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

The DOI matches its Crossref record; 565 of the 565 references it cites were found in a registry or an index.

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

Open access

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

Open this paper in CoChat — free account

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.jalz.2014.11.001

Cites

565 of 565 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

1 retracted

Items 401–565 of 565

Disease progression model in subjects with mild cognitive impairment from the Alzheimer's disease neuroimaging initiative: CSF biomarkers predict population subtypes
Samtani, M. N., Raghavan, N., Shi, Y., Novak, G., Farnum, M. A., Lobanov, V. S., Schultz, T., Yang, E., DiBernardo, A., Narayan, V. A., et al. (2012) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.2012.04308.x · Cited by 52
Cerebral atrophy, apolipoprotein E ɛ4, and rate of decline in everyday function among patients with amnestic mild cognitive impairment
Okonkwo, O. C., Alosco, M. L., Jerskey, B. A., Sweet, L. H., Ott, B. R., Tremont, G. N., & Initiative, A. D. N. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.02.003 · Cited by 52
Self-citation
Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets
Robitaille, N., Mouiha, A., Crépeault, B., Valdivia, F., Duchesne, S., & Initiative, T. A. D. N. (2012) · International Journal of Biomedical Imaging · doi:10.1155/2012/347120 · Cited by 52
Parallel but not equivalent: Challenges and solutions for repeated assessment of cognition over time
Gross, A. L., Inouye, S. K., Rebok, G. W., Brandt, J., Crane, P. K., Parisi, J. M., Tommet, D., Bandeen‐Roche, K., Carlson, M. C., & Jones, R. N. (2012) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803395.2012.681628 · Cited by 52
Sample size estimates for well‐powered cross‐sectional cortical thickness studies
Pardoe, H., Abbott, D. F., Jackson, G. D., & Initiative, T. A. D. N. (2012) · Human Brain Mapping · doi:10.1002/hbm.22120 · Cited by 52
The pilot European Alzheimer's Disease Neuroimaging Initiative of the European Alzheimer's Disease Consortium
Frisoni, G. B., Henneman, W. J. P., Weiner, M. W., Scheltens, P., Vellas, B. J., Reynish, E. L., Hudecova, J., Hampel, H. J., Bürger, K., Blennow, K., et al. (2008) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2008.04.009 · Cited by 51
Self-citation
The Value of Hippocampal and Temporal Horn Volumes and Rates of Change in Predicting Future Conversion to AD
Macdonald, K. E., Bartlett, J. W., Leung, K. K., Sebastien Ourselin, A., & Barnes, J. (2012) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e318260a79a · Cited by 51
Assessing the reproducibility of the SienaX and Siena brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans
Cover, K. S., van Schijndel, R. A., van Dijk, B. W., Redolfi, A., Knol, D. L., Frisoni, G. B., Barkhof, F., & Vrenken, H. (2011) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2011.02.012 · Cited by 50
Mapping the effects of ApoE4, age and cognitive status on 18F-florbetapir PET measured regional cortical patterns of beta-amyloid density and growth
Murphy, K. R., Landau, S. M., Choudhury, K. R., Hostage, C. A., Shpanskaya, K. S., Sair, H. I., Petrella, J. R., Wong, T. Z., & Doraiswamy, P. M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.048 · Cited by 50
Application of the National Institute on Aging-Alzheimer's Association AD criteria to ADNI
Lowe, V. J., Peller, P. J., Weigand, S. D., Quintero, C. M., Tosakulwong, N., Vemuri, P., Senjem, M. L., Jordan, L. G., Jack, C. R., Knopman, D. S., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e318295d6cf · Cited by 50
Self-citation
The prevalence of cortical gray matter atrophy may be overestimated in the healthy aging brain.
