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 contains MRI longitudinal image scans in AD patients from 63 sites across the US and Canada.
“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]
The Abstract describes longitudinal MRI scans of participants with Alzheimer’s disease at multiple North American sites. It specifies 55 participating sites, however, not the claimed 63, and does not explicitly identify the countries as the US and Canada. (LLM, conf=0.90)
Experimental — accuracy not yet measuredADNI develops standardized protocols for multi-center comparison and has helped over 1,000 scientific publications explore prevention and treatment of AD since 2004.
“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]
The Abstract and Discussion (page numbers unavailable) describe ADNI’s development of standardized MRI methods for multisite studies. The Results mention a protocol-selection meeting in 2004, but our copy does not substantiate the claim that ADNI helped over 1,000 publications explore AD prevention and treatment since then. (LLM, conf=0.88)
Experimental — accuracy not yet measuredADNI is the largest and most influential benchmark for Alzheimer's Disease research, including ADNI-1, ADNI-2, ADNI-GO and ADNI-3.
“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]
ADNI provides structural brain MRI, functional MRI, and PET from 1900+ subjects and 59 centers.
“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]
The ADNI dataset used in the experiment comprises ADNI-1 with 1.5T T1-weighted MRIs from 428 subjects (199 AD, 229 NC) and ADNI-2 with 3.0T T1-weighted MRIs from 360 subjects (159 AD, 201 NC).
“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]
This modeled expectation is precisely the observation reported by one of the largest autopsy series to date and is well supported by AD biomarker studies.
“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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Cites
Sequence-independent segmentation of magnetic resonance images
Fischl, B. R., Salat, D. H., van der Kouwe, A. J. W., Makris, N., Ségonne, F., Quinn, B. T., & Dale, A. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.016 · Cited by 2,117 · see citing works
Hill, D. L. G., Batchelor, P. G., Holden, M., & Hawkes, D. J. (2001) · Physics in Medicine and Biology · doi:10.1088/0031-9155/46/3/201 · Cited by 2,093 · see citing works
Self-citationThe Alzheimer's Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.008 · Cited by 1,525 · see citing works
Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment
Jack, C. R., Petersen, R. C., Xu, Y. C., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Boeve, B. F., Waring, S. C., Tangalos, E. G., & Kokmen, E. (1999) · Neurology · doi:10.1212/wnl.52.7.1397 · Cited by 1,404 · see citing works
Jovicich, J., Czanner, S., Greve, D. N., Haley, E., van der Kouwe, A. J. W., Gollub, R. L., Kennedy, D. N., Schmitt, F., Brown, G. G., MacFall, J. R., et al. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.09.046 · Cited by 1,269 · see citing works
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2005.06.003 · Cited by 1,233 · see citing works
Du, A. (2001) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.71.4.441 · Cited by 729 · see citing works
Use of structural magnetic resonance imaging to predict who will get Alzheimer's disease
Killiany, R., Gómez‐Isla, T., Moss, M. B., Kikinis, R., Sándor, T., Jolesz, F. A., Tanzi, R. E., Jones, K. J., Hyman, B. T., & Albert, M. (2000) · Annals of Neurology · doi:10.1002/1531-8249(200004)47:4 · Cited by 659 · see citing works
Presymptomatic hippocampal atrophy in Alzheimer's disease: A longitudinal MRI study
Fox, N. C., Warrington, E. K., Freeborough, P. A., Hartikainen, P., Kennedy, A. M., Stevens, J. M., & Rossor, M. N. (1996) · Brain · doi:10.1093/brain/119.6.2001 · Cited by 656 · see citing works
MRI measures of entorhinal cortex vs hippocampus in preclinical AD
Killiany, R., Hyman, B. T., Gómez‐Isla, T., Moss, M. B., Kikinis, R., Jolesz, F. A., Tanzi, R. E., Jones, K., & Albert, M. (2002) · Neurology · doi:10.1212/wnl.58.8.1188 · Cited by 564 · see citing works
