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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.
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“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]
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Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease
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Disease progression meta‐analysis model in Alzheimer's disease
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Self-citationFactors affecting Aβ plasma levels and their utility as biomarkers in ADNI
Toledo, J. B., Vanderstichele, H., Figurski, M., Aisen, P. S. S., Petersen, R. C., Weiner, M. W., Jack, C. R., Jagust, W., DeCarli, C. S., Toga, A. W., et al. (2011) · Acta Neuropathologica · doi:10.1007/s00401-011-0861-8 · Cited by 182
Self-citationLee, G. J., Lu, P. H., Hua, X., Lee, S., Wu, S., Nguyen, K., Teng, E., Leow, A., Jack, C. R., Toga, A. W., et al. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.12.024 · Cited by 182
Self-citationBrain MAPS: An automated, accurate and robust brain extraction technique using a template library
Leung, K. K., Barnes, J., Marc Modat, A., Ridgway, G. R., Bartlett, J. W., Fox, N. C., & Sebastien Ourselin, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.067 · Cited by 181
Longitudinal change in CSF Tau and Aβ biomarkers for up to 48 months in ADNI
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Self-citationLeow, A. D., Yanovsky, I., Parikshak, N., Hua, X., Lee, S., Toga, A. W., Jack, C. R., Bernstein, M. A., Britson, P. J., Gunter, J. L., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.004 · Cited by 180
Self-citationVillemagne, V. L., Ataka, S., Mizuno, T., Brooks, W. S., Wada, Y., Kondo, M., Jones, G. R., Watanabe, Y., Mulligan, R. S., Nakagawa, M., et al. (2009) · Archives of Neurology · doi:10.1001/archneurol.2009.285 · Cited by 176
Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers
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Self-citationHua, X., Leow, A., Lee, S., Klunder, A. D., Toga, A. W., Leporé, N., Chou, Y.-Y., Brun, C., Chiang, M. Y.-H., & Marina Barysheva, A. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.02.010 · Cited by 175
Self-citationNMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease
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Self-citationMeasurement of MRI scanner performance with the ADNI phantom
Gunter, J. L., Bernstein, M. A., Borowski, B., Ward, C. P., Britson, P. J., Felmlee, J. P., Schuff, N. W., Weiner, M. W., & Jack, C. R. (2009) · Medical Physics · doi:10.1118/1.3116776 · Cited by 172
Self-citationAutomatic quality assessment in structural brain magnetic resonance imaging
Mortamet, B., Bernstein, M. A., Jack, C. R., Gunter, J. L., Ward, C. P., Britson, P. J., Meuli, R. A., Thiran, J., & Krüger, G. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21992 · Cited by 171
Self-citationLocally linear embedding (LLE) for MRI based Alzheimer's disease classification
Liu, X., Tosun, D., Weiner, M. W., & Schuff, N. W. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.033 · Cited by 171
Self-citationClinical and multimodal biomarker correlates of ADNI neuropathological findings
Toledo, J. B., Cairns, N. J., Da, X., Chen, K., Carter, D. R., Fleisher, A., Householder, E., Ayutyanont, N., Roontiva, A., Bauer, R. J., III, et al. (2013) · Acta Neuropathologica Communications · doi:10.1186/2051-5960-1-65 · Cited by 171
Self-citationCerebral atrophy in mild cognitive impairment and Alzheimer disease
Leung, K. K., Bartlett, J. W., Barnes, J., Manning, E. N., Sebastien Ourselin, A., Fox, N. C., & Initiative, A. D. N. (2013) · Neurology · doi:10.1212/wnl.0b013e318281ccd3 · Cited by 171
Self-citationA non-local approach for image super-resolution using intermodality priors☆
Rousseau, F. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.04.005 · Cited by 168
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Characterizing Alzheimer's disease using a hypometabolic convergence index
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Self-citationKruggel, F., Turner, J. A., & Muftuler, L. T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.006 · Cited by 165
Cognitive reserve and Alzheimer's disease biomarkers are independent determinants of cognition
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Self-citationBiological markers of amyloid β-related mechanisms in Alzheimer's disease
Hampel, H. J., Shen, Y., Walsh, D. M., Aisen, P. S. S., Shaw, L. M., Zetterberg, H., Trojanowski, J. Q., & Blennow, K. (2009) · Experimental Neurology · doi:10.1016/j.expneurol.2009.09.024 · Cited by 162
Self-citationLCC-Demons: A robust and accurate symmetric diffeomorphic registration algorithm
Lorenzi, M., Ayache, N., Frisoni, G. B., & Pennec, X. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.114 · Cited by 160
Dickerson, B. C. (2013) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2013.00055 · Cited by 160
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Larger temporal volume in elderly with high versus low beta-amyloid deposition
Chételat, G., Villemagne, V. L., Pike, K. E., Baron, J.-C., Bourgeat, P., Jones, G., Faux, N. G., Ellis, K. A., Salvado, O., Szoeke, C., et al. (2010) · Brain · doi:10.1093/brain/awq187 · Cited by 159
Multi-modal imaging predicts memory performance in normal aging and cognitive decline
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Leung, K. K., Clarkson, M. J., Bartlett, J. W., Clegg, S., Jack, C. R., Weiner, M. W., Fox, N. C., & Sebastien Ourselin, A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.059 · Cited by 158
Self-citationFurney, S. J., Simmons, A., Breen, G., Pedroso, I., Lunnon, K., Proitsi, P., Hodges, A., Powell, J., Wahlund, L.-O., Kloszewska, I., et al. (2011) · Molecular Psychiatry · doi:10.1038/mp.2010.123 · Cited by 157
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Self-citationVascular burden and Alzheimer disease pathologic progression
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Self-citationRegional shape abnormalities in mild cognitive impairment and Alzheimer's disease
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Serial MRI and CSF biomarkers in normal aging, MCI, and AD
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Self-citationThe role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI)
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Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622
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Self-citationCommon polygenic variation enhances risk prediction for Alzheimer’s disease
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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.
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