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The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Open accessAbstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and Alzheimer's disease. Magnetic resonance imaging (MRI), (18F)-fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, and cerebrospinal fluid (CSF) biomarkers, as well as clinical/psychometric assessments are acquired at multiple time points. All data will be cross-linked and made available to the general scientific community. The purpose of this report is to describe the MRI methods employed in ADNI. The ADNI MRI core established specifications that guided protocol development. A major effort was devoted to evaluating 3D T(1)-weighted sequences for morphometric analyses. Several options for this sequence were optimized for the relevant manufacturer platforms and then compared in a reduced-scale clinical trial. The protocol selected for the ADNI study includes: back-to-back 3D magnetization prepared rapid gradient echo (MP-RAGE) scans; B(1)-calibration scans when applicable; and an axial proton density-T(2) dual contrast (i.e., echo) fast spin echo/turbo spin echo (FSE/TSE) for pathology detection. ADNI MRI methods seek to maximize scientific utility while minimizing the burden placed on participants. The approach taken in ADNI to standardization across sites and platforms of the MRI protocol, postacquisition corrections, and phantom-based monitoring of all scanners could be used as a model for other multisite trials.
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“The ADNI dataset as one example in brain datasets contains MRI longitudinal image scans in AD patients from 63 sites across the US and Canada.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [26]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“ADNI develops standardized protocols for multi-center comparison and helps over 1,000 scientific publications explore the prevention and treatment of AD since 2004”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [26]
Experimental — accuracy not yet measuredIn the citing paper
“ADNI-The Alzheimer's Disease Neuroimaging Initiative (ADNI) [101,102] is the largest and most influential benchmark for the research of Alzheimer's Disease (AD), including ADNI-1, ADNI-2, ADNI-GO and ADNI-3.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [102]
In the citing paper
“Structural brain MRI, functional MRI, and positron emission tomography (PET) from 1900+ subjects and 59 centers are provided for analysis and research.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [102]
In the citing paper
“This dataset comprises two subsets: ADNI-1, featuring 1.5T T1-weighted MRIs from 428 subjects (199 with Alzheimer's Disease (AD) and 229 normal controls (NCs)), and ADNI-2, with 3.0T T1weighted MRIs from 360 subjects (159 AD patients and 201 NCs).”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [102]
In the citing paper
“Indeed, this is precisely the observation reported by one of the largest autopsy series to date (142) and is well supported by AD biomarker studies (143-145).”
From Correlation of Alzheimer Disease Neuropathologic Changes With Cognitive Status: A Review of the Literature (2012) · cites this paper as [144]
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Metadata
- Authors
- 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., Dale, A. M., Felmlee, J. P., Gunter, J. L., Hill, D. L. G., Killiany, R., Schuff, N., Fox‐Bosetti, S., Lin, C., Studholme, C., DeCarli, C. S., Krueger, G., Ward, H. A., Metzger, G. J., Scott, K. T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J. P., Fleisher, A. S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., & Weiner, M. W.
- Affiliations
- Mayo Clinic (US)
- National Hospital for Neurology and Neurosurgery (GB)
- University of California, Los Angeles (US)
- Arizona State University (US)
- University of California, Davis (US)
- University of California San Diego (US)
- San Francisco VA Medical Center (US)
- Philips (Netherlands) (NL)
- General Electric (United States) (US)
- Johns Hopkins University (US)
- Boston University (US)
- Stanford Medicine (US)
- University of Virginia (US)
- Corresponding author
- Jack, Clifford R.
- ORCID
- Jack, Clifford R.
- Bernstein, Matt A.
- Fox, Nick C.
- Thompson, Paul
- Alexander, Gene
- Harvey, Danielle
- Dale, Anders M.
- Felmlee, Joel P.
- Gunter, Jeffrey L.
- Lin, Chen
- Studholme, Colin
- DeCarli, Charles S.
- Metzger, Gregory J.
- Blezek, Daniel
- Fleisher, Adam S.
- Albert, Marilyn
- Green, Robert
- Glover, Gary
- Mugler, John
- Weiner, Michael W.
- Venue
- Journal of Magnetic Resonance Imaging
- Publisher
- Wiley
- Published
- 2008-02-26
- Volume
- 27
- Issue
- 4
- Pages
- 685-691
- ISSN
- 1053-1807 (ISSN-L)
- 1522-2586
- PMC
- PMC2544629
- OpenAlex
- W2078524519
- Cited by
- 4,347
- References
- 21
- Citations by year
- 2026: 298 · 2025: 359 · 2024: 292 · 2023: 291 · 2022: 266 · 2021: 319 · 2020: 336 · 2019: 256 · 2018: 223 · 2017: 193 · 2016: 255 · 2015: 264 · 2014: 233 · 2013: 217 · 2012: 165
- FWCI
- 86.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Foundation for the National Institutes of Health (U01AG024904, AG024904)
- Alzheimer's Association (U01 AG024904)
- Bristol-Myers Squibb (U01 AG024904, AG024904)
- Eli Lilly and Company (U01 AG024904, AG024904)
- Pfizer (AG024904, U01 AG024904)
- AstraZeneca
- GlaxoSmithKline
- Alzheimer's Disease Neuroimaging Initiative (U01AG024904, AG024904)
- Novartis Pharmaceuticals Corporation (U01 AG024904, AG024904)
- Elan (U01 AG024904)
- Eisai (U01 AG024904, AG024904)
- National Institutes of Health (5u01ag024904-10, U01-AG024904, AG024904)
- U.S. Food and Drug Administration
- National Institute on Aging (U01-AG-024904, P30AG010129, AG 024904, U19AG010483)
- National Institute of Biomedical Imaging and Bioengineering (U01 AG024904, AG024904)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- Alzheimer's disease, mild cognitive impairment, MRI protocol standardization, 3D T1-weighted imaging, MPRAGE, fast spin echo, brain morphometry, longitudinal neuroimaging
- MeSH terms
- Aged, Alzheimer Disease, Brain, Humans, Magnetic Resonance Imaging
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.
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