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A European multicentre PET study of fibrillar amyloid in Alzheimer’s disease
Open accessAbstract
PURPOSE: Amyloid PET tracers have been developed for in vivo detection of brain fibrillar amyloid deposition in Alzheimer's disease (AD). To serve as an early biomarker in AD the amyloid PET tracers need to be analysed in multicentre clinical studies. METHODS: In this study 238 [(11)C]Pittsburgh compound-B (PIB) datasets from five different European centres were pooled. Of these 238 datasets, 18 were excluded, leaving [(11)C]PIB datasets from 97 patients with clinically diagnosed AD (mean age 69 ± 8 years), 72 patients with mild cognitive impairment (MCI; mean age 67.5 ± 8 years) and 51 healthy controls (mean age 67.4 ± 6 years) available for analysis. Of the MCI patients, 64 were longitudinally followed for 28 ± 15 months. Most participants (175 out of 220) were also tested for apolipoprotein E (ApoE) genotype. RESULTS: [(11)C]PIB retention in the neocortical and subcortical brain regions was significantly higher in AD patients than in age-matched controls. Intermediate [(11)C]PIB retention was observed in MCI patients, with a bimodal distribution (64 % MCI PIB-positive and 36 % MCI PIB-negative), which was significantly different the pattern in both the AD patients and controls. Higher [(11)C]PIB retention was observed in MCI ApoE ε4 carriers compared to non-ApoE ε4 carriers (p < 0.005). Of the MCI PIB-positive patients, 67 % had converted to AD at follow-up while none of the MCI PIB-negative patients converted. CONCLUSION: This study demonstrated the robustness of [(11)C]PIB PET as a marker of neocortical fibrillar amyloid deposition in brain when assessed in a multicentre setting. MCI PIB-positive patients showed more severe memory impairment than MCI PIB-negative patients and progressed to AD at an estimated rate of 25 % per year. None of the MCI PIB-negative patients converted to AD, and thus PIB negativity had a 100 % negative predictive value for progression to AD. This supports the notion that PIB-positive scans in MCI patients are an indicator of prodromal AD.
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“Numerous studies have shown that CU individuals with abnormal amyloid biomarkers have more rapid progression of atrophy, hypometabolism, and clinical/cognitive decline than individuals without biomarker evidence of Aβ deposition [13,33,80,93–99]”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [94]
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Cites
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657
Mild cognitive impairment as a diagnostic entity
Petersen, R. C. (2004) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2004.01388.x · Cited by 7,864
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
Diagnostic and Statistical Manual of Mental Disorders ( DSM ‐ IV ‐ TR )
Segal, D. L. (2010) · Wiley eBooks · doi:10.1002/9780470479216.corpsy0271 · Cited by 4,687
Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound‐B
Klunk, W. E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D. P., Bergström, M., Savitcheva, I., Huang, G., Estrada, S., et al. (2004) · Annals of Neurology · doi:10.1002/ana.20009 · Cited by 4,496
Self-citationResearch criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria
Dubois, B., Feldman, H. H., Jacova, C., DeKosky, S. T., Barberger-Gateau, P., Cummings, J., Delacourte, A., Galasko, D., Gauthier, S., Jicha, G., et al. (2007) · The Lancet Neurology · doi:10.1016/s1474-4422(07)70178-3 · Cited by 4,411
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Revising the definition of Alzheimer's disease: a new lexicon
Dubois, B., Feldman, H. H., Jacova, C., Cummings, J. L., DeKosky, S. T., Barberger-Gateau, P., Delacourte, A., Frisoni, G., Fox, N. C., Galasko, D., et al. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(10)70223-4 · Cited by 1,869
Frequency of Stages of Alzheimer-Related Lesions in Different Age Categories
Braak, H., & Braak, E. (1997) · Neurobiology of Aging · doi:10.1016/s0197-4580(97)00056-0 · Cited by 1,656
Flagged first, then by citations
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Research works printed but not linked: 1
- Collignon A, Maes F, Delaere D, Vandermeulen D, Suetens P, Marchal G. Automated multi-modality image registration based on information theory. Proceedings of the International Conference on Information Processing in Medical Imaging. Dordrecht: Kluwer Academic; 1995.
