Cited by 348Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsCancer-related cognitive impairment studies

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2014 Update of the Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception

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Weiner, Michael W., Veitch, Dallas P., Aisen, Paul S., Beckett, Laurel A., Cairns, Nigel J., Cedarbaum, Jesse, Green, Robert C., Harvey, Danielle, Jack, Clifford R., Jagust, William, Luthman, Johan, Morris, John C., Petersen, Ronald C., Saykin, Andrew J., Shaw, Leslie, Shen, Li, Schwarz, Adam, Toga, Arthur W., Trojanowski, John Q., Alzheimer's Disease Neuroimaging Initiative·2015·Alzheimer's & Dementia

DOI 10.1016/j.jalz.2014.11.001PMC PMC5469297

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Abstract

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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Toussaint, P., Perlbarg, V., Bellec, P., Désarnaud, S., Lacomblez, L., Doyon, J., Habert, M., & Benali, H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.091 · Cited by 112
Relationship between regional atrophy rates and cognitive decline in mild cognitive impairment
McDonald, C. R., Gharapetian, L., McEvoy, L. K., Fennema‐Notestine, C., Hagler, D. J., Holland, D., & Dale, A. M. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.03.015 · Cited by 111
Characterization of PiB Binding to White Matter in Alzheimer Disease and Other Dementias
Fodero‐Tavoletti, M. T., Rowe, C. C., McLean, C., Leone, L., Li, Q., Masters, C. L., Cappai, R., & Villemagne, V. L. (2009) · Journal of Nuclear Medicine · doi:10.2967/jnumed.108.057984 · Cited by 111
Body Mass Index and Cognitive Decline in Mild Cognitive Impairment
Cronk, B. B., Johnson, D. K., & Burns, J. M. (2010) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e3181a6bf3f · Cited by 109
Genetic Variants in the Fat and Obesity Associated (FTO) Gene and Risk of Alzheimer's Disease
Reitz, C., Tosto, G., Mayeux, R. P., Luchsinger, J. A., & the NIA-LOAD/NCRAD Family Study Group and the Alzheimer's Disease Neuroimaging Initiative (2012) · PLoS ONE · doi:10.1371/journal.pone.0050354 · Cited by 109
Simultaneous analysis of cerebrospinal fluid biomarkers using microsphere-based xMAP multiplex technology for early detection of Alzheimer’s disease
Kang, J., Vanderstichele, H., Trojanowski, J. Q., & Shaw, L. M. J. (2012) · Methods · doi:10.1016/j.ymeth.2012.03.023 · Cited by 109
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Arterial spin labeled MRI in prodromal Alzheimer's disease: A multi-site study
Wang, Z., Das, S. R., Xie, S. X., Arnold, S. E., Detre, J. A., & Wolk, D. A. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.04.014 · Cited by 108
Subregions of the inferior parietal lobule are affected in the progression to Alzheimer's disease
Greene, S. J., & Killiany, R. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.026 · Cited by 108
Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer's Disease
Escudero, J., Ifeachor, E., Zajicek, J. P., Green, C., Shearer, J., & Pearson, S. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2212278 · Cited by 107
Alzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods
Casanova, R., Hsu, F., Sink, K. M., Rapp, S. R., Williamson, J. D., Resnick, S. M., Espeland, M. A., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077949 · Cited by 107
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In vitro characterisation of BF227 binding to α-synuclein/Lewy bodies
Fodero‐Tavoletti, M. T., Mulligan, R. S., Okamura, N., Furumoto, S., Rowe, C. C., Kudo, Y., Masters, C. L., Cappai, R., Yanai, K., & Villemagne, V. L. (2009) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2009.06.042 · Cited by 107
CSF α-synuclein improves diagnostic and prognostic performance of CSF tau and Aβ in Alzheimer’s disease
Toledo, J. B., Korff, A., Shaw, L. M. J., Trojanowski, J. Q., & Zhang, J. (2013) · Acta Neuropathologica · doi:10.1007/s00401-013-1148-z · Cited by 106
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Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease
Koikkalainen, J. R., Lötjönen, J. M. P., Thurfjell, L., Rueckert, D., Waldemar, G., & Soininen, H. S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.03.029 · Cited by 106
Brain Atrophy in Healthy Aging Is Related to CSF Levels of Aβ1-42
Fjell, A. M., Walhovd, K. B., Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Blennow, K., Brewer, J. B., Dale, A. M., & Initiative, T. A. D. N. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp279 · Cited by 106
Rates of Decline in Alzheimer Disease Decrease with Age
Holland, D., Desikan, R. S., Dale, A. M., McEvoy, L. K., & Initiative, A. D. N. (2012) · PLoS ONE · doi:10.1371/journal.pone.0042325 · Cited by 104
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Association and Expression Analyses With Single-Nucleotide Polymorphisms in TOMM40 in Alzheimer Disease
Cruchaga, C. (2011) · Archives of Neurology · doi:10.1001/archneurol.2011.155 · Cited by 103
Consistent reconstruction of cortical surfaces from longitudinal brain MR images
Li, G., Nie, J., Guorong Wu, A., Wang, Y., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.012 · Cited by 103
Automated voxel-based 3D cortical thickness measurement in a combined Lagrangian–Eulerian PDE approach using partial volume maps
Acosta, O., Bourgeat, P., Zuluaga, M. A., Fripp, J., Salvado, O., & Sebastien Ourselin, A. (2009) · Medical Image Analysis · doi:10.1016/j.media.2009.07.003 · Cited by 101
Plasma Based Markers of [11C] PiB-PET Brain Amyloid Burden
Kiddle, S. J., Thambisetty, M., Simmons, A., Riddoch-Contreras, J., Hye, A., Westman, E., Pike, I., Ward, M., Johnston, C., Lupton, M. K., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044260 · Cited by 100
Genome-wide association analysis of age-at-onset in Alzheimer's disease
Kamboh, M. I., Barmada, M. M., Demirci, F. Y., Minster, R. L., Carrasquillo, M. M., Pankratz, V. S., Younkin, S. G., Saykin, A. J., Initiative, T. A. D. N., Sweet, R. A., et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.135 · Cited by 100
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The membrane-spanning 4-domains, subfamily A (MS4A) gene cluster contains a common variant associated with Alzheimer's disease
Antúnez, C., Boada, M., González‐Pérez, A., Gayán, J., Ramírez‐Lorca, R., Marín, J., Hernández, I., Moreno‐Rey, C., Morón, F. J., López‐Arrieta, J., et al. (2011) · Genome Medicine · doi:10.1186/gm249 · Cited by 100
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Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models
Shen, K., Fripp, J., Mériaudeau, F., Chételat, G., Salvado, O., & Bourgeat, P. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.014 · Cited by 100
Predicting the location of human perirhinal cortex, Brodmann's area 35, from MRI
Augustinack, J. C., Huber, K. E., Stevens, A. A., Roy, M., Frosch, M. P., van der Kouwe, A. J. W., Wald, L. L., Leemput, K. V., McKee, A. C., & Fischl, B. R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.071 · Cited by 100
Relations between brain tissue loss, CSF biomarkers, and the ApoE genetic profile: a longitudinal MRI study
Tosun, D., Schuff, N. W., Truran‐Sacrey, D., Shaw, L. M. J., Trojanowski, J. Q., Aisen, P. S. S., Peterson, R., & Weiner, M. W. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.030 · Cited by 99
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Harmonization of magnetic resonance‐based manual hippocampal segmentation: A mandatory step for wide clinical use
Frisoni, G. B., & Jack, C. R. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.06.007 · Cited by 98
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Association between mitochondrial DNA variations and Alzheimer's disease in the ADNI cohort
Lakatos, A., Derbeneva, O. A., Younes, D., Keator, D. B., Bakken, T. E., Lvova, M., Brandon, M. C., Guffanti, G., Reglődi, D., Saykin, A. J., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.031 · Cited by 97
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A coding variant in CR1 interacts with APOE-ɛ4 to influence cognitive decline