Medial temporal lobe function and structure in mild cognitive impairment
Dickerson, B. C., Salat, D. H., Bates, J. F., Atiya, M., Killiany, R., Greve, D. N., Dale, A. M., Stern, C. E., Blacker, D., Albert, M., et al. (2004) · Annals of Neurology · doi:10.1002/ana.20163 · Cited by 561 · see citing works
Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI
Jack, C. R., Shiung, M. M., Weigand, S. D., O’Brien, P. C., Gunter, J. L., Boeve, B. F., Knopman, D. S., Smith, G. E., Ivnik, R. J., Tangalos, E. G., et al. (2005) · Neurology · doi:10.1212/01.wnl.0000180958.22678.91 · Cited by 492 · see citing works
Deoni, S., Peters, T. M., & Rutt, B. K. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20314 · Cited by 482 · see citing works
Dickerson, B. C., Goncharova, I. I., Sullivan, M., Forchetti, C., Wilson, R. S., Bennett, D. A., Beckett, L. A., & deToledo‐Morrell, L. (2001) · Neurobiology of Aging · doi:10.1016/s0197-4580(01)00271-8 · Cited by 481 · see citing works
Fox, N. C., Crum, W. R., Scahill, R. I., Stevens, J. M., Janssen, J. C., & Rossor, M. N. (2001) · The Lancet · doi:10.1016/s0140-6736(01)05408-3 · Cited by 455 · see citing works
Self-citationHajnal, J. V., & Hill, D. L. G. (2001) · Lecture notes in computer science · doi:10.1007/978-3-032-25169-5 · Cited by 402 · see citing works
Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD
de Leon, M. J., Bobinski, M., Convit, A. J., Wolf, O. T., & Insausti, R. (2001) · Neurology · doi:10.1212/wnl.56.6.820 · Cited by 348 · see citing works
Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD
Xu, Y., Jack, C. R., O’Brien, P. C., Kokmen, E., Smith, G. E., Ivnik, R. J., Boeve, B. F., Tangalos, R. G., & Petersen, R. C. (2000) · Neurology · doi:10.1212/wnl.54.9.1760 · Cited by 336 · see citing works
Longitudinal stability of MRI for mapping brain change using tensor-based morphometry
Leow, A., Klunder, A. D., Jack, C., Toga, A., Dale, A., Bernstein, M., Britson, P. J., Gunter, J., Ward, C. P., Whitwell, J., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.12.013 · Cited by 237 · see citing works
Compensation for surface coil sensitivity variation in magnetic resonance imaging
Narayana, P. A., Brey, W. W., Kulkarni, M. V., & Sievenpiper, C. (1988) · Magnetic Resonance Imaging · doi:10.1016/0730-725x(88)90401-8 · Cited by 131 · see citing works
Gunter, J. L., Bernstein, M. A., Borowski, B., Felmlee, J. P., Blezek, D. J., Mallozzi, R. P., Levy, J. R., & Schuff, N. W. (2006) · Cited by 42 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Dickerson BC, Goncharova I, Sullivan MP, et al. MRI-derived ento- rhinal and hippocampal atrophy in incipient and very mild Alzhei- mer's disease. Neurobiol Aging 2001;22:747-754.
Cited by
Sorted by citations
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487 · see citing works
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · see citing works
Villemagne, V. L., Burnham, S., Bourgeat, P., Brown, B., Ellis, K. A., Salvado, O., Szoeke, C., Macaulay, S. L., Martins, R., Maruff, P., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70044-9 · Cited by 2,279 · see citing works
Nelson, P. T., Alafuzoff, I., Bigio, E. H., Bouras, C., Braak, H., Cairns, N. J., Castellani, R. J., Crain, B. J., Davies, P., Tredici, K. D., et al. (2012) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e31825018f7 · Cited by 2,050 · see citing works
Alzheimer’s Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W. J., Trojanowski, J. Q., Toga, A. W., & Beckett, L. A. (2007) · Springer eBooks · doi:10.1007/978-0-387-72076-0_18 · Cited by 1,493 · see citing works
Behavioral and psychological symptoms of dementia
Cerejeira, J., Lagarto, L., & Mukaetova-Ladinska, E. B. (2012) · Frontiers in Neurology · doi:10.3389/fneur.2012.00073 · Cited by 1,425 · see citing works
Alzheimer’s Disease: The Challenge of the Second Century
Holtzman, D. M., Morris, J. C., & Goate, A. M. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002369 · Cited by 1,424 · see citing works
Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease
Mattsson, N., Andreasson, U., Zetterberg, H., Blennow, K., & Initiative, F. T. A. D. N. (2017) · JAMA Neurology · doi:10.1001/jamaneurol.2016.6117 · Cited by 1,070 · see citing works