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Items 101–185 of 185
Forsberg, A., Juréus, A., Cselényi, Z., Eriksdotter, M., Freund‐Levi, Y., Jeppsson, F., Swahn, B., Sandell, J., Julin, P., Schou, M., et al. (2013) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-012-2322-6 · Cited by 19
Self-citationKim, S., Kim, Y. S., & Kim, S. (2020) · Journal of Cardiology · doi:10.1016/j.jjcc.2020.07.003 · Cited by 20
The Relationship between Amyloid Deposition, Neurodegeneration, and Cognitive Decline in Dementia
Vandenberghe, R. R. (2014) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-014-0498-9 · Cited by 21
Self-citationSeo, E. H., Park, W. Y., & Choo, I. H. (2017) · Psychiatry Investigation · doi:10.4306/pi.2017.14.2.205 · Cited by 21
The Use of Random Forests to Classify Amyloid Brain PET
Zukotynski, K., Gaudet, V., Kuo, P. H., Adamo, S. M., Goubran, M., Scott, C. J. M., Bocti, C., BORRIE, M. J., Chertkow, H. M., Frayne, R., et al. (2019) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000002747 · Cited by 19
Optimal Target Region for Subject Classification on the Basis of Amyloid PET Images
Carbonell, F., Zijdenbos, A. P., Charil, A., Grand’Maison, M., & Bedell, B. J. (2015) · Journal of Nuclear Medicine · doi:10.2967/jnumed.115.158774 · Cited by 19
Diagnostic Value of 11C-PIB PET/MR in Cardiac Amyloidosis
Bi, X., Xu, B., Liu, J., Wang, G., An, J., Zhang, X., Wang, R., Dong, W., & Guan, Z. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.830572 · Cited by 18
Li, Y., Zhang, T., Feng, J., Qian, S., Wu, S., Zhou, R., Wang, J., Sa, G., Wang, X., Li, L., et al. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-05740-w · Cited by 17
Biomarkers and Tools for Predicting Alzheimer's Disease in the Preclinical Stage
Li, T., Yang, Q. Z., Hu, X., & Han, Y. (2022) · Kölner Universitäts PublikationsServer (Universität zu Köln) · Cited by 17
Ellis, K. A., Rainey‐Smith, S. R., Rembach, A., Macaulay, S. L., Villemagne, V. L., & Aibl research group (2013) · International Review of Psychiatry · doi:10.3109/09540261.2013.870136 · Cited by 17
Initiative, J. A. D. N., Akamatsu, G., Ikari, Y., Ohnishi, A., Matsumoto, K., Nishida, H., Yamamoto, Y., & Senda, M. (2019) · EJNMMI Research · doi:10.1186/s13550-019-0561-2 · Cited by 16
Kang, K. M., Sohn, C., Byun, M. S., Lee, J. H., Yi, D., Lee, Y., Lee, J., Kim, Y. K., Sohn, B. K., Yoo, R., et al. (2020) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s252293 · Cited by 16
Wang, Y., Lou, F., Li, Y., Liu, F., Wang, Y., Cai, L., Gordon, M. L., Zhang, Y., & Zhang, N. (2021) · Current Alzheimer Research · doi:10.2174/1567205018666211001113349 · Cited by 14
Yang, B., Chen, J., Chou, W.-H., Huang, W.-S., Fuh, J., Liu, R., & Wu, C.-H. (2020) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-020-00548-1 · Cited by 13
Initiative, A. D. N., Chiotis, K., Carter, S. F., Farid, K., Savitcheva, I., & Nordberg, A. K. (2015) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-015-3115-5 · Cited by 16
