A Monte Carlo approach for improving transient dopamine release detection sensitivity

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.

Spatiotemporal Kernel Reconstruction for Linear Parametric Neurotransmitter PET Kinetic Modeling in Motion Correction Brain PET of Awake Rats

The linear parametric neurotransmitter positron emission tomography (lp-ntPET) kinetic model can be used to detect transient changes (activation) in endogenous neurotransmitter levels. Preclinical PET scans in awake animals can be performed to investigate neurotransmitter transient changes. Here we use the spatiotemporal kernel reconstruction (Kernel) for noise reduction in dynamic PET, and lp-ntPET kinetic modeling. Kernel is adapted for motion correction reconstruction, applied in awake rat PET scans. We performed 2D rat brain phantom simulation using the ntPET model at 3 different noise levels. Data was reconstructed with independent frame reconstruction (IFR), IFR with HYPR denoising, and Kernel, and lp-ntPET kinetic parameters (k2a: efflux rate, γ: activation magnitude, td: activation onset time, and tp: activation peak time) were calculated. Additionally, significant activation magnitude (γ) difference with respect to a region with no activation (rest) was calculated. Finally, [11C]raclopride experiments were performed in anesthetized and awake rats, injecting cold raclopride at 20 min after scan start to simulate endogenous neurotransmitter release. For simulated data at the regional level, IFR coefficient of variation (COV) of k2a, γ, td and tp was reduced with HYPR denoising, but Kernel showed the lowest COV (2 fold reduction compared with IFR). At the pixel level the same trend is observed for k2a, γ, td and tp COV, but reduction is larger with Kernel compared with IFR (10–14 fold). Bias in γ with respect with noise-free values was additionally reduced using Kernel (difference of 292, 72.4, and −6.92% for IFR, IFR+KYPR, and Kernel, respectively). Significant difference in activation between the rest and active region could be detected at a simulated activation of 160% for IFR and IFR+HYPR, and of 120% for Kernel. In rat experiments, lp-ntPET parameters have better confidence intervals using Kernel. In the γ, and td parametric maps, the striatum structure can be identified with Kernel but not with IFR. Striatum voxel-wise γ, td and tp values have lower variability using Kernel compared with IFR and IFR+HYPR. The spatiotemporal kernel reconstruction adapted for motion correction reconstruction allows to improve lp-ntPET kinetic modeling noise in awake rat studies, as well as detection of subtle neurotransmitter activations.

Assessment of transient dopamine responses to smoked cannabis

BACKGROUND

Dopaminergic mechanisms that may underlie cannabis' reinforcing effects are not well elucidated in humans. This positron emission tomography (PET) imaging study used the dopamine D2/3 receptor antagonist [11C]raclopride and kinetic modelling testing for transient changes in radiotracer uptake to assess the striatal dopamine response to smoked cannabis in a preliminary sample.

METHODS

PET emission data were acquired from regular cannabis users (n = 14; 7 M/7 F; 19-32 years old) over 90 min immediately after [11C]raclopride administration (584 ± 95 MBq) as bolus followed by constant infusion (Kbol = 105 min). Participants smoked a cannabis cigarette, using a paced puff protocol, 35 min after scan start. Plasma concentrations of Δ9-THC and metabolites and ratings of subjective "high" were collected during imaging. Striatal dopamine responses were assessed voxelwise with a kinetic model testing for transient reductions in [11C]raclopride binding, linear-parametric neurotransmitter PET (lp-ntPET) (cerebellum as a reference region).

RESULTS

Cannabis smoking increased plasma Δ9-THC levels (peak: 0-10 min) and subjective high (peak: 0-30 min). Significant clusters (>16 voxels) modeled by transient reductions in [11C]raclopride binding were identified for all 12 analyzed scans. In total, 26 clusters of significant responses to cannabis were detected, of which 16 were located in the ventral striatum, including at least one ventral striatum cluster in 11 of the 12 analyzed scans.

CONCLUSIONS

These preliminary data support the sensitivity of [11C]raclopride PET with analysis of transient changes in radiotracer uptake to detect cannabis smoking-induced dopamine responses. This approach shows future promise to further elucidate roles of mesolimbic dopaminergic signaling in chronic cannabis use. ClinicalTrials.gov Identifier: NCT02817698.

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.

Methods for Quantifying Neurotransmitter Dynamics in the Living Brain With PET Imaging

Positron emission tomography (PET) neuroimaging in neuropsychiatry is a powerful tool for the quantification of molecular brain targets to characterize disease, assess disease subtype differences, evaluate short- and long-term effects of treatments, or even to measure neurotransmitter levels in healthy and psychiatric conditions. In this work, we present different methodological approaches (time-invariant models and models with time-varying terms) that have been used to measure dynamic changes in neurotransmitter levels induced by pharmacological or behavioral challenges in humans. The developments and potential use of hybrid PET/magnetic resonance imaging (MRI) for neurotransmission brain research will also be highlighted.