Jack, C. R., Lowe, V. J., Weigand, S. D., Wiste, H. J., Senjem, M. L., Knopman, D. S., Shiung, M. M., Gunter, J. L., Boeve, B. F., Kemp, B. J., et al. (2009) · Brain · doi:10.1093/brain/awp062 · Cited by 1,099 · see citing works
The Human Connectome Project's neuroimaging approach
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4361 · Cited by 1,090 · see citing works
Biomarker Modeling of Alzheimer’s Disease
Jack, C. R., & Holtzman, D. M. (2013) · Neuron · doi:10.1016/j.neuron.2013.12.003 · Cited by 1,002 · see citing works
Preparing Medical Imaging Data for Machine Learning
Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology · doi:10.1148/radiol.2020192224 · Cited by 1,020 · see citing works
The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Liu, E., et al. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.05.1769 · Cited by 1,011 · see citing works
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968 · see citing works
Palmqvist, S., Schöll, M., Strandberg, O., Mattsson, N., Stomrud, E., Zetterberg, H., Blennow, K., Landau, S., Jagust, W., & Hansson, O. (2017) · Nature Communications · doi:10.1038/s41467-017-01150-x · Cited by 911 · see citing works
Domain adaptation for medical image analysis: A survey
Guan, H., & Liu, M. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3117407 · Cited by 1,057 · see citing works
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912 · see citing works
Cuingnet, R., Gérardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M., Marie Chupin, A., Benali, H., & Colliot, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.013 · Cited by 960 · see citing works
López, J. A., González, H. M., & Léger, G. C. (2019) · Handbook of clinical neurology · doi:10.1016/b978-0-12-804766-8.00013-3 · Cited by 834 · see citing works
Jack, C. R., Lowe, V. J., Senjem, M. L., Weigand, S. D., Kemp, B. J., Shiung, M. M., Knopman, D. S., Boeve, B. F., Klunk, W. E., Mathis, C. A., et al. (2008) · Brain · doi:10.1093/brain/awm336 · Cited by 958 · see citing works
Impact of multiple pathologies on the threshold for clinically overt dementia
Kapasi, A., DeCarli, C., & Schneider, J. A. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1717-7 · Cited by 739 · see citing works
Mattsson, N., Cullen, N. C., Andreasson, U., Zetterberg, H., & Blennow, K. (2019) · JAMA Neurology · doi:10.1001/jamaneurol.2019.0765 · Cited by 726 · see citing works
Docosahexaenoic Acid Supplementation and Cognitive Decline in Alzheimer Disease: A Randomized Trial
Quinn, J. F., Raman, R., Thomas, R. G., Yurko-Mauro, K., Nelson, E. B., Van Dyck, C., Galvin, J. E., Emond, J., Jack, C. R., Weiner, M., et al. (2010) · JAMA · doi:10.1001/jama.2010.1510 · Cited by 764 · see citing works
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data
the Alzheimer’s Disease Neuroimaging Initiative, E. C., Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., Toro, R., Jahanshad, N., Schumann, G., et al. (2014) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9269-5 · Cited by 838 · see citing works
Jo, T., Nho, K., & Saykin, A. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00220 · Cited by 724 · see citing works
Mindboggling morphometry of human brains
Klein, A., Ghosh, S., Bao, F. S., Giard, J., Häme, Y., Stavsky, E., Lee, N., Rossa, B., Reuter, M., Neto, E. C., et al. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005350 · Cited by 732 · see citing works
Rinne, J. O., Brooks, D. J., Rossor, M. N., Fox, N. C., Bullock, R., Klunk, W. E., Mathis, C. A., Blennow, K., Barakos, J., Okello, A. A., et al. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(10)70043-0 · Cited by 701 · see citing works
Informatics and Data Mining Tools and Strategies for the Human Connectome Project
Marcus, D. S., Harwell, J., Olsen, T., Hodge, M. R., Glasser, M. F., Prior, F., Jenkinson, M., Laumann, T. O., Curtiss, S. W., & Essen, D. C. V. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00004 · Cited by 692 · see citing works
Raji, C. A., Ho, A. J., Parikshak, N., Becker, J. T., López, O. L., Kuller, L. H., Hua, X., Leow, A., Toga, A. W., & Thompson, P. M. (2009) · Human Brain Mapping · doi:10.1002/hbm.20870 · Cited by 692 · see citing works
Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials
Ellingson, B. M., Bendszus, M., Boxerman, J. L., Barboriak, D. P., Erickson, B. J., Smits, M., Nelson, S. J., Gerstner, E. R., Alexander, B. M., Goldmacher, G., et al. (2015) · Neuro-Oncology · doi:10.1093/neuonc/nov095 · Cited by 681 · see citing works
FastSurfer - A fast and accurate deep learning based neuroimaging pipeline
Henschel, L., Conjeti, S., Estrada, S., Diers, K., Fischl, B. R., & Reuter, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117012 · Cited by 672 · see citing works
Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M. J., & ADNI (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2372011 · Cited by 637 · see citing works
Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives
Hampel, H. J., Frank, R., Broich, K., Teipel, S. J., Katz, R. G., Hardy, J., Herholz, K., Bokde, A. L. W., Jessen, F., Hoessler, Y. C., et al. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3115 · Cited by 630 · see citing works
Initiative, A. D. N., Crane, P. K., Carle, A. C., Gibbons, L. E., Insel, P. S., Mackin, R. S., Gross, A. L., Jones, R. N., Mukherjee, S., Curtis, S. M., et al. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9186-z · Cited by 615 · see citing works
Comparing predictors of conversion and decline in mild cognitive impairment
Landau, S. M., Harvey, D., Madison, C. M., Reiman, E. M., Foster, N. L., Aisen, P. S., Petersen, R. C., Shaw, L. M., Trojanowski, J. Q., Jack, C. R., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181e8e8b8 · Cited by 616 · see citing works
The Alzheimer's Disease Neuroimaging Initiative: Progress report and future plans
Weiner, M. W., Aisen, P. S. S., Jack, C. R., Jagust, W. J., Trojanowski, J. Q., Shaw, L. M. J., Saykin, A. J., Morris, J. C., Cairns, N. J., Beckett, L. A., et al. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.007 · Cited by 599 · see citing works
Psilocybin for treatment-resistant depression: fMRI-measured brain mechanisms
Carhart-Harris, R. L., Roseman, L., Bolstridge, M., Demetriou, L., Pannekoek, J. N., Wall, M. B., Tanner, M., Kaelen, M., McGonigle, J., Murphy, K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13282-7 · Cited by 587 · see citing works
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease
Shamonin, D. P. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00050 · Cited by 582 · see citing works
Neuroimaging of the Philadelphia Neurodevelopmental Cohort
Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E., Hopson, R., Jackson, C., Keefe, J., Riley, M., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.064 · Cited by 504 · see citing works
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification
Davatzikos, C. A., Bhatt, P., Shaw, L. M. J., Batmanghelich, K. N., & Trojanowski, J. Q. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.023 · Cited by 578 · see citing works
Episodic memory loss is related to hippocampal-mediated -amyloid deposition in elderly subjects
Mormino, E. C., Kluth, J. T., Madison, C. M., Rabinovici, G. D., Baker, S. L., Miller, B. L., Koeppe, R. A., Mathis, C. A., Weiner, M. W., Jagust, W. J., et al. (2009) · Brain · doi:10.1093/brain/awn320 · Cited by 684 · see citing works
Computational anatomy with the SPM software
Ashburner, J. (2009) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2009.01.006 · Cited by 565 · see citing works
Lian, C., Liu, M., Zhang, J., & Shen, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889096 · Cited by 544 · see citing works
Schuff, N., Woerner, N., Boreta, L., Kornfield, T., Shaw, L. M., Trojanowski, J. Q., Thompson, P. M., Jack, C. R., Weiner, M. W., & Initiative, T. A. D. N. (2008) · Brain · doi:10.1093/brain/awp007 · Cited by 679 · see citing works
Jovicich, J., Czanner, S., Han, X., Salat, D. H., van der Kouwe, A. J. W., Quinn, B. T., Pacheco, J., Albert, M., Killiany, R., & Blacker, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.02.010 · Cited by 549 · see citing works
Early diagnosis of Alzheimer's disease with deep learning
Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., & Feng, D. D. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6868045 · Cited by 559 · see citing works
Functional brain connectivity using fMRI in aging and Alzheimer’s disease
Dennis, E. L., & Thompson, P. M. (2014) · Neuropsychology Review · doi:10.1007/s11065-014-9249-6 · Cited by 583 · see citing works
Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort
Risacher, S., Saykin, A., Wes, J., Shen, L., Firpi, H., & McDonald, B. (2009) · Current Alzheimer Research · doi:10.2174/156720509788929273 · Cited by 538 · see citing works
A Phase II Trial of Tideglusib in Alzheimer's Disease
Lovestone, S., for the ARGO investigators, Boada, M., Dubois, B., Hüll, M., Rinne, J. O., Huppertz, H.-J., Calero, M., Andrés, M. V., Gómez-Carrillo, B., et al. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-141959 · Cited by 536 · see citing works
Pomponio, R., Erus, G., Habes, M., Doshi, J., Srinivasan, D., Mamourian, E., Bashyam, V. M., Nasrallah, I., Satterthwaite, T., Fan, Y., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116450 · Cited by 576 · see citing works
Human Connectome Project informatics: Quality control, database services, and data visualization
Marcus, D. S., Harms, M. P., Snyder, A. Z., Jenkinson, M., Wilson, J. A., Glasser, M. F., Barch, D. M., Archie, K. A., Burgess, G. C., Ramaratnam, M., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.077 · Cited by 465 · see citing works
Plasma tau in Alzheimer disease
Mattsson, N., Zetterberg, H., Janelidze, S., Insel, P. S., Andreasson, U., Stomrud, E., Palmqvist, S., Baker, D., Tan Hehir, C. A., Jeromin, A., et al. (2016) · Neurology · doi:10.1212/wnl.0000000000003246 · Cited by 502 · see citing works
AD dementia risk in late MCI, in early MCI, and in subjective memory impairment.
Jessen, F., Wolfsgruber, S., Wiese, B., Bickel, H., Mösch, E., Kaduszkiewicz, H., Pentzek, M., Riedel‐Heller, S. G., Luck, T., Fuchs, A., et al. (2014) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2012.09.017 · Cited by 511 · see citing works
Homocysteine, B Vitamins, and Cognitive Impairment
Smith, A. D., & Refsum, H. (2016) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-071715-050947 · Cited by 524 · see citing works
Deep MRI brain extraction: A 3D convolutional neural network for skull stripping
Kleesiek, J., Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., & Biller, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.01.024 · Cited by 505 · see citing works
Initiative, A. D. N., Gibbons, L. E., Carle, A. C., Mackin, R. S., Harvey, D. J., Mukherjee, S., Insel, P. S., Curtis, S. M., Mungas, D., & Crane, P. K. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9176-1 · Cited by 495 · see citing works
Liu, M., Li, F., Yan, H., Wang, K., Ma, Y., Shen, L., & Xu, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116459 · Cited by 568 · see citing works
Federated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518 · see citing works
A cross-disorder connectome landscape of brain dysconnectivity
van den Heuvel, M. P., & Sporns, O. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0177-6 · Cited by 462 · see citing works
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Morris, J. C., et al. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.10.006 · Cited by 497 · see citing works
Nestor, S. M., Rupsingh, R., Borrie, M., Smith, M., Accomazzi, V., Wells, J. L., Fogarty, J., Bartha, R., & Initiative, T. A. D. N. (2008) · Brain · doi:10.1093/brain/awn146 · Cited by 543 · see citing works
Zetterberg, H., Skillbäck, T., Mattsson, N., Trojanowski, J. Q., Portelius, E., Shaw, L. M., Weiner, M. W., Blennow, K., & Initiative, F. T. A. D. N. (2016) · JAMA Neurology · doi:10.1001/jamaneurol.2015.3037 · Cited by 487 · see citing works
Self-citationVarma, V. R., Oommen, A. M., Varma, S., Casanova, R., An, Y., Andrews, R. M., O’Brien, R., Pletniková, O., Troncoso, J. C., Toledo, J. B., et al. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002482 · Cited by 478 · see citing works
The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