Self-citationSeo, E. H., Kim, S. H., Park, S. H., Kang, S., Choo, I. H., & Initiative, A. D. N. (2016) · Journal of Korean Medical Science · doi:10.3346/jkms.2016.31.2.286 · Cited by 15
FDG PET/MR Imaging in Major Neurocognitive Disorders
Mainta, I., Perani, D., Delattre, B. M. A., Assal, F., Haller, S., Vargas, M. I., Zekry, D., Frisoni, G. B., Zaidi, H., Ratib, O. M., et al. (2016) · Current Alzheimer Research · doi:10.2174/1567205013666160620115130 · Cited by 14
Self-citationImaging Neuroinflammation: Quantification of Astrocytosis in a Multitracer PET Approach
Rodriguez‐Vieitez, E., Kumar, A., Malarte, M., Ioannou, K., Rocha, F. M., & Chiotis, K. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3774-6_13 · Cited by 14
Booij, J., Arbizu, J., Darcourt, J., Hesse, S., Nobili, F. M., Payoux, P., Pappatà, S., Tatsch, K., Walker, Z., & Pagani, M. (2013) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-013-2415-x · Cited by 13
The role of PET quantification in neurological imaging: FDG and amyloid imaging in dementia
Herholz, K. (2014) · Clinical and Translational Imaging · doi:10.1007/s40336-014-0073-z · Cited by 13
Self-citationEstimation of brain amyloid accumulation using deep learning in clinical [11C]PiB PET imaging
Ladefoged, C. N., Anderberg, L., Madsen, K., Henriksen, O. M., Hasselbalch, S. G., Andersen, F. L., Højgaard, L., Law, I. G., & Initiative, A. D. N. (2023) · EJNMMI Physics · doi:10.1186/s40658-023-00562-7 · Cited by 10
Thientunyakit, T., Thongpraparn, T., Sethanandha, C., Yamada, T., Kimura, Y., Muangpaisan, W., & Ishii, K. (2021) · Japanese Journal of Radiology · doi:10.1007/s11604-021-01132-6 · Cited by 11
Imago Mundi, Imago AD, Imago ADNI
Villemagne, V. L., Kim, S. Y., Rowe, C. C., & Iwatsubo, T. (2014) · Alzheimer s Research & Therapy · doi:10.1186/s13195-014-0062-5 · Cited by 10
Pilebro, B., Lindqvist, P., Gustafsson, S., Westermark, P., Sundström, T., Antoni, G., Suhr, O. B., & Sørensen, J. (2015) · Orphanet Journal of Rare Diseases · doi:10.1186/1750-1172-10-s1-o15 · Cited by 10
Notzon, S., Alferink, J., & Arolt, V. (2016) · Der Nervenarzt · doi:10.1007/s00115-016-0193-y · Cited by 10
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Wassef, H. R., & Colletti, P. M. (2022) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000004252 · Cited by 7
Molecular Imaging in Sporadic Alzheimer's Disease Populations and Those Genetically at Risk
Nordberg, A. K. (2014) · Neurodegenerative Diseases · doi:10.1159/000356333 · Cited by 7
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Jessen, F., & Dodel, R. C. (2014) · Der Nervenarzt · doi:10.1007/s00115-014-4064-0 · Cited by 7
Zukotynski, K., Black, S. E., Kuo, P. H., Bhan, A., Adamo, S. M., Scott, C. J. M., Lam, B., Masellis, M., Kumar, S., Fischer, C. E., et al. (2021) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000003668 · Cited by 7
Yoon, H., Kim, S.-G., Kim, S. H., Choo, I. H., Park, S. H., Seo, E. H., & Initiative, A. D. N. (2019) · Yonsei Medical Journal · doi:10.3349/ymj.2019.60.10.935 · Cited by 8