Keenan, B. T., Shulman, J. M., Chibnik, L. B., Raj, T., Tran, D., Sabuncu, M. R., Initiative, T. A. D. N., Allen, A. N., Corneveaux, J. J., Hardy, J., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds054 · Cited by 97
Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease
Hill, D., Schwarz, A. J., Isaac, M., Pani, L., Vamvakas, S., Hemmings, R., Carrillo, M. C., Yu, P., Sun, J., Beckett, L. A., et al. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.07.003 · Cited by 96
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Topology-Based Kernels With Application to Inference Problems in Alzheimer's Disease
Pachauri, D., Chris Hinrichs, A., Chung, M. K., Johnson, S. C., & Singh, V. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2147327 · Cited by 96
α-Synuclein in Cerebrospinal Fluid of Alzheimer's Disease and Mild Cognitive Impairment
Korff, A., Liu, C., Ginghina, C., Shi, M., Zhang, J., & Initiative, F. T. A. D. N. (2013) · Journal of Alzheimer’s Disease · doi:10.3233/jad-130458 · Cited by 95
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Olfactory Deficits and Amyloid-β Burden in Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Aging: A PiB PET Study
Bahar‐Fuchs, A., Chételat, G., Villemagne, V. L., Moss, S. A., Pike, K. E., Masters, C. L., Rowe, C. C., & Savage, G. R. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-100696 · Cited by 94
Biomarkers for Early Detection of Alzheimer Pathology
Clark, C. M., Davatzikos, C. A., Borthakur, A., Newberg, A. B., Leight, S., Lee, V. M.-Y., & Trojanowski, J. Q. (2007) · Neurosignals · doi:10.1159/000109754 · Cited by 94
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Japanese Alzheimer's Disease Neuroimaging Initiative: Present status and future
Iwatsubo, T. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.011 · Cited by 94
Hippocampal atrophy rates and CSF biomarkers in elderly APOE 2 normal subjects
Chiang, G. C., Insel, P. S., Tosun, D., Schuff, N., Truran-Sacrey, D., Raptentsetsang, S. T., Jack, C. R., Aisen, P. S., Petersen, R. C., Weiner, M. W., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181ffe4d1 · Cited by 94
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Bi-level multi-source learning for heterogeneous block-wise missing data
Xiang, S., Yuan, L., Fan, W., Wang, Y., Thompson, P. M., & Ye, J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.015 · Cited by 93
Genome-wide association reveals genetic effects on human Aβ 42 and τ protein levels in cerebrospinal fluids: a case control study
Han, M.-R., Schellenberg, G. D., Wang, L.-S., & Initiative, T. A. D. N. (2010) · BMC Neurology · doi:10.1186/1471-2377-10-90 · Cited by 93
Transforming cerebrospinal fluid Aβ42 measures into calculated Pittsburgh compound B units of brain Aβ amyloid
Weigand, S. D., Vemuri, P., Wiste, H. J., Senjem, M. L., Pankratz, V. S., Aisen, P. S., Weiner, M. W., Petersen, R. C., Shaw, L. M., Trojanowski, J. Q., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2010.08.230 · Cited by 93
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High Dimensional Classification of Structural MRI Alzheimer?s Disease Data Based on Large Scale Regularization
Casanova, R., Whitlow, C. T., Wagner, B., Williamson, J. D., Shumaker, S. A., Maldjian, J. A., & Espeland, M. A. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00022 · Cited by 93
Surface fluid registration of conformal representation: Application to detect disease burden and genetic influence on hippocampus
Shi, J., Thompson, P. M., Gutman, B. A., & Wang, Y. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.018 · Cited by 92
The future of amyloid-beta imaging: a tale of radionuclides and tracer proliferation
Klunk, W. E., & Mathis, C. A. (2008) · Current Opinion in Neurology · doi:10.1097/wco.0b013e3283168e1a · Cited by 92
Brain substrates of learning and retention in mild cognitive impairment diagnosis and progression to Alzheimer's disease
Chang, Y., Bondi, M. W., Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., Jacobson, M. W., & Dale, A. M. (2009) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2009.12.024 · Cited by 92
Effect of Complement CR1 on Brain Amyloid Burden During Aging and Its Modification by APOE Genotype