Weiner, M. W., Veitch, D. P., Aisen, P. S. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D. J., Jack, C. R., Jagust, W. J., Liu, E., et al. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.09.172 · Cited by 477 · see citing works
Jack, C. R., Wiste, H. J., Vemuri, P., Weigand, S. D., Senjem, M. L., Zeng, G., Bernstein, M. A., Gunter, J. L., Pankratz, V. S., Aisen, P. S., et al. (2010) · Brain · doi:10.1093/brain/awq277 · Cited by 531 · see citing works
BEaST: Brain extraction based on nonlocal segmentation technique
Eskildsen, S. F., Pierrick Coupé, A., Fonov, V., Manjón, J. V., Leung, K. K., Guizard, N., Wassef, S. N., Østergaard, L. R., & Collins, D. L. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.012 · Cited by 475 · see citing works
Voevodskaya, O., Simmons, A., Nordenskjöld, R., Kullberg, J., Ahlström, H., Lind, L., Wahlund, L., Larsson, E., Westman, E., & Initiative, A. D. N. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00264 · Cited by 473 · see citing works
Holland, D., Kuperman, J. M., & Dale, A. M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.044 · Cited by 471 · see citing works
Eo, T., Jun, Y., Kim, T., Jang, J., Lee, H., & Hwang, D. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27201 · Cited by 455 · see citing works
Saygin, Z. M., Kliemann, D., Iglesias, J. E., van der Kouwe, A. J. W., Boyd, E. C., Reuter, M., Stevens, A. A., Leemput, K. V., McKee, A. C., Frosch, M. P., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.046 · Cited by 456 · see citing works
Smoking and increased Alzheimer's disease risk: A review of potential mechanisms
Durazzo, T. C., Mattsson, N., Weiner, M. W., & Initiative, A. D. N. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2014.04.009 · Cited by 461 · see citing works
Shi, J., Zheng, X., Li, Y., Zhang, Q., & Ying, S. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2655720 · Cited by 444 · see citing works
Holmes, A. J., Hollinshead, M. O., O’Keefe, T. M., Petrov, V. I., Fariello, G., Wald, L. L., Fischl, B. R., Rosen, B. R., Mair, R. W., Roffman, J. L., et al. (2015) · Scientific Data · doi:10.1038/sdata.2015.31 · Cited by 436 · see citing works
Chataway, J., Schuerer, N., Alsanousi, A., Chan, D., MacManus, D. G., Hunter, K., Anderson, V. M., Bangham, C. R. M., Clegg, S., Nielsen, C. F., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)62242-4 · Cited by 444 · see citing works
Saykin, A. J., Shen, L., Foroud, T. M., Potkin, S. G., Swaminathan, S., Kim, S., Risacher, S. L., Nho, K., Huentelman, M. J., Craig, D. W., et al. (2010) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2010.03.013 · Cited by 442 · see citing works
Donohue, M. C., Sperling, R. A., Petersen, R., Sun, C.-K., Weiner, M. W., Aisen, P. S., & Initiative, F. T. A. D. N. (2017) · JAMA · doi:10.1001/jama.2017.6669 · Cited by 452 · see citing works
The Lifespan Human Connectome Project in Aging: An overview
Bookheimer, S. Y., Salat, D. H., Terpstra, M., Ances, B. M., Barch, D. M., Buckner, R. L., Burgess, G. C., Curtiss, S. W., Díaz‐Santos, M., Elam, J. S., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.009 · Cited by 440 · see citing works
Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review
Ghahnavieh, A. E., Luo, S., & Chiong, R. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105242 · Cited by 439 · see citing works
Statistical normalization techniques for magnetic resonance imaging
Shinohara, R. T., Sweeney, E. M., Goldsmith, J., Shiee, N., Mateen, F. J., Calabresi, P. A., Jarso, S., Pham, D. L., Reich, D. S., & Crainiceanu, C. M. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.08.008 · Cited by 426 · see citing works
Landmark-based deep multi-instance learning for brain disease diagnosis
Liu, M., Zhang, J., Adeli, E., & Shen, D. G. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.10.005 · Cited by 424 · see citing works
Multimodal deep learning for Alzheimer's disease dementia assessment
Qiu, S., Miller, M. I., Joshi, P. S., Lee, J. C., Xue, C., Ni, Y., Wang, Y., De Anda-Duran, I., Hwang, P. H., Cramer, J. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31037-5 · Cited by 420 · see citing works
Palmqvist, S., Mattsson, N., Hansson, O., & Initiative, F. T. A. D. N. (2016) · Brain · doi:10.1093/brain/aww015 · Cited by 400 · see citing works