Colato, E., Initiative, A. D. N., Chiotis, K., Ferreira, D., Mazrina, M. S., Lemoine, L., Mohanty, R., Westman, E., Nordberg, A. K., & Rodriguez‐Vieitez, E. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210614 · Cited by 7
Self-citationThe use of amyloid imaging in clinical praxis: a critical review
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Self-citationPET and the multitracer concept in the study of neurodegenerative diseases
Engler, H., Damián, A., & Bentancourt, C. (2015) · Dementia & Neuropsychologia · doi:10.1590/1980-57642015dn94000343 · Cited by 4
Clinical Assessment of Amyloid Imaging in Alzheimer's Disease: A Systematic Review of the Literature
Wahlster, P., Niederländer, C., Kriza, C., Schaller, S., & Kolominsky‐Rabas, P. L. (2013) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000351679 · Cited by 8
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Perron, J., & Ko, J. H. (2022) · Applied Sciences · doi:10.3390/app122211463 · Cited by 5
Hong, Z., Xue, S., Yu, J., Calabretta, R., Haberl, D., Jiang, Z., Grünert, S., Beitzke, D., Kammerlander, A. A., Hacker, M., et al. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07308-w · Cited by 5
Towards multicenter β-amyloid PET imaging in mouse models: A triple scanner head-to-head comparison
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Resilienz gegenüber psychischen Störungen im Alter
Fellgiebel, A. (2018) · Der Nervenarzt · doi:10.1007/s00115-018-0544-y · Cited by 6
PET and SPECT Imaging of Neurodegenerative Diseases
Drzezga, A. E., Bischof, G. N., Giehl, K., & van Eimeren, T. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-816386-3.00085-5 · Cited by 4
Self-citationAlmkvist, O., Brüggen, K., & Nordberg, A. K. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201612 · Cited by 4
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Damián, A., Portugal, F., Niell, N., Quagliata, A., Bayardo, K., Alonso, Ó., & Ferrando, R. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.630958 · Cited by 4
Choo, I. H., Chong, A., Chung, J. Y., Ha, J., Choi, Y. Y., & Kim, H. (2022) · Psychiatry Investigation · doi:10.30773/pi.2022.0014 · Cited by 4
Cardiac Amyloidosis: Current Diagnostic Strategies Using Multimodality Imaging
Fukushima, K., Nakano, S., & Matsunari, I. (2020) · Annals of Nuclear Cardiology · doi:10.17996/anc.20-00130 · Cited by 3
Klinisch-validierte molekulare Biomarker neurodegenerativer Demenzerkrankungen
Wiltfang, J. (2014) · Der Nervenarzt · doi:10.1007/s00115-014-4086-7 · Cited by 3
Pérez‐Martínez, G. (2017) · Journal of Nutritional Health & Food Engineering · doi:10.15406/jnhfe.2017.06.00192 · Cited by 2
Prävention im Alter – Was ist gesichert?
Hardt, R., Seibert‐Grafe, M., Fellgiebel, A., Wiechelt, J., Schulz, G., & Thomczyk, S. (2019) · Springer eBooks · doi:10.1007/978-3-662-56788-3_2 · Cited by 2
PET Tracers for Beta-Amyloid and Other Proteinopathies
Nordberg, A. K. (2014) · Springer eBooks · doi:10.1007/978-3-642-42014-6_8 · Cited by 2
Self-citationKepe, V. (2013) · PET Clinics · doi:10.1016/j.cpet.2013.08.002 · Cited by 2
Amyloid imaging in Alzheimer’s disease: a potential new era of personalized medicine?
