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@INPROCEEDINGS{RgioBrambilla:908213,
      author       = {Régio Brambilla, Claudia and Matusch, Andreas and Mauler,
                      Jörg and Rajkumar, Ravichandran and Rota Kops, Elena and
                      Boers, Frank and Herzog, Hans and Shah, N. J. and Lerche,
                      Christoph and Neuner, Irene},
      title        = {{S}ignificant decreases in [11{C}]{ABP}688 binding after a
                      mismatch negativity paradigm - {B}rain {PET} {P}oster
                      {S}essions {PP}01-{M}01 to {PP}02-{N}07},
      issn         = {1559-7016},
      reportid     = {FZJ-2022-02464},
      year         = {2019},
      abstract     = {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)].},
      month         = {Jul},
      date          = {2019-07-04},
      organization  = {BrainPET 2019, Yokohama (Japan), 4 Jul
                       2019 - 7 Jul 2019},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)VDB1046},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)1},
      pubmed       = {31265793},
      UT           = {WOS:000473954100004},
      doi          = {10.1177/0271678X19851018},
      url          = {https://juser.fz-juelich.de/record/908213},
}