Dataset Information

A Monte Carlo approach for improving transient dopamine release detection sensitivity.

ABSTRACT: 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.

SUBMITTER: Bevington CW

PROVIDER: S-EPMC7747166 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

A Monte Carlo approach for improving transient dopamine release detection sensitivity.

Bevington Connor Wj CW Cheng Ju-Chieh Kevin JK Klyuzhin Ivan S IS Cherkasova Mariya V MV Winstanley Catharine A CA Sossi Vesna V

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20200212 1

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 incorp ...[more]

PMID: 32050828

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Dataset Information

A Monte Carlo approach for improving transient dopamine release detection sensitivity.

Publications

A Monte Carlo approach for improving transient dopamine release detection sensitivity.

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