Leuzy, A., Zimmer, E. R., Gauthier, S. G., & Rosa‐Neto, P. (2014) · Translational Neuroscience · doi:10.2478/s13380-014-0205-y · Cited by 2
Ni, R. (2015) · Figshare · Cited by 2
Update Amyloid-Bildgebung in der Diagnostik der Neurodegeneration
Drzezga, A. E., & Giehl, K. (2022) · Angewandte Nuklearmedizin · doi:10.1055/a-1712-6110 · Cited by 1
Self-citationAlzheimer disease (AD) medical image analysis with convolutional neural networks
Sohail, A., Fiaz, M., Nutini, A., & Iqbal, M. S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-318468-0.00017-9 · Cited by 1
Skeby, K. K. (2016) · Springer theses · doi:10.1007/978-3-319-20040-8_1 · Cited by 1
Dinkelborg, L. M. (2015) · Neurodegenerative Disease Management · doi:10.2217/nmt.15.26 · Cited by 1
Alzheimer’s Disease: A Clinical Perspective
Xu, W., Marseglia, A., Ferrari, C., & Wang, H. (2013) · InTech eBooks · doi:10.5772/54539 · Cited by 2
Early Diagnosis and Risk of Conversion from Presymptomatic Stages
Menéndez‐González, M. (2014) · Springer eBooks · doi:10.1007/978-3-319-07989-9_1 · Cited by 1
Grönholm-Nyman, P. (2014) · International perspectives on aging · doi:10.1007/978-3-319-06650-9_15
Evaluation of cognitive performance within the disease course of Alzheimers
Neuropsychology, M. T. (2013)
Application of Alzheimer Biomarkers in Clinical Practice
Menéndez‐González, M. (2014) · Springer eBooks · doi:10.1007/978-3-319-07989-9_4
Hybridbildgebung in der Neuroradiologie
Schlamann, M. U., Ruhlmann, V., & Quick, H. H. (2015) · DNP - Der Neurologe und Psychiater · doi:10.1007/s15202-015-0726-4
Klinisch-validierte molekulare Biomarker neurodegenerativer Demenzerkrankungen
Psychiatrie, K. F. (2014)
PET Imaging as a Diagnostic Tool in Alzheimer’s Disease
Rinne, J. O. (2016) · Methods in pharmacology and toxicology · doi:10.1007/978-1-4939-3560-4_13
Self-citationDrzezga, A. E. (2016) · Springer eBooks · doi:10.1007/978-3-319-30081-8_26
Self-citationZukotynski, K. (2020) · TSpace (University of Toronto)
On the inhomogeneous magnetised electron gas
Kettenis, M. (2001) · UvA-DARE (University of Amsterdam)
Gutzmann, H., & Rapp, M. A. (2016) · Springer eBooks · doi:10.1007/978-3-642-45028-0_60-1
Gutzmann, H., & Rapp, M. A. (2017) · Springer eBooks · doi:10.1007/978-3-662-49295-6_60
Perani, D. (2017) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.726105916.793537459
Self-citationNordberg, A. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00071-3
Self-citationOliveira, F. P. M., & Costa, D. C. (2023) · European Radiology · doi:10.1007/s00330-023-09637-6
Role of fluid biomarkers in the early diagnosis of Alzheimer’s disease
Vo, G. V., Nguyen, M. N., & Toi, V. V. (2021) · Science & Technology Development Journal - Health Sciences · doi:10.32508/stdjhs.v2i1.458
Recent progress in the pathology, diagnosis, and treatment of Alzheimer’s disease
Yu, X. (2023) · Theoretical and Natural Science · doi:10.54254/2753-8818/21/20230827
Jiménez-Bonilla, J. F., de Arcocha-Torres, M., Lage, C., López‐García, S., Martínez‐Rodríguez, I., Rodríguez‐Rodríguez, E., Sánchez‐Juan, P., & Quirce, R. (2024) · Research Square · doi:10.21203/rs.3.rs-4000629/v1
Evaluation of cognitive performance within the disease course of Alzheimer´s
Rettenwander, L. A. E. (2014) · Utrecht University Repository (Utrecht University)
赤松, 剛. (2015) · Institutional Repositories DataBase (IRDB) · doi:10.15017/1654738
Ladefoged, C. N., Anderberg, L., Madsen, K., Henriksen, O. M., Hasselbalch, S., Andersen, F. L., Højgaard, L., & Law, I. (2023) · Figshare · doi:10.6084/m9.figshare.23689667
Luckett, E. S., Zapater‐Fajarí, M., Almkvist, O., Johansson, C., Chiotis, K., Bucci, M., Wall, A. E., Ashton, N. J., Blennow, K., Zetterberg, H., et al. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-03829-6 · Cited by 1
Self-citationThe new era of Alzheimer’s disease: a possible burden for radiologists?
