Voxelwise lp-ntPET for detecting localized, transient dopamine release of unknown timing: sensitivity analysis and application to cigarette smoking in the PET scanner

Abstract

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.

Figure 1

Response functions of lp‐ntPET to model transient dopamine release at t _d = 40 min. Gamma‐variate function with α equal to 0.25 (a), 1 (b), and 4 (c) and exponential function with β = [0, 0.01, 0.03, 0.05, 0.1, 0.3, 0.5] (d).

Figure 2

Single PET voxel rest data (a) with fitted curve using MRTM in ventral striatum region (solid line) and single voxel level‐simulated rest data (b) with true value (solid line).

Figure 3

Distribution of F‐ratio based on (a) simulated rest data and (b) real‐rest PET data in human subject. F‐ratios are generated at each voxel in dorsal striatum by comparing the weighted residual sum of squares of the lp‐ntPET fit to that of the MRTM fit to the same TAC.

Figure 4

Cluster‐size distribution after thresholding F maps produced from the simulated rest data. Solid curve represents cumulative probability. Dotted vertical line shows where to set cluster‐size threshold to exclude 99% (P < 0.01) of clusters that will occur by chance in the lp‐ntPET analysis of rest data (no dopamine activation) based on 100 simulations of phantom images.

Figure 5

(a) Dopamine response with increasing peak height used to create simulated smoking data. Baseline DA level equals to 100 pmol/mL. Simulated TAC is decay‐corrected. (b) Single noisy simulated smoking TAC with peak DA level 200.

Figure 6

Sensitivity map in whole dorsal striatum region at given uniform dopamine signal over 100 runs (peak DA = 200).

Figure 7

Four‐slice phantom containing clusters of different sizes (top) and sensitivity map (peak DA = 200; bottom). Number with arrow indicates the number of voxels contained in each cluster. Voxel size is 2 × 2 × 2 mm³.

Figure 8

Sensitivity (ability to recover true DA activation) as a function of peak dopamine level and cluster size. Voxel size is 2 × 2 × 2 mm³.

Figure 9

Bias of dopamine parameters for different dopamine levels.

Figure 10

Significant voxels detected in PET data for two different smokers at rest and during smoking (top and bottom).

Figure 11

DA response curves for each voxel in each significant cluster (b–d) in four different slices through the striatum (a) in one subject's smoking scan (VST, ventral striatum; PUT, putamen; CAU, caudate).

Figure 12

Average DA curves in the significant clusters of one subject's smoking scan. Solid curves represent the mean of the data curves in Figure 11, and the dotted curves represent the mean + SD and the mean − SD, respectively.