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Imaging and cerebrospinal fluid biomarkers in early preclinical alzheimer disease
Open accessAbstract
Objective: Deposition of amyloid β (Aβ)‐containing plaques as evidenced by amyloid imaging and cerebrospinal fluid (CSF) Aβ1–42 (Aβ42) is an early indicator of preclinical Alzheimer disease (AD). To better understand their relationship during the earliest preclinical stages, we investigated baseline CSF markers in cognitively normal individuals at different stages of amyloid deposition defined by longitudinal amyloid imaging with Pittsburgh compound B (PIB): (1) PIB‐negative at baseline and follow‐up (PIB − ; normal), (2) PIB‐negative at baseline but PIB‐positive at follow‐up (PIB converters; early preclinical AD), and (3) PIB‐positive at baseline and follow‐up (PIB + ; preclinical AD). Methods: Cognitively normal individuals (n = 164) who had undergone baseline PIB scan and CSF collection within 1 year of each other and at least 1 additional PIB follow‐up were included. Amyloid status was defined dichotomously using an a priori mean cortical cutoff. Results: PIB converters (n = 20) at baseline exhibited significantly lower CSF Aβ42 compared to those who remained PIB‐negative (n = 123), but higher compared to the PIB + group (n = 21). A robust negative correlation ( r = −0.879, p = 0.0001) between CSF Aβ42 and absolute (but subthreshold) PIB binding was observed during this early preclinical stage. The negative correlation was not as strong once individuals were PIB‐positive ( r = −0.456, p = 0.038), and there was no correlation in the stable PIB − group ( p = 0.905) or in the group (n = 10) with early symptomatic AD ( p = 0.537). Interpretation: CSF Aβ42 levels are tightly coupled with cortical amyloid load in the earliest stages of preclinical AD, and begin to decrease dramatically prior to the point when an abnormal threshold of cortical accumulation is detected with amyloid imaging. Ann Neurol 2016;80:379–387
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
Self-citationImaging 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
Jack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70291-0 · Cited by 4,396
Automatically Parcellating the Human Cerebral Cortex
Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208
Buckner, R. L., Head, D. M., Parker, J. B., Fotenos, A. F., Marcus, D. S., Morris, J. C., & Snyder, A. Z. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.06.018 · Cited by 1,325
Self-citationInverse relation between in vivo amyloid imaging load and cerebrospinal fluid Aβ 42 in humans
Fagan, A. M., Mintun, M. A., Mach, R. H., Lee, S., Dence, C. S., Shah, A. R., LaRossa, G. N., Spinner, M. L., Klunk, W. E., Mathis, C. A., et al. (2006) · Annals of Neurology · doi:10.1002/ana.20730 · Cited by 1,246
Self-citation[ 11 C]PIB in a nondemented population: Potential antecedent marker of Alzheimer disease
Mintun, M. A., LaRossa, G. N., Sheline, Y. I., Dence, C. S., Lee, S. Y., Mach, R. H., Klunk, W. E., Mathis, C. A., DeKosky, S. T., & Morris, J. C. (2006) · Neurology · doi:10.1212/01.wnl.0000228230.26044.a4 · Cited by 1,036
Self-citationLongitudinal assessment of Aβ and cognition in aging and Alzheimer disease
Villemagne, V. L., Pike, K. E., Chételat, G., Ellis, K. A., Mulligan, R. S., Bourgeat, P., Ackermann, U., Jones, G., Szoeke, C., Salvado, O., et al. (2011) · Annals of Neurology · doi:10.1002/ana.22248 · Cited by 833
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Price, J. L., McKeel, D. W., Buckles, V. D., Roe, C. M., Xiong, C., Grundman, M., Hansen, L. A., Petersen, R. C., Parisi, J. E., Dickson, D. W., et al. (2009) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2009.04.002 · Cited by 661
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Research works printed but not linked: 1
- Goodglass H, 1983, Boston diagnostic aphasia examination booklet
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Ali, M., Sung, Y. J., Wang, F., Fernández, M. V., Morris, J. C., Fagan, A. M., Blennow, K., Zetterberg, H., Heslegrave, A., Johansson, P., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0267298 · Cited by 23
Self-citationXu, L., Zhao, Y., Choi, S., Li, M., Schilling, K. G., Zu, Z., Rogers, B. P., Ding, Z., Anderson, A. W., Landman, B. A., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14334 · Cited by 20