Anzalone, N., Rimini, M. L. D., & Gandolfo, N. G. (2026) · La radiologia medica · doi:10.1007/s11547-026-02194-0
Hanbouch, L. (2017) · theses.fr (ABES) · doi:10.70675/c4d2c894zf69dz4ae4z9ab1zf4135d0efa12
Liy-Salmerón, G., & Azrad, S. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-408139-0.00010-9
Metadata
- Authors
- Nordberg, A., Carter, S. F., Rinne, J., Drzezga, A., Brooks, D. J., Vandenberghe, R., Perani, D., Forsberg, A., Långström, B., Scheinin, N., Karrasch, M., Någren, K., Grimmer, T., Miederer, I., Edison, P., Okello, A., Van Laere, K., Nelissen, N., Vandenbulcke, M., Garibotto, V., Almkvist, O., Kalbe, E., Hinz, R., & Herholz, K.
- Affiliations
- Karolinska University Hospital (SE)
- Karolinska Institutet (SE)
- University of Turku (FI)
- Technical University of Munich (DE)
- Imperial College London (GB)
- KU Leuven (BE)
- Vita-Salute San Raffaele University (IT)
- Uppsala University (SE)
- University of Vechta (DE)
- University of Manchester (GB)
- Corresponding author
- Nordberg, Agneta
- ORCID
- Nordberg, Agneta
- Carter, Stephen F.
- Rinne, Juha
- Drzezga, Alexander
- Brooks, David J.
- Vandenberghe, Rik
- Perani, Daniela
- Forsberg, Anton
- Scheinin, Noora
- Karrasch, Mira
- Någren, Kjell
- Grimmer, Timo
- Miederer, Isabelle
- Edison, Paul
- Nelissen, Natalie
- Vandenbulcke, Mathieu
- Garibotto, Valentina
- Almkvist, Ove
- Kalbe, Elke
- Hinz, Rainer
- Herholz, Karl
- Venue
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2012-09-08
- Volume
- 40
- Issue
- 1
- Pages
- 104-114
- ISSN
- 1619-7070 (ISSN-L)
- 1619-7089
- PMC
- PMC3510420
- OpenAlex
- W2116591217
- Cited by
- 186
- References
- 46
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 5 · 2022: 11 · 2021: 13 · 2020: 9 · 2019: 10 · 2018: 9 · 2017: 14 · 2016: 25 · 2015: 27 · 2014: 27 · 2013: 24 · 2012: 2
- FWCI
- 18.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- European Journal of Nuclear Medicine and Molecular Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
- Lirias (KU Leuven) (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Åbo Akademi University Research Portal (submittedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- Alzheimer's Research Trust
- Deutsche Forschungsgemeinschaft (DR 445/3-1, DR 445/4-1)
- Academy of Finland
- Belgian Federal Science Policy Office (IUAP P6/29)
- Fonds Wetenschappelijk Onderzoek (05/014, G0668.07, P6/29, G0076.02)
- Karolinska Institutet (05817)
- Stockholms Läns Landsting
- Vetenskapsrådet (05817)
- Technische Universität München
- Swedish Brain Power (05817)
- Universität zu Köln
- Turun Yliopistollinen Keskussairaala
- Medical Research Council
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- Alzheimer's disease, amyloid PET, Pittsburgh compound B, mild cognitive impairment, prodromal Alzheimer's disease, APOE ε4, conversion to Alzheimer's disease, memory impairment, negative predictive value, amyloid biomarkers, longitudinal follow-up
- MeSH terms
- Aged, Alzheimer Disease, Amyloid, Aniline Compounds, Apolipoproteins E, Brain, Brain Chemistry, Europe, Female, Humans, Male, Middle Aged, Thiazoles, Case-Control Studies, Radiopharmaceuticals, Positron-Emission Tomography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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