Burgmans, S., van Boxtel, M. P. J., Vuurman, E. F. P. M., Smeets, F., Gronenschild, E. H. B. M., Uylings, H. B. M., & Jolles, J. (2009) · Neuropsychology · doi:10.1037/a0016161 · Cited by 50
Visual ratings of atrophy in MCI: prediction of conversion and relationship with CSF biomarkers
Lehmann, M., Koedam, E., Barnes, J., Bartlett, J. W., Barkhof, F., Wattjes, M. P., Schott, J. M., Scheltens, P., & Fox, N. C. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.03.010 · Cited by 49
Putting the Alzheimer's cognitive test to the test II: Rasch Measurement Theory
Hobart, J. C., Cano, S., Posner, H. B., Selnes, O. A., Stern, Y., Thomas, R. G., Zajicek, J., & Initiative, A. D. N. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.08.006 · Cited by 49
Self-citation
Putting the Alzheimer's cognitive test to the test I: Traditional psychometric methods
Hobart, J. C., Cano, S., Posner, H. B., Selnes, O. A., Stern, Y., Thomas, R. G., Zajicek, J., & Initiative, A. D. N. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.08.005 · Cited by 48
Self-citation
Whole brain quantitative T2 MRI across multiple scanners with dual echo FSE: Applications to AD, MCI, and normal aging
Bauer, C. M., Jara, H. J., & Killiany, R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.255 · Cited by 47
Design and Application of a Generic Clinical Decision Support System for Multiscale Data
Mattila, J., Koikkalainen, J., Virkki, A., van Gils, M., & Lötjönen, J. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2170986 · Cited by 47
Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia
Initiative, A. D. N., Arbizu, J., Prieto, E., Martínez‐Lage, P., Martí-Climent, J. M., García‐Granero, M., Lamet, I., Pástor, P., Riverol, M., Gómez‐Isla, T., et al. (2013) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-013-2458-z · Cited by 47
Self-citation
Predicting missing biomarker data in a longitudinal study of Alzheimer disease
Lo, R. Y., Jagust, W. J., Aisen, P. S. S., Jack, C. R., Toga, A. W., Beckett, L. A., Gamst, A. C., Soares, H. D., Green, R. C., Montine, T., et al. (2012) · Neurology · doi:10.1212/wnl.0b013e318253d5b3 · Cited by 46
Self-citation
Genetic interactions associated with 12-month atrophy in hippocampus and entorhinal cortex in Alzheimer's Disease Neuroimaging Initiative
Meda, S. A., Koran, M. E. I., Pryweller, J. R., Vega, J. N., & Thornton‐Wells, T. A. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.09.020 · Cited by 46
Fat-mass-related hormone, plasma leptin, predicts brain volumes in the elderly
Rajagopalan, P., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2012) · Neuroreport · doi:10.1097/wnr.0b013e32835c5254 · Cited by 46
Self-citation
Automatic analysis of medial temporal lobe atrophy from structural MRIs for the early assessment of Alzheimer disease
Calvini, P., Chincarini, A., Gemme, G., Penco, M., SQUARCIA, S., Nobili, F. M., Rodriguez, G., Bellotti, R., Catanzariti, E., Cerello, P., et al. (2009) · Medical Physics · doi:10.1118/1.3171686 · Cited by 45
Operationalizing hippocampal volume as an enrichment biomarker for amnestic mild cognitive impairment trials: effect of algorithm, test-retest variability, and cut point on trial cost, duration, and sample size
Yu, P., Sun, J., Wolz, R., Stephenson, D. T., Brewer, J. B., Fox, N. C., Cole, P. E., Jack, C. R., Hill, D., & Schwarz, A. J. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.09.039 · Cited by 45
Self-citation
Neuroimaging Enrichment Strategy for Secondary Prevention Trials in Alzheimer Disease
McEvoy, L. K., Edland, S. D., Holland, D., Hagler, D. J., Roddey, J. C., Fennema‐Notestine, C., Salmon, D. P., Koyama, A. K., Aisen, P. S. S., Brewer, J. B., et al. (2010) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e3181d1b814 · Cited by 45
Self-citation
Validating Predicted Biological Effects of Alzheimer's Disease Associated SNPs Using CSF Biomarker Levels
Kauwe, J. S. K., Cruchaga, C., Bertelsen, S. E., Mayo, K., Latu, W., Nowotny, P., Hinrichs, A. L., Fagan, A. M., Holtzman, D. M., Initiative, A. D. N., et al. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-091711 · Cited by 44
Self-citation
Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps
Silver, M. J., Montana, G., & Initiative, A. D. N. (2012) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1755 · Cited by 44
Self-citation
Variants in PPP3R1 and MAPT are associated with more rapid functional decline in Alzheimer's disease: The Cache County Dementia Progression Study
Peterson, D., Munger, C., Crowley, J., Corcoran, C., Cruchaga, C., Goate, A. M., Norton, M. C., Green, R. C., Munger, R. G., Breitner, J. C. S., et al. (2014) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2013.02.010 · Cited by 44
Self-citation
Using AD biomarker research results for clinical care
Shulman, M. B., Harkins, K. A., Green, R. C., & Karlawish, J. H. (2013) · Neurology · doi:10.1212/wnl.0b013e3182a55f4a · Cited by 44
Self-citation
Identifying Cognitively Healthy Elderly Individuals with Subsequent Memory Decline by Using Automated MR Temporoparietal Volumes
