Designing an optimized protocol for detecting transient dopamine release

A single positron emission tomography (PET) scan can be used to detect voxel-level transient dopamine release in response to mid-scan task or stimulus. A standard approach uses F-test (significance) thresholding followed by a cluster size threshold to limit false positives. The F statistic is computed from two neurophysiological kinetic models, one able to handle dopamine-induced perturbations to the time-varying radiotracer concentration, and the other unable to do so. Through simulation we first demonstrate that extensive denoising of the dynamic PET images is required for this method to have high detection sensitivity, though this often leads to a large cluster size threshold—limiting the detection of smaller cluster sizes—and poorer parametric accuracy. Our simulations also show that voxels near the peripheries of clusters are often rejected—becoming false negatives and ultimately distorting the F-distribution of rejected voxels. We then suggest a novel Monte Carlo method that incorporates these two observations into a cost function, allowing erroneously-rejected voxels to be accepted under specified criteria. In simulations, the proposed method boosts sensitivity by up to 77% while preserving cluster size threshold, or up to 180% when optimizing for detection sensitivity. A further parametric-based voxelwise thresholding is then suggested to better estimate the dopamine release dynamics in detected clusters. Finally, we apply the Monte Carlo method coupled with the parametric thresholding approach to a pilot scan from a human gambling study, where additional parametrically-unique clusters are detected as compared to the current best method.Science, Faculty ofPhysics and Astronomy, Department ofGraduat