The DOI matches its Crossref record; 565 of the 565 references it cites were found in a registry or an index.
2014 Update of the Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception
Open accessAbstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“SNPs in and around candidate genes including APOE, BIN1, SORL1, and CR1 have been associated with traits including medial temporal lobe atrophy and cortical thickness [225–228]”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [225]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
1 retracted
Integrative genomics identifies APOE ε4 effectors in Alzheimer's disease
Rhinn, H., Fujita, R., Qiang, L., Cheng, R., Lee, J. H., & Abeliovich, A. (2013) · Nature · doi:10.1038/nature12415 · Cited by 151
RetractedMcKhann, 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
Self-citationThe Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics
Hardy, J., & Selkoe, D. J. (2002) · Science · doi:10.1126/science.1072994 · Cited by 13,864
Amyloid oligomers exacerbate tau pathology in a mouse model of tauopathy
Selenica, M.-L. B., Brownlow, M., Jimenez, J. P., Lee, D. C., Pena, G., Dickey, C. A., Gordon, M. N., & Morgan, D. (2013) · Neurodegenerative Diseases · doi:10.1159/000337230 · Cited by 10,359
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · cited 1×
“Diagnosis of MCI using National Institute on AgingAlzheimer’s Association criteria The NIA-AA criteria for MCI due to AD recently incorporated the use of biomarkers for both amyloid deposition (CSF Ab42, (11)C-PiB-PET) and neuronal injury (CSF tau, hippocampal and whole brain atrophy on MRI, FDG-PET) in addition to cl…”
MethodSelf-citationSperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155
Self-citationHypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., & Trojanowski, J. Q. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(09)70299-6 · Cited by 4,806
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
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
Self-citationda Luz, A. C. D. D. S., Dias, G. P., Bevilaqua, M. C. D. N., Cocks, G. D., Gardino, P. F., Thuret, S., & Nardi, A. E. (2013) · Neuropsychobiology · doi:10.1159/000353269 · Cited by 4,015
Research works printed but not linked: 14
- Trojanowski J, 2004, Searching for the biomarkers of Alzheimer's, Pract Neurol, 3, 30
- Mueller SG, 2006, Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative, Cogn Dement, 5, 56
- Cruchaga C, 2009, SNPs in the regulatory subunit of calcineurin are associated with CSF tau protein levels, brain mRNA levels, Alzheimers Dement, 5, P471
- LiY WangY WuG ShiF ZhouL LinW et al.Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features.Neurobiol Aging (in press).
- Thambisetty M, 2012, Effect of Complement CR1 on Brain Amyloid Burden During Aging and Its Modification byAPOEGenotype, Biol Psychiatry, 73, 334
- Weiner MW, 2012, The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception, Alzheimers Dement, 8, S1
- 10.1016/j.bbadis.2011.09.008 · DOI
- 10.1016/j.neuroimage.2009.05.056 · DOI
- 10.3389/fnins.2012.00115 · DOI
- 10.1007/s11682-012-9184-1 · DOI
- 10.1007/s11682-012-9196-x · DOI
- 10.3389/fgene.2011.00073 · DOI
- 10.1016/j.neuroimage.2011.03.077 · DOI
- 10.2174/156720512802455331 · DOI
Cited by
Sorted by citations
Items 301–348 of 348
Saeed, U. (2018) · TSpace (University of Toronto)
Lessons Learned From Public Private Partnerships and Consortia: The ADNI Paradigm
Liu, E. (2019) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-803161-2.00017-5
Motter, J. N. (2019) · CUNY Academic Works (City University of New York)
Zhao, H., Ge, Y., & Wang, Z. (2018) · PubMed Central · doi:10.7507/1001-5515.201703002
Degenerative Diseases of the CNS
Capizzano, A. A., Moritani, T., & Capizzano, J. N. (2021) · Springer eBooks · doi:10.1007/978-3-030-62120-9_14
Information Flow Pattern in Early Mild Cognitive Impairment Patients
He, H., Ding, S., Jiang, C., Wang, Y.-Y., Luo, Q., & Wang, Y. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.706631
Self-citationNadeem, M., Zhang, W., Aslam, S., Ali, L., & Majid, A. (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit241047