Chiang, G. C.-Y., Insel, P. S., Tosun, D., Schuff, N. W., Truran‐Sacrey, D., Raptentsetsang, S. T., Jack, C. R., Weiner, M. W., & Initiative, A. D. N. (2011) · Radiology · doi:10.1148/radiol.11101637 · Cited by 44
Self-citation
Requiring an amyloid‐β 1‐42 biomarker for prodromal Alzheimer's disease or mild cognitive impairment does not lead to more efficient clinical trials
Schneider, L. S. S., Kennedy, R., Cutter, G., & Initiative, A. D. N. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.07.004 · Cited by 44
Self-citation
Classification of Structural MRI Images in Alzheimer's Disease from the Perspective of Ill-Posed Problems
Casanova, R., Hsu, F., & Mark A. Espeland, F. T. A. D. N. I. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044877 · Cited by 43
Cortical thickness is associated with different apolipoprotein E genotypes in healthy elderly adults
Fan, M., Liu, B., Zhou, Y., Zhen, X., Xu, C., & Jiang, T. (2010) · Neuroscience Letters · doi:10.1016/j.neulet.2010.05.092 · Cited by 43
Dynamics of brain structure and cognitive function in the Alzheimer's disease neuroimaging initiative
Song, X., Mitnitski, A., Zhang, N., Chen, W., & Rockwood, K. J. (2012) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2012-303579 · Cited by 43
In Vivo Assessment of Vesicular Monoamine Transporter Type 2 in Dementia With Lewy Bodies and Alzheimer Disease
Villemagne, V. L. (2011) · Archives of Neurology · doi:10.1001/archneurol.2011.142 · Cited by 43
Impact of SORL1 Single Nucleotide Polymorphisms on Alzheimer’s Disease Cerebrospinal Fluid Markers
Alexopoulos, P., Guo, L., Kratzer, M., Westerteicher, C., Kurz, A., Perneczky, R., & Initiative, A. D. N. (2011) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000332017 · Cited by 42
Self-citation
An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automatic Prognosis of MCI Patients
Aksu, Y., Miller, D. J., Kesidis, G., Bigler, D. C., & Yang, Q. X. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025074 · Cited by 42
Enriching Amnestic Mild Cognitive Impairment Populations for Clinical Trials: Optimal Combination of Biomarkers to Predict Conversion to Dementia
Yu, P., Initiative, A. D. N., Dean, R. A., Hall, S. D., Qi, Y., Sethuraman, G., Willis, B. A., Siemers, E., Martényi, F., Tauscher, J. T., et al. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-120832 · Cited by 42
Self-citation
Value of Diagnostic tests to Predict Conversion to Alzheimer's Disease in Young and Old Patients with Amnestic Mild Cognitive Impairment
Schmand, B. A., Initiative, A. D. N., Eikelenboom, P., & van Gool, W. A. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-111703 · Cited by 41
Self-citation
Influence of magnetic field strength and image registration strategy on voxel‐based morphometry in a study of Alzheimer's disease
Marchewka, A., Kherif, F., Krüger, G., Grabowska, A., Frackowiak, R. S. J., Draganski, B., & Initiative, T. A. D. N. (2013) · Human Brain Mapping · doi:10.1002/hbm.22297 · Cited by 41
CSF and Brain Structural Imaging Markers of the Alzheimer's Pathological Cascade
Yang, X., Tan, M., & Qiu, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0047406 · Cited by 41
Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation
Duc, A. K. H., Marc Modat, A., Leung, K. K., Cardoso, M. J., Barnes, J., Kadir, T., Sebastien Ourselin, A., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0070059 · Cited by 41
Self-citation
Hippocampal Subregions are Differentially Affected in the Progression to Alzheimer's Disease
Greene, S. J., Killiany, R., & Initiative, T. A. D. N. (2011) · The Anatomical Record · doi:10.1002/ar.21493 · Cited by 40
Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer's disease
Markiewicz, P. J., Matthews, J. C., Declerck, J. M., & Herholz, K. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.020 · Cited by 40
Autopsy consent, brain collection, and standardized neuropathologic assessment of ADNI participants: The essential role of the Neuropathology Core
Cairns, N. J., Taylor‐Reinwald, L., Morris, J. C., & Initiative, A. D. N. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.012 · Cited by 39
Self-citation
Multivariate Statistical Analysis of Deformation Momenta Relating Anatomical Shape to Neuropsychological Measures
Singh, N., Fletcher, P. T., Preston, J. S., Ha, L. K., King, R., Marron, J. S., Wiener, M., & Joshi, S. C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15711-0_66 · Cited by 39
The Effect of Subsyndromal Symptoms of Depression and White Matter Lesions on Disability for Individuals with Mild Cognitive Impairment
Mackin, R. S., Insel, P. S., Tosun, D., Mueller, S. G., Schuff, N. W., Truran‐Sacrey, D., Raptentsetsang, S. T., Lee, J., Jack, C. R., Aisen, P. S. S., et al. (2013) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2013.01.021 · Cited by 38
Self-citation
Basics of Multivariate Analysis in Neuroimaging Data
Habeck, C. G. (2010) · Journal of Visualized Experiments · doi:10.3791/1988 · Cited by 38
Subgroup of ADNI normal controls characterized by atrophy and cognitive decline associated with vascular damage.
