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

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.

Novel voxelwise residual analysis of [11C]raclopride PET data improves detection of low-amplitude dopamine release.

Existing methods for voxelwise transient dopamine (DA) release detection rely on explicit kinetic modeling of the [11C]raclopride PET time activity curve, which at the voxel level is typically confounded by noise, leading to poor performance for detection of low-amplitude DA release-induced signals. Here we present a novel data-driven, task-informed method-referred to as Residual Space Detection (RSD)-that transforms PET time activity curves to a residual space where DA release-induced perturbations can be isolated and processed. Using simulations, we demonstrate that this method significantly increases detection performance compared to existing kinetic model-based methods for low-magnitude DA release (simulated +100% peak increase in basal DA concentration). In addition, results from nine healthy controls injected with a single bolus of [11C]raclopride performing a finger tapping motor task are shown as proof-of-concept. The ability to detect relatively low magnitudes of dopamine release in the human brain using a single bolus injection, while achieving higher statistical power than previous methods, may additionally enable more complex analyses of neurotransmitter systems. Moreover, RSD is readily generalizable to multiple tasks performed during a single PET scan, further extending the capabilities of task-based single-bolus protocols.

A Monte Carlo approach for improving transient dopamine release detection sensitivity.

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.

Detecting and classifying neurotransmitter signals from ultra-high sensitivity PET data: the future of molecular brain imaging.

Efforts to build the next generation of brain PET scanners are underway. It is expected that a new scanner (NS) will offer anorder-of-magnitude improvementin sensitivity to counts compared to the current state-of-the-art, Siemens HRRT. Our goal was to explore the use of the anticipated increased sensitivity in combination with the linear-parametric neurotransmitter PET (lp-ntPET) model to improve detection and classification of transient dopamine (DA) signals. We simulated striatal [11C]raclopride PET data to be acquired on a future NS which will offer ten times the sensitivity of the HRRT. The simulated PET curves included the effects of DA signals that varied in start-times, peak-times, and amplitudes. We assessed the detection sensitivity of lp-ntPET to various shapes of DA signal. We evaluated classification thresholds for their ability to separate 'early'- versus 'late'-peaking, and 'low'- versus 'high'-amplitude events in a 4D phantom. To further refine the characterization of DA signals, we developed a weighted k-nearest neighbors (wkNN) algorithm to incorporate information from the neighborhood around each voxel to reclassify it, with a level of certainty. Our findings indicate that the NS would expand the range of detectable neurotransmitter events to 72%, compared to the HRRT (31%). Application of wkNN augmented the detection sensitivity to DA signals in simulated NS data to 92%. This work demonstrates that the ultra-high sensitivity expected from a new generation of brain PET scanner, combined with a novel classification algorithm, will make it possible to accurately detect and classify short-lived DA signals in the brain based on their amplitude and timing.

Bayesian Estimation of the ntPET Model in Single-Scan Competition PET Studies.

This paper proposes an innovative method, named b-ntPET, for solving a competition model in PET. The model is built upon the state-of-the-art method called lp-ntPET. It consists in identifying the parameters of the PET kinetic model relative to a reference region that rule the steady state exchanges, together with the identification of four additional parameters defining a displacement curve caused by an endogenous neurotransmitter discharge, or by a competing injected drug targeting the same receptors as the PET tracer. The resolution process of lp-ntPET is however suboptimal due to the use of discretized basis functions, and is very sensitive to noise, limiting its sensitivity and accuracy. Contrary to the original method, our proposed resolution approach first estimates the probability distribution of the unknown parameters using Markov-Chain Monte-Carlo sampling, distributions from which the estimates are then inferred. In addition, and for increased robustness, the noise level is jointly estimated with the parameters of the model. Finally, the resolution is formulated in a Bayesian framework, allowing the introduction of prior knowledge on the parameters to guide the estimation process toward realistic solutions. The performance of our method was first assessed and compared head-to-head with the reference method lp-ntPET using well-controlled realistic simulated data. The results showed that the b-ntPET method is substantially more robust to noise and much more sensitive and accurate than lp-ntPET. We then applied the model to experimental animal data acquired in pharmacological challenge studies and human data with endogenous releases induced by transcranial direct current stimulation. In the drug challenge experiment on cats using [18F]MPPF, a serotoninergic 1A antagonist radioligand, b-ntPET measured a dose response associated with the amount of the challenged injected concurrent 5-HT1A agonist, where lp-ntPET failed. In human [11C]raclopride experiment, contrary to lp-ntPET, b-ntPET successfully detected significant endogenous dopamine releases induced by the stimulation. In conclusion, our results showed that the proposed method b-ntPET has similar performance to lp-ntPET for detecting displacements, but with higher resistance to noise and better robustness to various experimental contexts. These improvements lead to the possibility of detecting and characterizing dynamic drug occupancy from a single PET scan more efficiently.