A Monte Carlo approach for boosting transient dopamine release detection sensitivity

Current methods to detect voxel-level transient dopamine release that use F-test (significance) and cluster size thresholding have poor detection sensitivity in clusters of release that are small in size and/or have 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 novel Monte Carlo approach that incorporates these two observations into a cost function, allowing erroneously rejected voxels to be accepted under certain criteria. In simulations, the proposed method is able to boost sensitivity by up to 77% at equivalent cluster size threshold and up to 200% when optimizing for sensitivity.