Thambisetty, M., An, Y., Nalls, M., Sojkova, J., Swaminathan, S., Zhou, Y., Singleton, A. B., Wong, D. F., Ferrucci, L., Saykin, A. J., et al. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.08.015 · Cited by 90
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A Disease State Fingerprint for Evaluation of Alzheimer's Disease
Mattila, J., Initiative, A. F. T. A. D. N., Koikkalainen, J. R., Virkki, A., Simonsen, A. H., van Gils, M., Waldemar, G., Soininen, H. S., & Lötjönen, J. M. P. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-110365 · Cited by 89
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Effects of hardware heterogeneity on the performance of SVM Alzheimer's disease classifier
Abdulkadir, A., Mortamet, B., Vemuri, P., Jack, C. R., Krüger, G., & Klöppel, S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.029 · Cited by 88
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Increasing power for voxel-wise genome-wide association studies: The random field theory, least square kernel machines and fast permutation procedures
Ge, T., Feng, J., Hibar, D. P., Thompson, P. M., & Nichols, T. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.012 · Cited by 88
Automated 3D mapping of baseline and 12-month associations between three verbal memory measures and hippocampal atrophy in 490 ADNI subjects
Apostolova, L. G., Morra, J. H., Green, A. E., Hwang, K., Avedissian, C., Woo, E., Cummings, J. L., Toga, A. W., Jack, C. R., Weiner, M. W., et al. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.125 · Cited by 87
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Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression
Silver, M. J., Janoušová, E., Hua, X., Thompson, P. M., & Montana, G. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.002 · Cited by 86
Identification of cis-regulatory variation influencing protein abundance levels in human plasma
Lourdusamy, A., Newhouse, S., Lunnon, K., Proitsi, P., Powell, J. F., Hodges, A., Nelson, S. K., Stewart, A., Williams, S. A., Kłoszewska, I., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds186 · Cited by 86
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Unbiased comparison of sample size estimates from longitudinal structural measures in ADNI
Holland, D., McEvoy, L. K., Dale, A. M., & Initiative, T. A. D. N. (2011) · Human Brain Mapping · doi:10.1002/hbm.21386 · Cited by 85
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome
Cuingnet, R., Rosso, C., Marie Chupin, A., Lehéricy, S., Dormont, D., Benali, H., Samson, Y., & Colliot, O. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.05.007 · Cited by 85
Summary Metrics to Assess Alzheimer Disease–Related Hypometabolic Pattern with 18F-FDG PET: Head-to-Head Comparison
Caroli, A., Prestia, A., Chen, K., Ayutyanont, N., Landau, S. M., Madison, C. M., Haense, C., Herholz, K., Nobili, F. M., Reiman, E. M., et al. (2012) · Journal of Nuclear Medicine · doi:10.2967/jnumed.111.094946 · Cited by 85
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Analysis of Copy Number Variation in Alzheimer’s Disease: The NIALOAD/ NCRAD Family Study
Swaminathan, S., Shen, L., Kim, S., Inlow, M. H., West, J. D., Faber, K. M., Foroud, T., Mayeux, R. P., & Saykin, A. J. (2012) · Current Alzheimer Research · doi:10.2174/156720512802455331 · Cited by 85
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LoAd: A locally adaptive cortical segmentation algorithm
Cardoso, M. J., Clarkson, M. J., Ridgway, G. R., Marc Modat, A., Fox, N. C., & Sebastien Ourselin, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.013 · Cited by 84
Confirmatory factor analysis of the ADNI neuropsychological battery
Initiative, A. D. N., Park, L. Q., Gross, A. L., McLaren, D. G., Pa, J., Johnson, J. K., Mitchell, M. B., & Manly, J. J. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9190-3 · Cited by 84
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Multivariate Protein Signatures of Pre-Clinical Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset
Johnstone, D. M., Milward, E. A., Berretta, R. E., Moscato, P., & Initiative, A. D. N. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034341 · Cited by 83
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Voxel and surface-based topography of memory and executive deficits in mild cognitive impairment and Alzheimer’s disease
Initiative, A. D. N., Nho, K. T., Risacher, S. L., Crane, P. K., DeCarli, C. S., Glymour, M. M., Habeck, C. G., Kim, S., Lee, G. J., Mormino, E. C., et al. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9203-2 · Cited by 83
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Reduced sample sizes for atrophy outcomes in Alzheimer's disease trials: baseline adjustment
Schott, J. M., Bartlett, J. W., Barnes, J., Leung, K. K., Sebastien Ourselin, A., & Fox, N. C. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.011 · Cited by 82
An Improved Model for Disease Progression in Patients From the Alzheimer's Disease Neuroimaging Initiative
Samtani, M. N., Farnum, M. A., Lobanov, V. S., Yang, E., Raghavan, N., DiBernardo, A., Narayan, V. A., & Initiative, A. T. A. D. N. (2011) · The Journal of Clinical Pharmacology · doi:10.1177/0091270011405497 · Cited by 81
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Whole-exome sequencing and imaging genetics identify functional variants for rate of change in hippocampal volume in mild cognitive impairment
Nho, K. T., Corneveaux, J. J., Kim, S., Lin, H., Risacher, S. L., Shen, L., Swaminathan, S., Ramanan, V. K., Liu, Y., Foroud, T., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.24 · Cited by 81
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Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry
Hua, X., Gutman, B. A., Boyle, C. P., Rajagopalan, P., Leow, A., Yanovsky, I., Kumar, A., Toga, A. W., Jack, C. R., Schuff, N. W., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.079 · Cited by 80
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Nonlinear time course of brain volume loss in cognitively normal and impaired elders
Schuff, N. W., Tosun, D., Insel, P. S., Chiang, G. C.-Y., Truran, D., Aisen, P. S. S., Jack, C. R., & Weiner, M. W. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.07.012 · Cited by 80
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Maternal family history is associated with Alzheimer's disease biomarkers
Honea, R. A., Initiative, F. T. A. D. N., Vidoni, E. D., Swerdlow, R. H., & Burns, J. M. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-120676 · Cited by 79
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Design Considerations for Characterizing Psychiatric Trajectories Across the Lifespan: Application to Effects of APOE-ε4 on Cerebral Cortical Thickness in Alzheimer's Disease
Thompson, W. K., Hallmayer, J., O'Hara, R., & Initiative, T. A. D. N. (2011) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2011.10111690 · Cited by 79
Six-month atrophy in MTL structures is associated with subsequent memory decline in elderly controls
Murphy, E. A., Holland, D., Donohue, M., McEvoy, L. K., Hagler, D. J., Dale, A. M., & Brewer, J. B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.016 · Cited by 79
Blood-based biomarkers for Alzheimer's disease: plasma Aβ40 and Aβ42, and genetic variants
Mayeux, R. P., & Schupf, N. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.09.004 · Cited by 79
Brain structure in healthy adults is related to serum transferrin and the H63D polymorphism in the HFE gene
Jahanshad, N., Omid Kohannim, A., Hibar, D. P., Stein, J. L., McMahon, K. L., de Zubicaray, G. I., Medland, S. E., Montgomery, G. W., Whitfield, J. B., Martin, N. G., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1105543109 · Cited by 79
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Cerebrospinal Fluid Abnormalities and Rate of Decline in Everyday Function Across the Dementia Spectrum
Okonkwo, O. C. (2010) · Archives of Neurology · doi:10.1001/archneurol.2010.118 · Cited by 78
White matter hyperintensities and amyloid are independently associated with entorhinal cortex volume among individuals with mild cognitive impairment
