A framework for designing dynamic lp-ntPET studies to maximize the sensitivity to transient neurotransmitter responses to drugs: Application to dopamine and smoking

- A framework for designing dynamic PET studies to probe neurotransmitter responses.
- Design parameters were tuned to maximize sensitivity of the novel lp-ntPET model to responses.
- Bolus paradigm could replace B/I paradigm without sacrificing detection sensitivity.
- Pre-processing PET data with de-noising algorithm HYPR could improve sensitivity.
- Optimal timing of the drug challenge, scan duration and dose limit were reported.

The “linear parametric neurotransmitter PET” (lp-ntPET) model was introduced to capture the time course of transient endogenous neurotransmitter response to drug stimulus from dynamic PET data. We previously used this novel analysis tool to probe the short-lived dopamine (DA) response induced by cigarette smoking in the PET scanner. It allowed us to find a sex difference in the DA signature of cigarette smoking. To make best use of this tool to characterize neurotransmitter response to drug stimulus, the sensitivity of lp-ntPET to detect such responses must be maximized. We designed a series of simulation studies to examine the impact of the following factors on the sensitivity of lp-ntPET using smoking-induced DA release as an example application: tracer delivery protocol, pre-processing for image denoising, timing of the smoking task, duration of the PET scan, and dose of the radiotracer. Our results suggest that a Bolus paradigm could replace a more difficult B/I paradigm without sacrificing the sensitivity of the method. Pre-processing the PET data with the de-noising algorithm HYPR could improve the sensitivity. The optimal timing to start the smoking task is 45min in a 90min scan and 35min in a 75min scan. A mild shortening of the scan time from 90mCi to 75min should be acceptable without loss of sensitivity. We suggest a lower dose limit of a bolus injection at 16mCi to limit underestimation of DA activation. This study established the framework to optimize the experimental design for reaching the full potential of lp-ntPET to detect neurotransmitter responses to drugs or even behavioral tasks.

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