Evaluation of 18F-flutemetamol amyloid PET image analysis parameters on the effect of verubecestat on brain amlyoid load in Alzheimer’s disease

Hardy JA, Higgins GA (1992) Alzheimer’s disease: the amyloid cascade hypothesis. Science 256:184–185

CAS  PubMed  Article  Google Scholar 

Nelson PT, Braak H, Markesbery WR (2009) Neuropathology and cognitive impairment in Alzheimer disease: a complex but coherent relationship. J Neuropathol Exp Neurol 68:1–14

CAS  PubMed  Article  Google Scholar 

Selkoe DJ, Hardy J (2016) The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol Med 8:595–608

CAS  PubMed  PubMed Central  Article  Google Scholar 

Femminella GD, Thayanandan T, Calsolaro V et al (2018) Imaging and molecular mechanisms of Alzheimer’s disease: a review. Int J Mol Sci 19:3702

PubMed Central  Article  CAS  Google Scholar 

Vandenberghe R, Adamczuk K, Dupont P, Laere KV, Chételat G (2013) Amyloid PET in clinical practice: Its place in the multidimensional space of Alzheimer’s disease. Neuroimage Clin 2:497–511

PubMed  PubMed Central  Article  Google Scholar 

Klunk WE, Engler H, Nordberg A et al (2004) Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol 55:306–319

CAS  PubMed  Article  Google Scholar 

Nelissen N, Van Laere K, Thurfjell L et al (2009) Phase 1 study of the Pittsburgh compound B derivative 18F-flutemetamol in healthy volunteers and patients with probable Alzheimer disease. J Nucl Med 50:1251–1259

CAS  PubMed  Article  Google Scholar 

Vandenberghe R, Van Laere K, Ivanoiu A et al (2010) 18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial. Ann Neurol 68:319–329

PubMed  Article  Google Scholar 

Adamczuk K, Schaeverbeke J, Nelissen N et al (2016) Amyloid imaging in cognitively normal older adults: comparison between (18)F-flutemetamol and (11)C-Pittsburgh compound B. Eur J Nucl Med Mol Imaging 43:142–151

CAS  PubMed  Article  Google Scholar 

Mountz JM, Laymon CM, Cohen AD et al (2015) Comparison of qualitative and quantitative imaging characteristics of [11C]PiB and [18F]flutemetamol in normal control and Alzheimer’s subjects. Neuroimage Clin 9:592–598

PubMed  PubMed Central  Article  Google Scholar 

Thurfjell L, Lilja J, Lundqvist R et al (2014) Automated quantification of 18F-flutemetamol PET activity for categorizing scans as negative or positive for brain amyloid: concordance with visual image reads. J Nucl Med 55:1623–1628

CAS  PubMed  Article  Google Scholar 

Duff K, Horn KP, Hoffman JM (2019) Long-term changes in 18F-flutemetamol uptake in nondemented older adults. Alzheimer Dis Assoc Disord 33:113–117

CAS  PubMed  PubMed Central  Article  Google Scholar 

Thal DR, Rub U, Orantes M, Braak H (2002) Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology 58:1791–1800

PubMed  Article  Google Scholar 

Thal DR, Beach TG, Zanette M et al (2018) Estimation of amyloid distribution by [18F]flutemetamol PET predicts the neuropathological phase of amyloid β-protein deposition. Acta Neuropathol 136:557–567

CAS  PubMed  PubMed Central  Article  Google Scholar 

Bucci M, Savitcheva I, Farrar G et al (2021) A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [(18)F]flutemetamol amyloid PET images. Eur J Nucl Med Mol Imaging 48:2183–2199

CAS  PubMed  PubMed Central  Article  Google Scholar 

Chen K, Roontiva A, Thiyyagura P et al (2015) Improved power for characterizing longitudinal amyloid-beta PET changes and evaluating amyloid-modifying treatments with a cerebral white matter reference region. J Nucl Med 56:560–566

CAS  PubMed  Article  Google Scholar 

Schwarz CG, Gunter JL, Lowe VJ et al (2019) A comparison of partial volume correction techniques for measuring change in serial amyloid PET SUVR. J Alzheimers Dis 67:181–195

CAS  PubMed  PubMed Central  Article  Google Scholar 

Yang J, Hu C, Guo N et al (2017) Partial volume correction for PET quantification and its impact on brain network in Alzheimer’s disease. Sci Rep 7:13035

PubMed  PubMed Central  Article  CAS  Google Scholar 

Meltzer CC, Kinahan PE, Greer PJ et al (1999) Comparative evaluation of MR-based partial-volume correction schemes for PET. J Nucl Med 40:2053–2065

CAS  PubMed  Google Scholar 

Rousset OG, Ma Y, Evans AC (1998) Correction for partial volume effects in PET: principle and validation. J Nucl Med 39:904–911

CAS  PubMed  Google Scholar 

Labbé C, Koepp M, Ashburner J, Spinks T, Richardson M, Duncan J, Cunningham V (1998) Absolute PET Quantification with Correction for Partial Volume Effects with Cerebral Structures. In: Carson RE, Daube-Witherspoon ME, Herscovitch P (eds) Quantitative functional brain imaging with positron emission tomography. Academic Press, San Diego, pp 59–66

