, and [ 11 C]PiB. We used data from 30 control subjects for each tracer. Five operators manually delineated reference regions for each subject. The data were processed using Magia using the manually and automatically generated reference regions. We first assessed inter-operator variance resulting from the manual delineation of reference regions. We then compared the differences between the manually and automatically produced reference regions and the subsequently obtained binding potentials and standardizeduptake-value-ratios. The results show that manually produced reference regions can be remarkably different from each other, leading to substantial differences also in outcome measures. While the Magia-derived reference regions were anatomically different from the manual ones, Magia produced outcome measures highly consistent with the average of the manually obtained estimates. For [ 11 C]carfentanil and [ 11 C]PiB there was no bias, while for [ 11 C]raclopride and [ 11 C]MADAM Magia produced 3-5% higher binding potentials. Based on these results and considering the high inter-operator variance of the manual method, we conclude that Magia can be reliably used to process brain PET data.

Introduction:Modelling of the radioactivity images produced by PET scanners into biologically meaningful quantities, such as binding potential, is a complex multi-stage process involving data retrieval, preprocessing, drawing reference regions, kinetic modelling, and post-processing of parametric images. The process is challenging to automatize mainly because of manual work related to input generation, thus prohibiting large-scale standardized analysis of brain PET data.To resolve this problem, we introduce the Magia pipeline that enables processing of brain PET data with minimal user intervention. We investigated the accuracy of Magia in the automatic brain-PET data processing with four tracers binding to different binding sites: [11C]raclopride,[11C]carfentanil, [11C]MADAM, and [11C]PiB. Materials and methods:For each tracer, we processed 30 historical control subjects' data with manual and automated methods. Five persons manually delineated the reference regions (cerebellar or occipital cortex depending on tracer) for each subject according to written and visual instructions. The automatic reference-region extraction was based on FreeSurfer parcellations. We first assessed inter-operator variance resulting from manual delineation of reference regions. Then we compared the differences between the manually and automatically produced reference regions and the subsequently obtained metrics. Results:The manually delineated reference regions were remarkably different from each other.The differences translated into differences in outcome measures (binding potential or SUV-ratio), and the intra-class correlation coefficients were between 47 % and 96 % for the tracers. While the Magia-derived reference regions were topographically very different from the manually defined reference regions, Magia produced outcome measures highly consistent with average of the manually obtained estimates. For [11C]carfentanil and [11C]PiB there was no bias, while for [11C]raclopride and [11C]MADAM Magia produced 3-5 % higher binding potentials as a result of slightly lower time-integrals of reference region time-activity curves. Conclusion: Even if Magia produces reference regions that are anatomically different from manually drawn reference regions, the resulting outcome measures are highly similar. Based on these results and considering the high inter-operator variance of the manual method, the high level of standardization and strong scalability of Magia, we conclude that Magia can be reliably used to process brain PET data. Receptor Binding in PET Using a Simplified Reference Region Model. NeuroImage 1997; 6(4): 279-287. 17. Lundberg J, Odano I, Olsson H, Halldin C, Farde L. Quantification of C-11-MADAM binding to the serotonin transporter in the human brain. J Nucl Med 2005; 46(9): 1505-1515. 18. Lopresti BJ, Klunk WE, Mathis CA, Hoge JA, Ziolko SK, Lu XL et al. Simplified quantification of Pittsburgh compound B amyloid imaging PET studies: A comparative analysis. J Nucl Med 2005; 46(12): 1959-1972. 19. Endres CJ, Bencherif B, Hilto...

Eating behavior varies greatly between individuals, but the neurobiological basis of these trait-like differences in feeding remains poorly understood. Central μ-opioid receptors (MOR) and cannabinoid CB1 receptors (CB1R) regulate energy balance via multiple neural pathways, promoting food intake and reward. Because obesity and eating disorders have been associated with alterations in the brain’s opioid and endocannabinoid signaling, the variation in MOR and CB1R system function could potentially underlie distinct eating behavior phenotypes. In this retrospective positron emission tomography (PET) study, we analyzed [11C]carfentanil PET scans of MORs from 92 healthy subjects (70 males and 22 females), and [18F]FMPEP-d2 scans of CB1Rs from 35 subjects (all males, all also included in the [11C]carfentanil sample). Eating styles were measured with the Dutch Eating Behavior Questionnaire (DEBQ). We found that lower cerebral MOR availability was associated with increased external eating—individuals with low MORs reported being more likely to eat in response to environment’s palatable food cues. CB1R availability was associated with multiple eating behavior traits. We conclude that although MORs and CB1Rs overlap anatomically in brain regions regulating food reward, they have distinct roles in mediating individual feeding patterns. Central MOR system might provide a pharmacological target for reducing individual’s excessive cue-reactive eating behavior.

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