Fan, D., Jian, J.-M., Huang, S., Li, W., Shen, Y., Wang, Z., Zeng, G., Yi, X., Jin, W., Liu, Y.-H., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02016-7 · Cited by 20
Castor, K., Shenoi, S., Edminster, S. P., Tran, T. T., King, K. S., Chui, H. C., Pogoda, J. M., Fonteh, A. N., & Harrington, M. G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231765 · Cited by 22
Parasoglou, P., Osorio, R. S., Khegai, O., Kovbasyuk, Z., Miller, M. D., Ho, A., Dehkharghani, S., Wısnıewskı, T., Convit, A. J., Mosconi, L., et al. (2022) · Neuroimage Reports · doi:10.1016/j.ynirp.2022.100121 · Cited by 18
Nallapu, B. T., Initiative, T. A. D. N., Petersen, K. K., Lipton, R. B., Davatzikos, C. A., & Ezzati, A. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-230620 · Cited by 17
Day, G. S., Cruchaga, C., Wingo, T. S., Schindler, S. E., Coble, D. W., & Morris, J. C. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.13491 · Cited by 17
Self-citationKoops, E. A., Dutta, J., Hanseeuw, B. J., Becker, J. A., Egroo, M. V., Prokopiou, P. C., Price, J. C., Arnold, S. E., Sperling, R. A., Johnson, K. A., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14385 · Cited by 16
Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study
Kruggel, F., Masaki, F., & Solodkin, A. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.12.020 · Cited by 16
Lin, R.-R., Xue, Y., Li, X., Chen, Y.-H., Tao, Q., & Wu, Z. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.718959 · Cited by 15
Hartz, S. M., Mozersky, J. T., Schindler, S. E., Linnenbringer, E. L., Wang, J., Gordon, B. A., Raji, C. A., Moulder, K. L., West, T., Benzinger, T. L. S., et al. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12781 · Cited by 10
Self-citationEscarcega, R. D., J., V. K. M., Kyriakopoulos, V. E., Ortiz, G. J., Gusdon, A. M., Fan, H., Peesh, P., Conesa, M. P. B., Colpo, G. D., Ahnstedt, H. W., et al. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106747 · Cited by 11
Echeveste, B., Prieto, E., Guillén, E. F., Jimenez, A., Montoya‐Murillo, G., Villino‐Rodríguez, R. A., Riverol, M., & Arbizu, J. (2025) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-025-07275-2 · Cited by 10
Petersen, K. K., Ezzati, A., Lipton, R. B., Gordon, B. A., Hassenstab, J. J., Morris, J. C., & Grober, E. H. (2022) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2022.98 · Cited by 8
Self-citationCacciottolo, M., Morgan, T. E., & Finch, C. E. (2016) · Journal of Alzheimer’s Disease & Parkinsonism · doi:10.4172/2161-0460.1000287 · Cited by 7
Brain Biomarkers in Familial Alzheimer’s Disease Mouse Models
Hirshler, Y., Venkatasubramanian, P. N., Apolinario, J., Bonds, J. A., Wyrwicz, A. M., & Lazarov, O. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-170269 · Cited by 6
Manca, C., Jonveaux, T., Roch, V., Marie, P., Karcher, G., Lamiral, Z., Malaplate, C., & Verger, A. (2019) · Journal of Neurology · doi:10.1007/s00415-019-09315-y · Cited by 7
Sacchi, L., Carandini, T., Fumagalli, G., Pietroboni, A. M., Contarino, V. E., Siggillino, S., Arcaro, M., Fenoglio, C., Zito, F., Marotta, G., et al. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210593 · Cited by 6
Neural network training with highly incomplete medical datasets
Chang, Y.-W., Natali, L., Jamialahmadi, O., Romeo, S., Pereira, J. B., & Volpe, G. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac7b69 · Cited by 6
da Silva, A. F., Gomes, A., Gonçalves, L., Fernandes, A. M. A., & Almeida, A. J. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17020141 · Cited by 4
Xu, C., Xu, E., Xiao, Y., Yang, D., Guorong Wu, A., & Chen, M. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.142887 · Cited by 3
Tau positron emission tomography imaging in C9orf72 repeat expansion carriers
Ly, C. V., Koenig, L., Christensen, J. J., Gordon, B. A., Beaumont, H., Dahiya, S. M., Chen, Y.-H., Su, Y., Nelson, B., Jockel, J., et al. (2019) · European Journal of Neurology · doi:10.1111/ene.13940 · Cited by 4
Self-citationBiomarqueurs du liquide cérébrospinal dans la maladie d’Alzheimer
Hugon, J., Dumurgier, J., Cognat, E., & Paquet, C. (2018) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/s0001-4079(19)30359-0 · Cited by 2
Pronko, P. S., Zimatkin, S. M., & Kuz'mich, A. B. (1994) · PubMed · Cited by 2
Millar, P. R., Gordon, B. A., Luckett, P. H., Benzinger, T. L. S., Cruchaga, C., Fagan, A. M., Hassenstab, J. J., Perrin, R. J., Schindler, S. E., Allegri, R., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.25.505251 · Cited by 2