Nettiksimmons, J., Beckett, L. A., Schwarz, C. G., Carmichael, O. T., Fletcher, E. M., & DeCarli, C. S. (2013) · Psychology and Aging · doi:10.1037/a0031063 · Cited by 37
Self-citation
Approaches and Costs for Sharing Clinical Research Data
Wilhelm, E. E., Oster, E. F., & Shoulson, I. (2014) · JAMA · doi:10.1001/jama.2014.850 · Cited by 36
The informatics core of the Alzheimer's Disease Neuroimaging Initiative
Toga, A. W., Crawford, K. L., & Initiative, A. D. N. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.001 · Cited by 35
Self-citation
Epistatic Genetic Effects among Alzheimer’s Candidate Genes
Hohman, T. J., Koran, M. E. I., Thornton‐Wells, T. A., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080839 · Cited by 35
Self-citation
ODVBA: Optimally-Discriminative Voxel-Based Analysis
Zhang, T., & Davatzikos, C. A. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2114362 · Cited by 35
Modeling the heterogeneity in risk of progression to Alzheimer's disease acrosscognitive profiles in mild cognitive impairment
Tatsuoka, C. M., Tseng, H., Jaeger, J., Váradi, F., Smith, M. A., Yamada, T., Smyth, K. A., Lerner, A. J., & Initiative, T. A. D. N. (2013) · Alzheimer s Research & Therapy · doi:10.1186/alzrt168 · Cited by 35
A commonly carried genetic variant in the delta opioid receptor gene, OPRD1, is associated with smaller regional brain volumes: Replication in elderly and young populations
Roussotte, F. F., Jahanshad, N., Hibar, D. P., Sowell, E. R., Omid Kohannim, A., Marina Barysheva, A., Hansell, N. K., McMahon, K. L., de Zubicaray, G. I., Montgomery, G. W., et al. (2013) · Human Brain Mapping · doi:10.1002/hbm.22247 · Cited by 35
Self-citation
Simultaneous Fine and Coarse Diffeomorphic Registration: Application to Atrophy Measurement in Alzheimer’s Disease
Risser, L., Vialard, F., Wolz, R., Holm, D. D., & Rueckert, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15745-5_75 · Cited by 34
Distinct profiles of brain and cognitive changes in the very old with Alzheimer disease
Stricker, N. H., Chang, Y., Fennema‐Notestine, C., Delano‐Wood, L., Salmon, D. P., Bondi, M. W., Dale, A. M., & Initiative, A. D. N. (2011) · Neurology · doi:10.1212/wnl.0b013e31822b0004 · Cited by 34
Self-citation
Predicting AD Conversion: Comparison between Prodromal AD Guidelines and Computer Assisted PredictAD Tool
Liu, Y., Mattila, J., Muñoz‐Ruiz, M. Á., Paajanen, T. I., Koikkalainen, J. R., van Gils, M., Herukka, S., Waldemar, G., Lötjönen, J. M. P., Soininen, H. S., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055246 · Cited by 34
Self-citation
Longitudinal stability of subsyndromal symptoms of depression in individuals with mild cognitive impairment: relationship to conversion to dementia after 3 years
Mackin, R. S., Insel, P. S., Aisen, P. S. S., Geda, Y. E., & Weiner, M. W. (2011) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.2713 · Cited by 34
Self-citation
The National Institute on Aging-Alzheimer’s Association research criteria for mild cognitive impairment due to Alzheimer’s disease: predicting the outcome
Guo, L., Alexopoulos, P., Eisele, T., Wagenpfeil, S., Kurz, A., & Perneczky, R. (2012) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-012-0349-0 · Cited by 33
Use of Alzheimer Disease Biomarkers
Petersen, R. C., & Trojanowski, J. Q. (2009) · JAMA · doi:10.1001/jama.2009.1073 · Cited by 33
Self-citation
Amyloid Imaging in Alzheimer’s Disease and Other Dementias
Fodero‐Tavoletti, M. T., Cappai, R., McLean, C., Pike, K. E., Adlard, P. A., Cowie, T., Connor, A. R., Masters, C. L., Rowe, C. C., & Villemagne, V. L. (2009) · Brain Imaging and Behavior · doi:10.1007/s11682-009-9067-2 · Cited by 33
A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology
Grzegorczyk, M. A., & Husmeier, D. (2012) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/1544-6115.1761 · Cited by 33
Imaging and biomarkers will be used for detection and monitoring progression of early Alzheimer's disease
Weiner, M. W. (2009) · The journal of nutrition health & aging · doi:10.1007/s12603-009-0032-y · Cited by 32
Self-citation
Medication for Alzheimer’s Disease and Associated Fall Hazard: A Retrospective Cohort Study from the Alzheimer’s Disease Neuroimaging Initiative
Epstein, N. U., Guo, R., Farlow, M. R., Singh, J. P., & Fisher, M. A. (2013) · Drugs & Aging · doi:10.1007/s40266-013-0143-3 · Cited by 32