Deep Learning for Medical Anomaly Detection – A Survey
Fernando, T., Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2021) · ACM Computing Surveys · doi:10.1145/3464423 · Cited by 414 · see citing works
Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis
Initiative, T. A. D. N., Liu, M., Cheng, D., Wang, K., & Wang, Y. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9370-4 · Cited by 404 · see citing works
MRI and CSF biomarkers in normal, MCI, and AD subjects
Vemuri, P., Wiste, H. J., Weigand, S. D., Shaw, L. M. J., Trojanowski, J. Q., Weiner, M. W., Knopman, D. S., Petersen, R. C., & Jack, C. R. (2009) · Neurology · doi:10.1212/wnl.0b013e3181af79fb · Cited by 399 · see citing works
Regional variability of imaging biomarkers in autosomal dominant Alzheimer's disease
Benzinger, T. L. S., Blazey, T., Jack, C. R., Koeppe, R. A., Su, Y., Xiong, C., Raichle, M. E., Snyder, A. Z., Ances, B. M., Bateman, R. J., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317918110 · Cited by 391 · see citing works
Ewers, M., Walsh, C., Trojanowski, J. Q., Shaw, L. M. J., Petersen, R. C., Jack, C. R., Feldman, H., Bokde, A. L. W., Alexander, G. E., Scheltens, P., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.10.019 · Cited by 396 · see citing works
Low‐field MRI: An MR physics perspective
Marques, J. P., Simonis, F. F. J., & WEBB, A. G. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26637 · Cited by 389 · see citing works
Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models
Bernal-Rusiel, J. L., Greve, D. N., Reuter, M., Fischl, B., & Sabuncu, M. R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.065 · Cited by 360 · see citing works
Martín‐Bastida, A., Ward, R. J., Newbould, R. D., Piccini, P. P., Sharp, D. J., Kabba, C., Patel, M. C., Spino, M., Connelly, J., Tricta, F., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-01402-2 · Cited by 382 · see citing works
Shen, L., Kim, S., Risacher, S. L., Nho, K. T., Swaminathan, S., West, J. D., Foroud, T., Pankratz, N., Moore, J. H., Sloan, C., et al. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.042 · Cited by 379 · see citing works
Potkin, S. G., Guffanti, G., Lakatos, A., Turner, J. A., Kruggel, F., Fallon, J. H., Saykin, A. J., Orro, A., Lupoli, S., Salvi, E., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006501 · Cited by 376 · see citing works
Optimized three‐dimensional fast‐spin‐echo MRI
Mugler, J. P. (2014) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24542 · Cited by 372 · see citing works
Age-related changes in the default mode network are more advanced in Alzheimer disease
Jones, D. T., Machulda, M. M., Vemuri, P., McDade, E. M., Zeng, G., Senjem, M. L., Gunter, J. L., Przybelski, S. A., Avula, R. T., Knopman, D. S., et al. (2011) · Neurology · doi:10.1212/wnl.0b013e318233b33d · Cited by 370 · see citing works
Self-citationLanglotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., et al. (2019) · Radiology · doi:10.1148/radiol.2019190613 · Cited by 368 · see citing works
Sex differences in the association between AD biomarkers and cognitive decline
Initiative, F. T. A. N., Koran, M. E. I., Wagener, M., & Hohman, T. J. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-016-9523-8 · Cited by 339 · see citing works
Apostolova, L. G., Green, A. E., Babakchanian, S., Hwang, K., Chou, Y., Toga, A. W., & Thompson, P. M. (2011) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e3182163b62 · Cited by 363 · see citing works
Voxel-Based Morphometry: An Automated Technique for Assessing Structural Changes in the Brain
Whitwell, J. L. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2160-09.2009 · Cited by 365 · see citing works
Bron, E. E., Smits, M., van der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, P., Papma, J. M., Steketee, R. M. E., Méndez Orellana, C., Meijboom, R., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.048 · Cited by 352 · see citing works
- 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
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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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