Significant decreases in [11C]ABP688 binding after a mismatch negativity paradigm - Brain PET Poster Sessions PP01-M01 to PP02-N07
Régio Brambilla, Claudia
Matusch, Andreas / Mauler, Jörg / Rajkumar, Ravichandran / Rota Kops, Elena / Boers, Frank / Herzog, Hans / Shah, N. J. / Lerche, Christoph / Neuner, Irene
Physik der Medizinischen Bildgebung; INM-4
Jülich-Aachen Research Alliance - Translational Brain Medicine; JARA-BRAIN
Jara-Institut Quantum Information; INM-11
2019
2019
10.1177/0271678X19851018
BrainPET 2019, Yokohama (Japan), 2019-07-04 - 2019-07-07
Abstract
Neuroimaging
Please use the identifier: http://dx.doi.org/10.1177/0271678X19851018 in citations.


The present work explores a novel estimation method based on a Markov-Chain Monte-Carlo (MCMC) sampling in a Bayesian context. This new methodology allows the integration of prior knowledge on parameters that constrain the available solutions, which alleviates the identifia-bility problem. It also quantifies the uncertainty of the parameters and does not rely on a set of chosen basis functions as the original method does. Methods: The proposed method relies on a stochastic approach which consists in assessing the whole posterior distribution of the parameters, i.e. estimating the probability of the parameters given the measurements. According to Bayes rule, the posterior distribution is proportional to the product of the likelihood and the prior. The likelihood, which corresponds to the noise model, is here considered normally distributed with the variance at each time point scaled by the decay factor and the frame duration. The prior is a uniform distribution in an interval of plausible values for each parameter. The posterior distribution is then approximated with MCMC using a hybrid Metropolis-within-Gibbs sampler, 2 where the step size of each chain is adjusted to ensure an optimal mixing behavior. For performance evaluation of the method, realistic dynamic whole brain PET data of a 90-minute bolus-infusion [ 11 C]raclopride protocol have been simulated using PET-SORTEO 3 for 21 structurally different subjects. Time activity curves (TAC) used as input for simulations included a dopamine release at 40 minutes in specific regions (Caudate, Accumbens and Putamen), with four different magnitudes of TAC decrease (0, 5, 10 and 25% of the basal TAC, respectively named placebo, stim05, stim10, stim25). To fit the lp-ntPET model on the reconstructed TACs, 5000 samples have been drawn with the proposed MCMC sampler. This few amount of samples has been shown to be sufficient considering the early convergence of the chains. Results: The maximum a-posteriori (MAP) estimates of each parameter have been derived from the marginal distributions for each condition and each region over the 21 subjects. Figure 1 shows the Displacement Ratio 4 calculated from these estimates. Results show that the obtained DRs allow to better distinguish the four simulated experimental conditions than the original method, even on small noisy regions such as the Accumbens. Conclusions: The early results show promising potential for estimating the parameters of the lp-ntPET model in a Bayesian framework. Current work explores the resolution of lp-ntPET at a voxel level, by integrating prior knowledge on spatial regularity with or without the use of anatomical MRI. Abstract Objectives: The glutamatergic receptor (mGluR5) is under investigation in clinical neurology and of great interest in several lines of research [1-3]. We assessed the feasibility of using [ 11 C]ABP688 to evaluate changes in glutamate levels through the Mismatch Negativity (MMN) auditory paradigm 4 as a part of a multimodal study. 5 Methods: Five healthy, male, non-smoking subjects were scanned with a Siemens 3 T MR-BrainPET insert. We analyzed the effect of MMN comparing the changes in non-displaceable binding potential (BP ND) prior, during and after the MMN with a bolus/infusion protocol during the tracer steady-state (50% of the total injected activity (446.4 AE 106.0 MBq) was infused during 65 minutes after bolus injection). Image reconstruction was performed with 3D-OP-OSEM (2 subsets, 32 iterations), isotropic Abstracts 545 voxel ¼ 1.25 mm 3 , 153 slices, matrix of 256 Â 256 pixels, and a frame scheme of 2 minutes. PET frames were synchronized with the different acquisition moments. The images were corrected for attenuation [6], random and scattered coincidences, and dead time. Post processing with a 2.5 mm 3D Gaussian filter and motion correction were performed. Anatomical images were acquired with T1 MPRAGE sequence (TR ¼ 2250 ms, TE ¼ 3.03 ms, 176 slices, 1 mm slice thickness). The MMN paradigm consisted of changes in tone duration and was presented in alternating sequences. The deviant positions were pseudo-randomized and a silent video was presented to the subject. PMOD software was used to define the volumes of interest (VOIs) with T1 images serving as the anatomical reference. All images were finally processed in the PET subject's space, and the Hammers atlas 7 was used for activity concentration analysis. The maximum probability operation was applied to generate the VOIs in the grey matter cortex (GM). Furthermore, functional network regions, 8 GM corrected, were also applied. Cerebellum GM was chosen as the reference region. Statistical analysis was performed using repeated ANOVA with inter-subject corrections. 9 Results: There was a significant ÁBP ND between conditions. On average, the reductions across all regions and subjects were of À11.46 AE 3.39%, F (2,6) ¼ 7.471; P < 0.05 in anatomical [FIGURE 1 (a)] and À10.37 AE 4.33%, F (2,8) ¼ 6.674; P < 0.05 in functional VOIs [FIGURE 1 (b)].