CETP polymorphisms associate with brain structure, atrophy rate, and Alzheimer’s disease risk in an APOE-dependent manner
Initiative, A. D. N., Murphy, E. A., Roddey, J. C., McEvoy, L. K., Holland, D., Hagler, D. J., Dale, A. M., & Brewer, J. B. (2011) · Brain Imaging and Behavior · doi:10.1007/s11682-011-9137-0 · Cited by 32
Self-citation
Cortical Surface Reconstruction via Unified Reeb Analysis of Geometric and Topological Outliers in Magnetic Resonance Images
Shi, Y., Lai, R., & Toga, A. W. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2224879 · Cited by 32
Self-citation
Genetic, Structural and Functional Imaging Biomarkers for Early Detection of Conversion from MCI to AD
Singh, N., Wang, A. Y., Sankaranarayanan, P., Fletcher, P., & Joshi, S. (2012) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-642-33415-3_17 · Cited by 32
Identifying Informative Imaging Biomarkers via Tree Structured Sparse Learning for AD Diagnosis
Initiative, T. A. D. N., Liu, M., Zhang, D., & Shen, D. G. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9218-x · Cited by 31
Coevolution of brain structures in amnestic mild cognitive impairment
Carmichael, O. T., McLaren, D. G., Tommet, D., Mungas, D., & Jones, R. N. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.029 · Cited by 31
Increased CNV-Region deletions in mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects in the ADNI sample
Guffanti, G., Torri, F., Rasmussen, J. M., Clark, A. P., Lakatos, A., Turner, J. A., Fallon, J. H., Saykin, A. J., Weiner, M. W., Vawter, M. P., et al. (2013) · Genomics · doi:10.1016/j.ygeno.2013.04.004 · Cited by 31
Self-citation
Genetic interactions found between calcium channel genes modulate amyloid load measured by positron emission tomography
Koran, M. E. I., Hohman, T. J., & Thornton‐Wells, T. A. (2013) · Human Genetics · doi:10.1007/s00439-013-1354-8 · Cited by 31
Hippocampal Surface Mapping of Genetic Risk Factors in AD via Sparse Learning Models
ADNI, Wan, J., Kim, S., Inlow, M. H., Nho, K. T., Swaminathan, S., Risacher, S. L., Fang, S., Weiner, M. W., Beg, M. F., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23629-7_46 · Cited by 31
Self-citation
Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction
Shen, L., Qi, Y., Kim, S., Nho, K. T., Wan, J., Risacher, S. L., Saykin, A. J., & ADNI, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15711-0_76 · Cited by 30
Self-citation
An MRI Brain Atrophy and Lesion Index to Assess the Progression of Structural Changes in Alzheimer's Disease, Mild Cognitive Impairment, and Normal Aging: A Follow-Up Study
Zhang, N., Initiative, A. D. N., Song, X., Zhang, Y., Chen, W., D’Arcy, R. C. N., Darvesh, S., Fisk, J. D., & Rockwood, K. J. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-0048 · Cited by 30
Self-citation
Establishing Magnetic Resonance Images Orientation for the EADC‐ADNI Manual Hippocampal Segmentation Protocol
Boccardi, M., Bocchetta, M., Apostolova, L. G., Preboske, G. M., Robitaille, N., Pasqualetti, P., Collins, D. L., Duchesne, S., Jack, C. R., & Frisoni, G. B. (2013) · Journal of Neuroimaging · doi:10.1111/jon.12065 · Cited by 30
Self-citation
Circadian Clock Gene Polymorphisms and Sleep–Wake Disturbance in Alzheimer Disease
Yesavage, J. A., Noda, A. M., Hernandez, B., Friedman, L., Cheng, J. J., Tinklenberg, J. R., Hallmayer, J. F., O'Hara, R. M., David, R., Robert, P. H., et al. (2011) · American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e31820d92b2 · Cited by 30
Predicting temporal lobe volume on MRI from genotypes using L1-L2 regularized regression
Omid Kohannim, A., Hibar, D. P., Jahanshad, N., Stein, J. L., Hua, X., Toga, A. W., Jack, C. R., Weinen, M. W., & Thompson, P. M. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235766 · Cited by 30
Self-citation
A Geographic Cline of Skull and Brain Morphology among Individuals of European Ancestry
Bakken, T. E., Dale, A. M., Schork, N. J., & Initiative, A. D. N. (2011) · Human Heredity · doi:10.1159/000330168 · Cited by 29
Self-citation
Modeling of Functional Assessment Questionnaire (FAQ) as continuous bounded data from the ADNI database
Ito, K., Hutmacher, M. M., & Corrigan, B. (2012) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-012-9271-3 · Cited by 29
Cortical gray matter atrophy in healthy aging cannot be explained by undetected incipient cognitive disorders: A comment on Burgmans et al. (2009).