Human genetics: Establishment of cause-and-effect relationships
Saido, T. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28979-8.00013-9
Tian, T., Guo, Y., & Sun, J. (2024) · Springer eBooks · doi:10.1007/978-3-031-65937-9_4
Fusion of autoencoder model for gene predication and RNA disease association
Sivasankari, S. A., Keshamma, E., Soni, M., Kumar, A., Shekhar, R., & Pande, S. D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30080-6.00014-6
A Survey on Alzheimer's from 2D MRIs Using Deep Learning Model
Suthar, K., & Vekariya, D. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933205
Roberman, J. L., Stamey, J. D., Holt, M. M., & Initiative, A. D. N. (2025) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2025.2487091
Self-citationWei, M., Wang, L., Yu, X., Hu, W., Wang, M., Zhang, Q., Guo, T., Zhong, J., Li, C., Jiang, J., et al. (2025) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70264
Zhang, C., Song, C., Li, X., Duan, Y., Liu, W., & Lu, Z. (2025) · Neuroreport · doi:10.1097/wnr.0000000000002218
Xie, L., Das, S. R., Wisse, L. E. M., Ittyerah, R., de Florès, R., Shaw, L. M., Yushkevich, P. A., & Wolk, D. A. (2023) · Figshare · doi:10.6084/m9.figshare.22625699
Self-citationPeng, D., Xu, S., Zou, T., Wang, Y., Ouyang, W., Zhang, Y., Dong, C., Li, D., Guo, J., Shen, Q., et al. (2024) · Figshare · doi:10.6084/m9.figshare.24630451
Peng, D., Xu, S., Zou, T., Wang, Y., Ouyang, W., Zhang, Y., Dong, C., Li, D., Guo, J., Shen, Q., et al. (2023) · Figshare · doi:10.6084/m9.figshare.24630304
Zhao, L.-J., Wang, Z.-T., Ma, Y.-H., Zhang, W., Dong, Q., Yu, J.-T., & Tan, L. (2021) · Figshare · doi:10.6084/m9.figshare.16755740
Early diagnosis of Alzheimer’s disease by AI: a hybrid approach based on U-Net and MobileNetV2
Keita, M., & Souki, O. (2025) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa66935.2025.11315396
Hong, Y., Michel, C. B., Gassner, G. M., Accorsi, C., Ances, B. M., Antonucci, L. M., Arnold, S. E., Babulal, G. M., Bookheimer, S. Y., Buckner, R. L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.21.700887
Wang, W., Xiao, L., Li, R., Luo, S., & Initiative, A. D. N. (2026) · Statistics in Medicine · doi:10.1002/sim.70442
Self-citationSánchez‐Soliño, O., Boroje, I. J., Yue, E. X., & Vinikoor-Imler, L. C. (2026) · Journal of Aging Research and Lifestyle · doi:10.1016/j.jarlif.2026.100067
Nassanga, R., Michael, K. G., Nakasujja, N., Sajatovic, M., Jones, S. E., & Kaddumukasa, M. (2026) · BMC Medical Education · doi:10.1186/s12909-026-08997-z
Heterogeneity in diagnostic trajectories of ADNI participants
Winalai, C., Mogharbel, N., & Bicout, D. (2026) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2026.110748
Xiong, X., Wang, X., Zhu, C., He, J., & Initiative, A. D. N. (2026) · PLoS ONE · doi:10.1371/journal.pone.0342549
Self-citationMulti-scale and multimodal imaging biomarkers for the early detection of Alzheimer’s disease
Hett, K. (2019) · theses.fr (ABES) · doi:10.70675/0d4a3118z347az4e13z812cz9ad7566ef5ba
Sanches, M. C. P. (2019) · theses.fr (ABES) · doi:10.70675/23d3688fza2a5z4b5czace8z77e89e074aaf
Early Detection of Alzheimer’s Disease using Transfer Learning and Explainable AI
Mounika, M., Priyanka, B., Suresh, P., Nithin, P., & Kiran, P. H. (2026) · IEEE International Conference for Convergence in Computing Technology (I3CTCON) · doi:10.1109/i3ctcon68242.2026.11507550
Alzheimer's Disease Diagnosis via Broad Learning with Pyramid Residual Attention from FDG-PET Images
Qiu, Y., & Duan, J. (2026) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1109/cacml68972.2026.11507110
Regularized Tensor Quantile Regression With Applications to Neuroimaging Data Analysis
Pietrosanu, M., Yu, D., Mizera, I., Jiang, B., & Kong, L. (2026) · Statistics in Medicine · doi:10.1002/sim.70582
Gowda, S. L. H., Yadahalli, S. V., Mirjanik, S., Muskan, S., & Pramee, L. (2026) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom70271.2026.11526237
Visual MRI ratings and automated volumetry in Ugandan dementia care
