A Monte Carlo approach for improving transient dopamine release detection sensitivity

Published on 2020-02-14T13:18:34Z (GMT) by
<div><p>Current methods using a single PET scan to detect voxel-level transient dopamine release—using F-test (significance) and cluster size thresholding—have limited detection sensitivity for clusters of release small in size and/or having low release levels. Specifically, simulations show that voxels with release near the peripheries of such clusters are often rejected—becoming false negatives and ultimately distorting the F-distribution of rejected voxels. We suggest a 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 improves detection sensitivity by up to 50% while preserving the cluster size threshold, or up to 180% when optimizing for sensitivity. A further parametric-based voxelwise thresholding is then suggested to better estimate the release dynamics in detected clusters. We apply the Monte Carlo method to a pilot scan from a human gambling study, where additional parametrically unique clusters are detected as compared to the current best methods—results consistent with our simulations.</p></div>

Cite this collection

Bevington, Connor WJ; Cheng, Ju-Chieh (Kevin); Klyuzhin, Ivan S; Cherkasova, Mariya V; Winstanley, Catharine A; Sossi, Vesna (2020): A Monte Carlo approach for improving transient dopamine release detection sensitivity. SAGE Journals. Collection. https://doi.org/10.25384/SAGE.c.4857072.v1