Fjell, A. M., Initiative, A. D. N., Westlye, L. T., Espeseth, T., Reinvang, I., Dale, A. M., Holland, D., & Walhovd, K. B. (2010) · Neuropsychology · doi:10.1037/a0018827 · Cited by 29
Self-citation
An MRI-Based Semiquantitative Index for the Evaluation of Brain Atrophy and Lesions in Alzheimer’s Disease, Mild Cognitive Impairment and Normal Aging
Chen, W., Song, X., Zhang, Y., Darvesh, S., Zhang, N., D’Arcy, R. C. N., Black, S. E., & Rockwood, K. J. (2010) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000319537 · Cited by 29
Genome-wide association identifies genetic variants associated with lentiform nucleus volume in N = 1345 young and elderly subjects
Initiative, T. A. D. N., Hibar, D. P., Stein, J. L., Ryles, A. B., Omid Kohannim, A., Jahanshad, N., Medland, S. E., Hansell, N. K., McMahon, K. L., de Zubicaray, G. I., et al. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9199-7 · Cited by 29
Self-citation
Topology-corrected segmentation and local intensity estimates for improved partial volume classification of brain cortex in MRI
Rueda, A., Acosta, O., Couprie, M., Bourgeat, P., Fripp, J., Dowson, N., Romero, E., & Salvado, O. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.02.020 · Cited by 28
Interactions between GSK3β and amyloid genes explain variance in amyloid burden
Hohman, T. J., Koran, M. E. I., & Thornton‐Wells, T. A. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.08.032 · Cited by 28
Apolipoprotein E ɛ2 and Functional Decline in Amnestic Mild Cognitive Impairment and Alzheimer Disease
Bonner‐Jackson, A., Okonkwo, O. C., & Tremont, G. N. (2011) · American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e3182203c32 · Cited by 28
Tensor-based morphometry with stationary velocity field diffeomorphic registration: Application to ADNI
Bossa, M. N., Zacur, E., & Olmos, S. C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.061 · Cited by 28
White Matter Abnormalities and Structural Hippocampal Disconnections in Amnestic Mild Cognitive Impairment and Alzheimer’s Disease
Rowley, J., Fonov, V., Wu, O., Eskildsen, S. F., Schoemaker, D., Wu, L., Mohades, S., Shin, M., Sziklas, V., Cheewakriengkrai, L., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074776 · Cited by 27
Self-citation
Semi-Supervised Multimodal Relevance Vector Regression Improves Cognitive Performance Estimation from Imaging and Biological Biomarkers
Initiative, T. A. D. N., Cheng, B., Zhang, D., Chen, S., Kaufer, D., & Shen, D. G. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9180-7 · Cited by 27
The relative efficiency of time-to-threshold and rate of change in longitudinal data
Donohue, M., Gamst, A. C., Thomas, R. G., Xu, R., Beckett, L. A., Petersen, R. C., Weiner, M. W., & Aisen, P. S. S. (2011) · Contemporary Clinical Trials · doi:10.1016/j.cct.2011.04.007 · Cited by 27
Self-citation
Using CSF biomarkers to replicate genetic associations in Alzheimer's disease
Schott, J. M. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.02.008 · Cited by 27
Alzheimer's Disease Neuroimaging Initiative in Europe
Frisoni, G. B. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.005 · Cited by 27
Neuroimaging to assess safety and efficacy of AD therapies
Hill, D. (2009) · Expert Opinion on Investigational Drugs · doi:10.1517/13543780903381320 · Cited by 26
Functional activity maps based on significance measures and Independent Component Analysis
Martínez-Murcia, F. J., Górriz, J. M., Ramı́rez, J., Puntonet, C. G., & Illán, I. A. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.03.015 · Cited by 26
Mapping the Genetic Variation of Regional Brain Volumes as Explained by All Common SNPs from the ADNI Study
Bryant, C., Giovanello, K. S., Ibrahim, J. G., Jing, C., Shen, D. G., Peterson, B. S., Hongtu Zhu, B., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071723 · Cited by 26
Self-citation
ISOMAP induced manifold embedding and its application to Alzheimer's disease and mild cognitive impairment.
