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Overhoff F, Brendel M, Jaworska A, et al. Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [(18)F]-Florbetaben PET Quantitation in Alzheimer's Model Mice. Front Neurosci. 2016;10:45doi: 10.3389/fnins.2016.00045.
Overhoff, F., Brendel, M., Jaworska, A., Korzhova, V., Delker, A., Probst, F., Focke, C., Gildehaus, F. J., Carlsen, J., Baumann, K., Haass, C., Bartenstein, P., Herms, J., & Rominger, A. (2016). Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [(18)F]-Florbetaben PET Quantitation in Alzheimer's Model Mice. Frontiers in neuroscience, 1045. https://doi.org/10.3389/fnins.2016.00045
Overhoff, Felix, et al. "Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [(18)F]-Florbetaben PET Quantitation in Alzheimer's Model Mice." Frontiers in neuroscience vol. 10 (2016): 45. doi: https://doi.org/10.3389/fnins.2016.00045
Overhoff F, Brendel M, Jaworska A, Korzhova V, Delker A, Probst F, Focke C, Gildehaus FJ, Carlsen J, Baumann K, Haass C, Bartenstein P, Herms J, Rominger A. Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [(18)F]-Florbetaben PET Quantitation in Alzheimer's Model Mice. Front Neurosci. 2016 Feb 29;10:45. doi: 10.3389/fnins.2016.00045. eCollection 2016. PMID: 26973442; PMCID: PMC4770021.
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Front Neurosci. 2016 Feb 29;10:45. doi: 10.3389/fnins.2016.00045. eCollection 2016.

Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [(18)F]-Florbetaben PET Quantitation in Alzheimer's Model Mice.

Frontiers in neuroscience

Felix Overhoff, Matthias Brendel, Anna Jaworska, Viktoria Korzhova, Andreas Delker, Federico Probst, Carola Focke, Franz-Josef Gildehaus, Janette Carlsen, Karlheinz Baumann, Christian Haass, Peter Bartenstein, Jochen Herms, Axel Rominger

Affiliations

  1. Department of Nuclear Medicine, Ludwig-Maximilians-University of Munich Munich, Germany.
  2. DZNE-German Center for Neurodegenerative DiseasesMunich, Germany; Laboratory of Neurodegeneration, International Institute of Molecular and Cell BiologyWarsaw, Poland.
  3. DZNE-German Center for Neurodegenerative Diseases Munich, Germany.
  4. Roche Pharma Research and Early Development, Neuroscience Discovery, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd Basel, Switzerland.
  5. DZNE-German Center for Neurodegenerative DiseasesMunich, Germany; Munich Cluster for Systems Neurology (SyNergy)Munich, Germany; Biomedical Center, Ludwig-Maximilians-University of MunichMunich, Germany.
  6. Department of Nuclear Medicine, Ludwig-Maximilians-University of MunichMunich, Germany; Munich Cluster for Systems Neurology (SyNergy)Munich, Germany.
  7. DZNE-German Center for Neurodegenerative DiseasesMunich, Germany; Munich Cluster for Systems Neurology (SyNergy)Munich, Germany.

PMID: 26973442 PMCID: PMC4770021 DOI: 10.3389/fnins.2016.00045

Abstract

Preclinical PET studies of β-amyloid (Aβ) accumulation are of growing importance, but comparisons between research sites require standardized and optimized methods for quantitation. Therefore, we aimed to evaluate systematically the (1) impact of an automated algorithm for spatial brain normalization, and (2) intensity scaling methods of different reference regions for Aβ-PET in a large dataset of transgenic mice. PS2APP mice in a 6 week longitudinal setting (N = 37) and another set of PS2APP mice at a histologically assessed narrow range of Aβ burden (N = 40) were investigated by [(18)F]-florbetaben PET. Manual spatial normalization by three readers at different training levels was performed prior to application of an automated brain spatial normalization and inter-reader agreement was assessed by Fleiss Kappa (κ). For this method the impact of templates at different pathology stages was investigated. Four different reference regions on brain uptake normalization were used to calculate frontal cortical standardized uptake value ratios (SUVRCTX∕REF), relative to raw SUVCTX. Results were compared on the basis of longitudinal stability (Cohen's d), and in reference to gold standard histopathological quantitation (Pearson's R). Application of an automated brain spatial normalization resulted in nearly perfect agreement (all κ≥0.99) between different readers, with constant or improved correlation with histology. Templates based on inappropriate pathology stage resulted in up to 2.9% systematic bias for SUVRCTX∕REF. All SUVRCTX∕REF methods performed better than SUVCTX both with regard to longitudinal stability (d≥1.21 vs. d = 0.23) and histological gold standard agreement (R≥0.66 vs. R≥0.31). Voxel-wise analysis suggested a physiologically implausible longitudinal decrease by global mean scaling. The hindbrain white matter reference (R mean = 0.75) was slightly superior to the brainstem (R mean = 0.74) and the cerebellum (R mean = 0.73). Automated brain normalization with reference region templates presents an excellent method to avoid the inter-reader variability in preclinical Aβ-PET scans. Intracerebral reference regions lacking Aβ pathology serve for precise longitudinal in vivo quantification of [(18)F]-florbetaben PET. Hindbrain white matter reference performed best when considering the composite of quality criteria.

Keywords: Alzheimer's disease; [18F]-florbetaben; brain normalization; reference region; small animal PET; β-amyloid

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