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Hietala, J.

Publications and source records attributed to Hietala, J..

2 recordsLinked to original sources
bioRxiv2019

Links between central CB1-receptor availability and peripheral endocannabinoids in patients with first episode psychosis

There is an established, albeit poorly-understood link between psychosis and metabolic abnormalities such as altered glucose metabolism and dyslipidemia, which often precede the initiation of antipsychotic treatment. It is known that obesity-associated metabolic disorders are promoted by peripheral activation of the endocannabinoid system (ECS). Our recent data suggest that ECS dysregulation may also play a role in psychosis. With the aim of characterizing the involvement of the central and peripheral ECSs and their mutual associations, here we performed a combined neuroimaging and metabolomic study in patients with first-episode psychosis (FEP) and healthy controls (HC). Regional brain cannabinoid receptor type 1 (CB1R) availability was quantified in two, independent samples of patients with FEP (n=20 and n=8) and HC (n=20 and n=10), by applying 3D positron emission tomography (PET), using two radiotracers, [11C]MePPEP and [18F]FMPEP-d2. Ten endogenous endocannabinoids or related metabolites were quantified in serum, drawn from these individuals during the same imaging session. Circulating levels of arachidonic acid and oleyl ethanolamide were reduced in FEP individuals, but not in those who were predominantly medication-free. In HC, there was an inverse association between levels of circulating arachidonoyl glycerol, anandamide, oleyl ethanolamide and palmitoyl ethanolamide, and CB1R availability in the posterior cingulate cortex. This phenomenon was, however, not observed in FEP patients. Our data thus provide evidence of cross-talk and dysregulation between peripheral endocannabinoids and central CB1R availability in FEP.

neuroscience↗
bioRxiv2019

Magia: Robust automated modeling and image processing toolbox for PET neuroinformatics

IntroductionModelling 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.\n\nMaterials and methodsFor 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.\n\nResultsThe 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.\n\nConclusionEven 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.

neuroscience↗