A Monte Carlo approach for improving transient dopamine release detection sensitivity
| Autor: | Connor W. J. Bevington, Ju-Chieh Kevin Cheng, Mariya V. Cherkasova, Catharine A. Winstanley, Vesna Sossi, Ivan S. Klyuzhin |
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| Rok vydání: | 2020 |
| Předmět: |
Materials science
Dopamine Monte Carlo method Original Articles 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Neurology Positron-Emission Tomography medicine Cluster size Humans Neurology (clinical) Sensitivity (control systems) Transient (oscillation) Current (fluid) Cardiology and Cardiovascular Medicine Biological system Monte Carlo Method 030217 neurology & neurosurgery medicine.drug |
| Zdroj: | J Cereb Blood Flow Metab |
| ISSN: | 1559-7016 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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