Serotonin 1B receptor density mapping of the human brainstem using positron emission tomography and autoradiography.

Emma R Veldman, Andrea Varrone, Katarina Varnäs, Marie M Svedberg, Zsolt Cselényi, Mikael Tiger, Balázs Gulyás, Christer Halldin, Johan Lundberg
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Abstract

The serotonin 1B (5-HT1B) receptor has lately received considerable interest in relation to psychiatric and neurological diseases, partly due to findings based on quantification using Positron Emission Tomography (PET). Although the brainstem is an important structure in this regard, PET radioligand binding quantification in brainstem areas often shows poor reliability. This study aims to improve PET quantification of 5-HT1B receptor binding in the brainstem.Volumes of interest (VOIs) were selected based on a 3D [3H]AZ10419369 Autoradiography brainstem model, which visualized 5-HT1B receptor distribution in high resolution. Two previously developed VOI delineation methods were tested and compared to a conventional manual method. For a method based on template data, a [11C]AZ10419369 PET template was created by averaging parametric binding potential (BPND) images of 52 healthy subjects. VOIs were generated based on a predefined volume and BPND thresholding and subsequently applied to test-retest [11C]AZ10419369 parametric BPND images of 8 healthy subjects. For a method based on individual subject data, VOIs were generated directly on each individual parametric image.Both methods showed improved reliability compared to a conventional manual VOI. The VOIs created with [11C]AZ10419369 template data can be automatically applied to future PET studies measuring 5-HT1B receptor binding in the brainstem.

Abstract Image

Abstract Image

Abstract Image

利用正电子发射断层扫描和放射自显像技术绘制人脑干血清素1B受体密度图。
5-羟色胺1B (5-HT1B)受体最近在精神和神经系统疾病中引起了相当大的兴趣,部分原因是基于正电子发射断层扫描(PET)定量分析的发现。虽然脑干是这方面的重要结构,但PET放射配体结合定量在脑干区域往往表现出较差的可靠性。本研究旨在提高脑干5-HT1B受体结合的PET定量。基于3D [3H]AZ10419369自放射成像脑干模型选择感兴趣体积(VOIs),该模型以高分辨率可视化5-HT1B受体分布。测试了两种先前开发的VOI圈定方法,并与传统的手动方法进行了比较。基于模板数据的方法,对52名健康受试者的参数结合电位(BPND)图像进行平均,生成[11C]AZ10419369 PET模板。基于预定义的体积和BPND阈值生成voi,随后应用于8名健康受试者的参数BPND图像的test-retest [11C]AZ10419369。对于基于个体主体数据的方法,在每个单独的参数图像上直接生成voi。与传统的手动VOI相比,这两种方法都显示出更高的可靠性。用[11C]AZ10419369模板数据创建的voi可以自动应用于未来的PET研究,测量脑干中5-HT1B受体的结合。
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