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Regional grey matter shrinks in hypertensive individuals despite successful lowering of blood pressure
Jennings, J. R., Mendelson, D. N., Muldoon, M. F., Ryan, C. M., Gianaros, P. J., Raz, N., Aizenstein, H. J., & Initiative, T. A. D. N. (2011) · Journal of Human Hypertension · doi:10.1038/jhh.2011.31 · Cited by 76
Comparing 3 T and 1.5 T MRI for tracking Alzheimer's disease progression with tensor‐based morphometry
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Feature selection using factor analysis for Alzheimer's diagnosis using PET images
Salas-Gonzalez, D., Górriz, J. M., Ramı́rez, J., Illán, I. A., López, M., Segovia, F., Chaves, R., Padilla, P., Puntonet, C. G., & Initiative, T. A. D. N. (2010) · Medical Physics · doi:10.1118/1.3488894 · Cited by 74
Fine Mapping of Genetic Variants in BIN1, CLU, CR1 and PICALM for Association with Cerebrospinal Fluid Biomarkers for Alzheimer's Disease
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Addressing population aging and Alzheimer's disease through the Australian Imaging Biomarkers and Lifestyle study: Collaboration with the Alzheimer's Disease Neuroimaging Initiative
Ellis, K. A., Rowe, C. C., Villemagne, V. L., Martins, R. N., Masters, C. L., Salvado, O., Szoeke, C. E. I., & Ames, D. J. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.009 · Cited by 73
Value of Neuropsychological Tests, Neuroimaging, and Biomarkers for Diagnosing Alzheimer's Disease in Younger and Older Age Cohorts
Schmand, B. A., Eikelenboom, P., van Gool, W. A., & Initiative, T. A. D. N. (2011) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2011.03539.x · Cited by 72
Comparison of Imaging Biomarkers in the Alzheimer Disease Neuroimaging Initiative and the Mayo Clinic Study of Aging
Whitwell, J. L. (2012) · Archives of Neurology · doi:10.1001/archneurol.2011.3029 · Cited by 72
Are apathy and depression independently associated with longitudinal trajectories of cortical atrophy in mild cognitive impairment?
Zahodne, L. B., Gongvatana, A., Cohen, R. A., Ott, B. R., & Tremont, G. (2013) · The American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2013.01.043 · Cited by 72
Blood-Borne Amyloid-β Dimer Correlates with Clinical Markers of Alzheimer's Disease
Villemagne, V. L., Perez, K. A., Pike, K. E., Kok, W. M., Rowe, C. C., White, A. R., Bourgeat, P., Salvado, O., Bedő, J., Hutton, C. A., et al. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.5180-09.2010 · Cited by 72
Focal hemosiderin deposits and β‐amyloid load in the ADNI cohort
Kantarci, K., Gunter, J. L., Tosakulwong, N., Weigand, S. D., Senjem, M. L., Petersen, R. C., Aisen, P. S. S., Jagust, W. J., Weiner, M. W., Jack, C. R., et al. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.10.011 · Cited by 71
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Evaluation of Plasma Proteomic Data for Alzheimer Disease State Classification and for the Prediction of Progression From Mild Cognitive Impairment to Alzheimer Disease
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Enrichment and Stratification for Predementia Alzheimer Disease Clinical Trials
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Sorted by citations

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See all 348

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: Weiner, Michael W.
Veitch, Dallas P.
Beckett, Laurel A.
Cairns, Nigel J.
Cedarbaum, Jesse
Green, Robert C.
Harvey, Danielle
Jack, Clifford R.
Jagust, William
Morris, John C.
Petersen, Ronald C.
Saykin, Andrew J.
Shaw, Leslie
Shen, Li
Schwarz, Adam
Toga, Arthur W.
Trojanowski, John Q.
Venue: Alzheimer's & Dementia
Publisher: Wiley
Published: 2015-06
Volume: 11
Issue: 6
Pages: e1-120
ISSN: 1552-5260 (ISSN-L)
1552-5279
DOI: 10.1016/j.jalz.2014.11.001
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
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
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

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2014 Update of the Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception

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