Park, H. (2012) · Neuroscience Letters · doi:10.1016/j.neulet.2012.02.016 · Cited by 26
Empirical derivation of the reference region for computing diagnostic sensitive 18fluorodeoxyglucose ratios in Alzheimer's disease based on the ADNI sample
Rasmussen, J. M., Lakatos, A., van Erp, T. G. M., Kruggel, F., Keator, D. B., Fallon, J., Macciardi, F. M., & Potkin, S. G. (2011) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2011.09.008 · Cited by 26
Cortical signatures of cognition and their relationship to Alzheimer’s disease
Initiative, F. T. A. D. N., Gross, A. L., Manly, J. J., Pa, J., Johnson, J. K., Park, L. Q., Mitchell, M. B., Melrose, R. J., Inouye, S. K., & McLaren, D. G. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9180-5 · Cited by 25
Self-citation
Dimensionality reduced cortical features and their use in predicting longitudinal changes in Alzheimer's disease
Park, H., Yang, J.-J., Seo, J., & Lee, J. (2013) · Neuroscience Letters · doi:10.1016/j.neulet.2013.06.042 · Cited by 25
Alzheimer's Disease Neuroimaging Initiative special issue
Frisoni, G. B., & Weiner, M. W. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.006 · Cited by 25
Self-citation
Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time‐Varying Covariates
Skup, M., Zhu, H., & Zhang, H. (2012) · Biometrics · doi:10.1111/j.1541-0420.2012.01767.x · Cited by 25
The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer's Disease
Wu, X., Li, J., Ayutyanont, N., Protas, H. D., Jagust, W. J., Fleisher, A., Reiman, E. M., Yao, L., & Chen, K. (2012) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.141 · Cited by 25
Self-citation
Genetic architecture of resilience of executive functioning
Initiative, A. D. N., Mukherjee, S., Kim, S., Gibbons, L. E., Nho, K. T., Risacher, S. L., Glymour, M. M., Habeck, C. G., Lee, G. J., Mormino, E. C., et al. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9184-1 · Cited by 25
Self-citation
Statistical analysis of relative pose information of subcortical nuclei: Application on ADNI data
Bossa, M. N., Zacur, E., & Olmos, S. C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.078 · Cited by 24
Higher education is not associated with greater cortical thickness in brain areas related to literacy or intelligence in normal aging or mild cognitive impairment
Pillai, J. A., McEvoy, L. K., Hagler, D. J., Holland, D., Dale, A. M., Salmon, D. P., Galasko, D., & Fennema‐Notestine, C. (2012) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803395.2012.702733 · Cited by 23
A Two-Study Comparison of Clinical and MRI Markers of Transition from Mild Cognitive Impairment to Alzheimer’s Disease
Devanand, D. P., Liu, X., Brown, P. J., Huey, E. D., Stern, Y., & Pelton, G. H. (2012) · International Journal of Alzheimer s Disease · doi:10.1155/2012/483469 · Cited by 23
4D Segmentation of Brain MR Images with Constrained Cortical Thickness Variation
Wang, L., Shi, F., Li, G., & Shen, D. G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064207 · Cited by 23
Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis
Zhang, D., Liu, J., & Shen, D. G. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2_33 · Cited by 22
Tree-Guided Sparse Coding for Brain Disease Classification
Liu, M., Zhang, D., Yap, P., & Shen, D. G. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2_30 · Cited by 22
Predicting brain activity using a Bayesian spatial model
Derado, G., Bowman, F. D., Zhang, L., & Initiative, A. T. A. D. N. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280212448972 · Cited by 21
Self-citation
Alzheimers Disease Drug Development: Old Problems Require New Priorities
Becker, R. E., & Greig, N. H. (2008) · CNS & Neurological Disorders - Drug Targets · doi:10.2174/187152708787122950 · Cited by 21
Effects of BDNF Val66Met polymorphism on brain metabolism in Alzheimer's disease
Xu, C., Wang, Z., Fan, M., Liu, B., Song, M., Zhen, X., & Jiang, T. (2010) · Neuroreport · doi:10.1097/wnr.0b013e32833ccaf4 · Cited by 21
Differences in Medication Use in the Alzheimerʼs Disease Neuroimaging Initiative
Epstein, N. U., Saykin, A. J., Risacher, S. L., Gao, S., & Farlow, M. R. (2010) · Drugs & Aging · doi:10.2165/11538260-000000000-00000 · Cited by 21
Self-citation
Applications of Neuroimaging to Disease-Modification Trials in Alzheimer’s Disease
Fleisher, A., Donohue, M., Chen, K., Brewer, J. B., Aisen, P. S. S., & Initiative, T. A. D. N. (2009) · Behavioural Neurology · doi:10.1155/2009/836437 · Cited by 21
Self-citation
Maximizing the Alzheimer's Disease Neuroimaging Initiative II
Carrillo, M. C., Sanders, C. A., & Katz, R. G. (2009) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2009.02.005 · Cited by 20
Genetic association of complement receptor 1 polymorphism rs3818361 in Alzheimer's disease