Self-citationCerebrospinal Fluid Biomarkers of Alzheimer Disease
Васильевна, Н. К., Shpilyukova, Y. A., Шабалина, А. А., Fedotova, E. Y., & Illarioshkin, S. N. (2025) · Annals of Clinical and Experimental Neurology · doi:10.17816/acen.1185 · Cited by 1
Steenberghen, H., Beuckeleer, S. D., Hellings, N., Somers, V. A., Breedam, E. V., Ponsaerts, P., Nuydens, R., Maurin, H., Larsen, P. H., & Vos, W. H. D. (2024) · Cytometry Part A · doi:10.1002/cyto.a.24905 · Cited by 1
Neural Network Training with Highly Incomplete DatasetsPreprint
Chang, Y., Natali, L., Jamialahmadi, O., Romeo, S., Pereira, J. B., & Volpe, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00429 · Cited by 1
Jett, S., Dyke, J. P., Andy, C., Schelbaum, E., Jang, G., Boneu, C., Pahlajani, S. S., Díaz, I., Brinton, R. D., & Mosconi, L. (2022) · Research Square · doi:10.21203/rs.3.rs-1723651/v1 · Cited by 1
BrainVAE: Exploring the role of white matter BOLD in preclinical Alzheimer’s disease classificationPreprint
Li, Y., Xu, L., Zuo, L., Chang, Y., Ding, Z., Anderson, A. W., Schilling, K. G., Gore, J. C., Gao, Y., & Initiative, T. A. D. N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.07.652697 · Cited by 1
Azaraein, M. H., Zarrazvand, E., Heydari, F., Asimi, N., Ramezannezhad, E., Rahimipour, D., Janeshin, K., Mahdian, A., Sichani, M. F., Farhadi, S., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7327653/v1 · Cited by 1
Major, C. K., & Okhravi, H. R. (2021) · Cureus · doi:10.7759/cureus.13481
Jardel, A., Hopes, L., Malaplate, C., Roch, V., Manca, C., Jonveaux, T., & Verger, A. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201435
Neuroimaging and Cerebrospinal Fluid Biomarkers of Alzheimer’s Disease
Gordon, B. A., Vos, S. J. B., & Fagan, A. M. (2017) · Oxford University Press eBooks · doi:10.1093/med/9780190681425.003.0052
Self-citationFaculty Opinions recommendation of Persistent metabolic youth in the aging female brain.
Nelson, R. J., & Weil, Z. M. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735000495.793556909
Faculty Opinions recommendation of Persistent metabolic youth in the aging female brain.
Tower, J. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735000495.793556694
PET imagining in Alzheimer’s disease
Korol, P. O., Lukashenko, M. S., Щербіна, О. В., Murashko, V. O., & Ivchuk, V. P. (2023) · Ukrainian Journal of Military Medicine · doi:10.46847/ujmm.2023.4(4)-045
Fast Declining Prediction in Alzheimer’s Disease from Early Clinical Assessment
Álvarez‐Sánchez, L., Peretó, M., García, L., Balaguer, Á., Peña‐Bautista, C., Ferré‐González, L., Baquero, M., & Cháfer-Pericas, C. (2024) · Current Neuropharmacology · doi:10.2174/011570159x332930240925095423
New Research on Biomarkers in Alzheimer’s Continuum
Vecchio, I., & Colica, C. (2025) · Reviews on Recent Clinical Trials · doi:10.2174/0115748871331138250114052615 · Cited by 2
Metadata
- Authors
- Vlassenko, A. G., McCue, L., Jasielec, M. S., Su, Y., Gordon, B. A., Xiong, C., Holtzman, D. M., Benzinger, T. L. S., Morris, J. C., & Fagan, A. M.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Vlassenko, Andrei G.
- ORCID
- Venue
- Annals of Neurology
- Publisher
- Wiley
- Published
- 2016-07-25
- Volume
- 80
- Issue
- 3
- Pages
- 379-387
- ISSN
- 0364-5134 (ISSN-L)
- 1531-8249
- PMC
- PMC5016232
- OpenAlex
- W2492380270
- Cited by
- 106
- References
- 35
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 7 · 2023: 4 · 2022: 14 · 2021: 12 · 2020: 9 · 2019: 16 · 2018: 15 · 2017: 12 · 2016: 3
- FWCI
- 13.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annals of Neurology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (P50 AG05681, P01 AG026276, P01 AG03991)
- 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 › Clinical Neurology › Cerebrospinal fluid and hydrocephalus
- Keywords
- preclinical Alzheimer's disease, amyloid-beta 42, cerebrospinal fluid biomarkers, amyloid imaging, Pittsburgh compound B, amyloid plaques, cognitively normal individuals, amyloid PET
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Aniline Compounds, Female, Follow-Up Studies, Humans, Male, Middle Aged, Peptide Fragments, Thiazoles, Amyloid beta-Peptides, Disease Progression, Early Diagnosis, Prodromal Symptoms
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