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Free keywords:
Serotonin; 5-HT1B; Positron emission tomography; Graphical analysis
Abstract:
[11C]P943 is a novel, highly selective 5-HT1B PET radioligand. The aim of this study was to determine the test–retest reliability of [11C]P943 using two different modeling methods and to perform a power analysis with each quantification technique.
Methods
Seven healthy volunteers underwent two PET scans on the same day. Regions of interest (ROIs) were the amygdala, hippocampus, pallidum, putamen, insula, frontal, anterior cingulate, parietal, temporal and occipital cortices, and cerebellum. Two multilinear radioligand quantification techniques were used to estimate binding potential: MA1, using arterial input function data, and the second version of the multilinear reference tissue model analysis (MRTM2), using the cerebellum as the reference region. Between-scan percent variability and intraclass correlation coefficients (ICC) were used to assess test–retest reliability. We also performed power analyses to determine the method that would allow the least number of subjects using within-subject or between-subject study designs. A voxel-wise ICC analysis for MRTM2 BPND was performed for the whole brain and all the ROIs studied.
Results
Mean percent variability between two scans across regions ranged between 0.4 % and 12.4 % for MA1 BPND, 0.5 % and 11.5 % for MA1 BPP, 16.7 % and 28.3 % for MA1 BPF, and between 0.2 % and 5.4 % for MRTM2 BPND. The power analyses showed a greater number of subjects were required using MA1 BPF compared with other outcome measures for both within-subject and between-subject study designs. ICC values were the highest using MRTM2 BPND and the lowest with MA1 BPF in ten ROIs. Small regions and regions with low binding had lower ICC values than large regions and regions with high binding.
Conclusion
Reliable measures of 5-HT1B receptor binding can be obtained using the novel PET radioligand [11C]P943. Quantification of 5-HT1B receptor binding with MRTM2 BPND and with MA1 BPP provided the least variability and optimal power for within-subject and between-subject designs.