Nassanga, R., Nakasujja, N., Kaddumukasa, M., Sajatovic, M., Mubuuke, A. G., Basiimwa, R. C., Kawooya, M. G., Tumwine, J. K., Lwere, K., Gumikiriza‐Onoria, J. L., et al. (2026) · Academia Mental Health and Well-Being · doi:10.20935/mhealthwellb8385
Single-index semiparametric transformation cure models with interval-censored data
Huang, X., Yu, T., & Liu, X. (2026) · Statistics and Computing · doi:10.1007/s11222-026-10908-7
Wang, M., Qian, Y., Huang, E., Chu, C., Gao, T., Chen, S., Zhao, N., Luo, C., Liu, Y., Zheng, X., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1911726
Mimenza-Alvarado, A. J., Aguilar-Navarro, S. G., Hernandez, M. E., & López-Teros, M. T. (2026) · Revista de investigaci�n Cl�nica · doi:10.1016/j.ric.2026.100054
Bao, R., Zhang, S., Wang, F., Cheng, W., Zhang, T., & Ding, W. (2026) · Research Square · doi:10.21203/rs.3.rs-10895317/v1
Khalsa, S. S., Barch, D. M., Brady, L. S., Buracchio, T., Carlezon, W. A., Chatham, C., Drevets, W. C., Epperson, C. N., Etkin, A., Fischer, B. A., et al. (2026) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-026-00069-w
Hong, Y., Michel, C. B., Gassner, G. M., Zhang, Y., Accorsi, C., Ances, B. M., Antonucci, L. M., Arnold, S. E., Babulal, G. M., Bookheimer, S. Y., et al. (2026) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2026.09.007 · Cited by 1
CHENG, W. K. W. (2026) · Research Square · doi:10.21203/rs.3.rs-10884877/v3
Mouiha, A., Taiebine, M., & Hilaly, J. E. (2026) · Journal of Education and Health Promotion · doi:10.4103/jehp.jehp_1279_25
Zhao, J., Nie, C., Gao, R., Hu, Z., & Lin, Y. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1964029
Patel, P. R., Arumugam, P., & Mohan, D. B. (2026) · doi:10.1109/icstem68144.2026.11735485
Metadata
- Authors
- Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., Luthman, J., Morris, J. C., Petersen, R. C., Saykin, A. J., Shaw, L., Shen, L., Schwarz, A., Toga, A. W., Trojanowski, J. Q., & Initiative, A. D. N.
- Affiliations
- San Francisco VA Medical Center (US)
- University of California San Diego (US)
- University of California, Davis (US)
- Washington University in St. Louis (US)
- Biogen (United States) (US)
- Brigham and Women's Hospital (US)
- Mayo Clinic (US)
- University of California, Berkeley (US)
- Eisai (United States) (US)
- Indiana University Indianapolis (US)
- University of Pennsylvania (US)
- Eli Lilly (United States) (US)
- University of Southern California (US)
- Corresponding author
- Weiner, Michael W.
- ORCID
- Venue
- Alzheimer's & Dementia
- Publisher
- Wiley
- Published
- 2015-06
- Volume
- 11
- Issue
- 6
- Pages
- e1-120
- ISSN
- 1552-5260 (ISSN-L)
- 1552-5279
- PMC
- PMC5469297
- OpenAlex
- W1797408175
- Cited by
- 348
- References
- 565
- Citations by year
- 2026: 27 · 2025: 22 · 2024: 28 · 2023: 18 · 2022: 18 · 2021: 43 · 2020: 28 · 2019: 38 · 2018: 36 · 2017: 45 · 2016: 33 · 2015: 11
- FWCI
- 5.74
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Alzheimer s & Dementia (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion, other-oa)
- IUScholarWorks (Indiana University) (submittedVersion)
- Funders
- Eli Lilly and Company
- Pfizer
- Biogen
- Synarc
- TauRx Pharmaceuticals
- Bayer HealthCare
- Servier
- Ipsen
- National Institutes of Health (5u01ag024904-10)
- National Institute on Aging (U01-AG-024904, P30AG010129, P30AG010133, R01AG012975, R01AG030048, P30AG036468, P30AG010124, P01AG009215, P01AG017586, U01AG010483, U01AG029213, P50AG005681, R01AG016381, P01AG003991, R01AG019771, RC2AG036535, P01AG019724)
- National Cancer Institute (P30CA093373)
- National Institute of General Medical Sciences (R01GM088336)
- National Institute of Neurological Disorders and Stroke (P50NS053488)
- National Center for Research Resources (UL1RR024146, R25RR026008)
- 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 › Oncology › Cancer-related cognitive impairment studies
- Keywords
- Alzheimer's disease, Alzheimer's Disease Neuroimaging Initiative
- MeSH terms
- Alzheimer Disease, Brain, Humans, Radionuclide Imaging, Multicenter Studies as Topic, Biomarkers, Nootropic Agents, Early Diagnosis
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review