Chapter  Google Scholar 

Sattarivand M, Kusano M, Poon I, Caldwell C (2012) Symmetric geometric transfer matrix partial volume correction for PET imaging: principle, validation and robustness. Phys Med Biol 57:7101–7116

PubMed  Article  Google Scholar 

Thompson PM, Hayashi KM, de Zubicaray G et al (2003) Dynamics of gray matter loss in Alzheimer’s disease. J Neurosci 23:994–1005

CAS  PubMed  PubMed Central  Article  Google Scholar 

Blinkouskaya Y, Weickenmeier J (2021) Brain Shape Changes Associated with Cerebral Atrophy in Healthy Aging and Alzheimer's Disease. Front Mech Eng. https://doi.org/10.3389/fmech.2021.705653

Scott JD, Li SW, Brunskill AP et al (2016) Discovery of the 3-imino-1,2,4-thiadiazinane 1,1-dioxide derivative verubecestat (MK-8931)-a β-site amyloid precursor protein cleaving enzyme 1 inhibitor for the treatment of Alzheimer’s Disease. J Med Chem 59:10435–10450

CAS  PubMed  Article  Google Scholar 

Kennedy ME, Stamford AW, Chen X et al (2016) The BACE1 inhibitor verubecestat (MK-8931) reduces CNS β-amyloid in animal models and in Alzheimer's disease patients. Sci Transl Med 8:363ra150

Forman M, Palcza J, Tseng J et al (2019) Safety, tolerability, and pharmacokinetics of the beta-site amyloid precursor protein-cleaving enzyme 1 inhibitor verubecestat (MK-8931) in healthy elderly male and female subjects. Clin Transl Sci 12:545–555

CAS  PubMed  PubMed Central  Article  Google Scholar 

Min KC, Dockendorf MF, Palcza J et al (2019) Pharmacokinetics and pharmacodynamics of the BACE1 inhibitor verubecestat (MK-8931) in healthy Japanese adults: a randomized, placebo-controlled study. Clin Pharmacol Ther 105:1234–1243

Article  CAS  Google Scholar 

Vassar R (2014) BACE1 inhibitor drugs in clinical trials for Alzheimer’s disease. Alzheimers Res Ther 6:89

PubMed  PubMed Central  Article  CAS  Google Scholar 

Vassar R, Bennett BD, Babu-Khan S et al (1999) Beta-secretase cleavage of Alzheimer’s amyloid precursor protein by the transmembrane aspartic protease BACE. Science 286:735–741

CAS  PubMed  Article  Google Scholar 

Egan MF, Kost J, Tariot PN et al (2018) Randomized trial of verubecestat for mild-to-moderate Alzheimer’s disease. N Engl J Med 378:1691–1703

CAS  PubMed  PubMed Central  Article  Google Scholar 

Sur C, Kost J, Scott D et al (2020) BACE inhibition causes rapid, regional, and non-progressive volume reduction in Alzheimer’s disease brain. Brain 143:3816–3826

PubMed  PubMed Central  Article  Google Scholar 

Landau SM, Thomas BA, Thurfjell L, the Alzheimer’s Disease Neuroimaging Intiative, et al (2014) Amyloid PET imaging in Alzheimer’s disease: a comparison of three radiotracers. Eur J Nucl Med Mol Imaging 41:398–1407

Article  CAS  Google Scholar 

Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289

CAS  PubMed  Article  Google Scholar 

Greve DN, Svarer C, Fisher PM et al (2014) Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data. Neuroimage 92:225–236

PubMed  Article  Google Scholar 

Greve DN, Salat DH, Bowen SL et al (2016) Different partial volume correction methods lead to different conclusions: An (18)F-FDG-PET study of aging. Neuroimage 132:334–343

PubMed  Article  Google Scholar 

Kalheim LF, Fladby T, Coello C, Bjørnerud A, Selnes P (2018) [18F]-Flutemetamol uptake in cortex and white matter: comparison with cerebrospinal fluid biomarkers and [18F]-fludeoxyglucose. J Alzheimers Dis 62:1595–1607

CAS  PubMed  PubMed Central  Article  Google Scholar 

Heurling K, Buckley C, Vandenberghe R, Laere KV, Lubberink M (2015) Separation of β-amyloid binding and white matter uptake of (18)F-flutemetamol using spectral analysis. Am J Nucl Med Mol Imaging 5:515–526

CAS  PubMed  PubMed Central  Google Scholar 

Lowe VJ, Lundt E, Knopman D et al (2017) Comparison of [18F]flutemetamol and [11C]Pittsburgh Compound-B in cognitively normal young, cognitively normal elderly, and Alzheimer’s disease dementia individuals. NeuroImage Clinical 16:295–302

PubMed  PubMed Central  Article  Google Scholar 

Fleisher AS, Joshi AD, Sundell KL et al (2017) Use of white matter reference regions for detection of change in florbetapir positron emission tomography from completed phase 3 solanezumab trials. Alzheimers Dement 13:1117–1124

PubMed 

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