Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada.
Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, Canada.
J Cereb Blood Flow Metab. 2021 Jan;41(1):116-131. doi: 10.1177/0271678X20905613. Epub 2020 Feb 12.
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.
目前使用单一 PET 扫描来检测体素水平瞬态多巴胺释放的方法——使用 F 检验（显著性）和聚类大小阈值——对体积小或释放水平低的释放簇的检测灵敏度有限。具体来说，模拟表明，释放接近这些簇的边缘的体素通常会被拒绝——成为假阴性，最终扭曲了被拒绝体素的 F 分布。我们建议一种蒙特卡罗方法，将这两个观察结果纳入一个成本函数，允许在指定条件下接受错误拒绝的体素。在模拟中，所提出的方法在保持聚类大小阈值的情况下，将检测灵敏度提高了多达 50%，或者在优化灵敏度的情况下，提高了多达 180%。然后建议使用基于参数的体素级别的阈值来更好地估计检测到的聚类中的释放动力学。我们将蒙特卡罗方法应用于人类赌博研究的一项试点扫描中，与当前最佳方法相比，检测到了更多的参数独特聚类——与我们的模拟结果一致。
