ArticleJournal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism2021
A Monte Carlo approach for improving transient dopamine release detection sensitivity.
Article in Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Modeling PET Data Acquired During Nonsteady Conditions: What If Brain Conditions Change During the Scan?Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Review
- Novel voxelwise residual analysis of [Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024Article
- Spatiotemporal Kernel Reconstruction for Linear Parametric Neurotransmitter PET Kinetic Modeling in Motion Correction Brain PET of Awake Rats.Frontiers in neuroscience · 2022Article
- NRM 2021 Abstract Booklet.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021Article
- Assessment of transient dopamine responses to smoked cannabis.Drug and alcohol dependence · 2021Article
- Detecting and classifying neurotransmitter signals from ultra-high sensitivity PET data: the future of molecular brain imaging.Physics in medicine and biology · 2021Article
- Methods for Quantifying Neurotransmitter Dynamics in the Living Brain With PET Imaging.Frontiers in physiology · 2020Review
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6 authors.
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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.
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