The "linear parametric neurotransmitter PET" (lp-ntPET) model estimates time variation in endogenous neurotransmitter levels from dynamic PET data. The pattern of dopamine (DA) change over time may be an important element of the brain's response to addictive substances such as cigarettes or alcohol. We have extended the lp-ntPET model from the original region of interest (ROI) - based implementation to be able to apply the model at the voxel level. The resulting endpoint is a dynamic image, or movie, of transient neurotransmitter changes. Simulations were performed to select threshold values to reduce the false positive rate when applied to real (11)C-raclopride PET data. We tested the new voxelwise method on simulated data, and finally, we applied it to (11)C-raclopride PET data of subjects smoking cigarettes in the PET scanner. In simulation, the temporal precision of neurotransmitter response was shown to be similar to that of ROI-based lp-ntPET (standard deviation ∼ 3 min). False positive rates for the voxelwise method were well controlled by combining a statistical threshold (the F-test) with a new spatial (cluster-size) thresholding operation. Sensitivity of detection for the new algorithm was greater than 80% for the case of short-lived DA changes that occur in subregions of the striatum as might be the case with cigarette smoking. Finally, in (11)C-raclopride PET data, DA movies reveal for the first time that different temporal patterns of the DA response to smoking may exist in different subregions of the striatum. These spatiotemporal patterns of neurotransmitter change created by voxelwise lp-ntPET may serve as novel biomarkers for addiction and/or treatment efficacy.
Keywords: dopamine; lp-ntPET; nicotine; sensitivity; time-varying parameters; voxel analysis.
Copyright © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.