2026 ECNP Congress

PS03-3164

Comparison of sensitivity for detecting transient striatal dopamine release for different radioligands

Benjamin Classen — G. Gründer, C. Schmitz · Central Institute for Mental Health, Molecular Neuroimaging, Mannheim, Germany

2026 ECNP Congress

Introduction

The capacity of positron emission tomography (PET) to detect and quantify transient striatal dopamine (DA) release is shaped by radioligand kinetics, signal-to-noise properties, and the protocol of radioligand delivery. While specifically optimized delivery protocols exist for certain radioligands [1, 2], the choice of radioligand itself as possible leverage for improving detection sensitivity has only been partially investigated [3]. A principled, model-based framework for radioligand comparison is therefore needed to establish whether meaningful differences in the intrinsic capacity to detect transient striatal DA release exist across radioligands.

Methods

Detectability of transient striatal DA release was assessed by two complementary methods resting on distinct sets of assumptions: the detection accuracy of lp-ntPET [4], the current gold standard detection method based on an extension to the simplified tissue reference model, and the Bayes error rate (BER) for the binary task of distinguishing DA release from baseline. The BER was derived from the approximate posterior likelihood of DA release being present or absent in a given time-activity curve (TAC), estimated via Monte Carlo simulation with uniform priors over kinetic rates and a frame-wise Gaussian noise model. Both methods were applied to TACs generated by a validated generative modeling framework for four radioligands: [¹¹C]raclopride, [¹⁸F]desmethoxyfallypride, [¹⁸F]fallypride, and [¹¹C]-(+)-PHNO. The priors over kinetic rates resembled the striatal kinetics typically observed for the respective radioligands. The TAC set spanned a wide range of noise levels and inter-individual kinetic variation, and explicitly represented conditions with and without transient DA release. All analyses were repeated across varying onset times of DA release relative to scan start and with varying total injected dose.

Results

Both methods revealed consistent differences in detection performance across radioligands, with DMFP performing best and PHNO worst throughout. lp-ntPET achieved peak accuracies of 0.88 (DMFP), 0.82 (FP), 0.79 (RAC), and 0.74 (PHNO) for a noise magnitude typically seen on the voxel-level, with accuracy strongly depending on the timing of DA release. The BER achieved minimum values of 0.18 (DMFP), 0.16 (FP), 0.30 (RAC), and 0.31 (PHNO), with a moderate dependence on the timing of DA release. These differences were not attributable to scan duration, but instead reflected interactions between binding kinetics and noise structure. Reducing the noise increased the accuracy non-differentially across radioligands. The impact of radioligand choice on detection accuracy was modest relative to the effects of noise reduction and increase of total injected radioligand dose.

Conclusions

This work presents a theoretically grounded framework for comparing radioligands on the basis of their intrinsic capacity to detect transient DA release. Convergent evidence from two methodologically independent analyses strengthens the conclusion that meaningful differences exist between radioligands in this regard. These findings argue for deliberate radioligand selection in studies targeting transient striatal DA release. At the same time, adherence to optimal radioligand-specific delivery protocols appears to matter more than the choice of radioligand itself.

References

  1. Wang, S., Kim, S., Cosgrove, K. P., & Morris, E. D. (2017). A framework for designing dynamic lp-ntPET studies to maximize the sensitivity to transient neurotransmitter responses to drugs: Application to dopamine and smoking. NeuroImage, 146, 701–714.
  2. Ceccarini, J., Vrieze, E., Koole, M., Muylle, T., Bormans, G., Claes, S., & Van Laere, K. (2012). Optimized in vivo detection of dopamine release using 18F-fallypride PET. Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 53(10), 1565–1572.
  3. Morris, E. D., & Yoder, K. K. (2007). Positron emission tomography displacement sensitivity: predicting binding potential change for positron emission tomography tracers based on their kinetic characteristics. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 27(3), 606–617.
  4. Normandin, M. D., Schiffer, W. K., & Morris, E. D. (2012). A linear model for estimation of neurotransmitter response profiles from dynamic PET data. NeuroImage, 59(3), 2689–2699.