Antúnez, C., Boada, M., López‐Arrieta, J., Moreno‐Rey, C., Hernández, I., Marín, J., Gayán, J., Initiative, A. D. N., González‐Pérez, A., Real, L. M., et al. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.05.2412 · Cited by 20
Self-citation
Cortical Folding Analysis on Patients with Alzheimer’s Disease and Mild Cognitive Impairment
Cash, D. M., Melbourne, A., Marc Modat, A., Cardoso, M. J., Clarkson, M. J., Fox, N. C., & Sebastien Ourselin, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2_36 · Cited by 19
Small world network measures predict white matter degeneration in patients with early-stage mild cognitive impairment
Nir, T. M., Jahanshad, N., Jack, C. R., Weiner, M. W., Toga, A. W., & Thompson, P. M. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235831 · Cited by 19
Self-citation
Combining Structural Brain Changes Improves the Prediction of Alzheimer’s Disease and Mild Cognitive Impairment
Zhang, N., Song, X., & Zhang, Y. (2012) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000339364 · Cited by 19
A novel MRI‐compatible brain ventricle phantom for validation of segmentation and volumetry methods
Khan, A. F., Drozd, J. J., Moreland, R., Ta, R. M., BORRIE, M. J., Bartha, R., & Initiative, A. T. A. D. N. (2012) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.23612 · Cited by 18
Self-citation
Verification of predicted robustness and accuracy of multivariate analysis
Markiewicz, P. J., Matthews, J. C., Declerck, J. M., & Herholz, K. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.036 · Cited by 18
Advances in molecular imaging for the diagnosis of dementia
Okamura, N., Fodero‐Tavoletti, M. T., Kudo, Y., Rowe, C. C., Furumoto, S., Arai, H., Masters, C. L., Yanai, K., & Villemagne, V. L. (2009) · Expert Opinion on Medical Diagnostics · doi:10.1517/17530050903133790 · Cited by 18
Toward a Dynamic Biomarker Model in Alzheimer's Disease
Mouiha, A., Initiative, T. A. D. N., & Duchesne, S. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-111367 · Cited by 18
Increasing the Predictive Accuracy of Amyloid-β Blood-Borne Biomarkers in Alzheimer's Disease
Watt, A. D., Perez, K. A., Faux, N. G., Pike, K. E., Rowe, C. C., Bourgeat, P., Salvado, O., Masters, C. L., Villemagne, V. L., & Barnham, K. J. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-101722 · Cited by 17
11C-PiB PET ABri Imaging in Worster-Drought Syndrome (Familial British Dementia): A Case Report
Villemagne, V. L., Pike, K. E., Pejoska, S., Boyd, A., Power, M., Jones, G. R., Masters, C. L., & Rowe, C. C. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-1241 · Cited by 17
Assessing the reliability to detect cerebral hypometabolism in probable Alzheimer's disease and amnestic mild cognitive impairment
Wu, X., Chen, K., Yao, L., Ayutyanont, N., Langbaum, J. B. S., Fleisher, A., Reschke, C., Lee, W., Liu, X., Alexander, G. E., et al. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.07.030 · Cited by 17
Self-citation
Registration of Images With Varying Topology Using Embedded Maps
Li, X., Long, X., Laurienti, P. J., & Wyatt, C. L. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2178609 · Cited by 16
Applications of neuroimaging to disease-modification trials in Alzheimer's disease.
Fleisher, A., Donohue, M., Chen, K., et al. (2009) · PubMed · doi:10.3233/ben-2009-0241 · Cited by 16
Consistent 4D Cortical Thickness Measurement for Longitudinal Neuroimaging Study
Initiative, T. A. D. N., Li, Y., Wang, Y., Xue, Z., Shi, F., Lin, W., & Shen, D. G. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15745-5_17 · Cited by 15
Which Operation for Trigeminal Neuralgia
Haines, S. J., & Chittum, C. J. (2003) · Practical Neurology · doi:10.1046/j.1474-7766.2003.00120.x · Cited by 15
Application of multidimensional scaling to quantify shape in Alzheimer's disease and its correlation with Mini Mental State Examination: a feasibility study.
Park, H., & Seo, J. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.10.019 · Cited by 15
For debate: substituting placebo controls in long-term Alzheimer's prevention trials
Spiegel, R., Berres, M., Miserez, A. R., Monsch, A. U., & Initiative, T. A. D. N. (2011) · Alzheimer s Research & Therapy · doi:10.1186/alzrt68 · Cited by 15
Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment
Simonsen, A. H., Mattila, J., Hejl, A., Frederiksen, K. S., Herukka, S., Hallikainen, M., van Gils, M., Lötjönen, J. M. P., Soininen, H. S., & Waldemar, G. (2012) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000345554 · Cited by 14
Striving for an Integrated drug-development Process for Neurodegeneration: the Coalition Against Major Diseases
Romero, K., Corrigan, B., Neville, J., Kopko, S., & Cantillon, M. (2011) · Neurodegenerative Disease Management · doi:10.2217/nmt.11.51 · Cited by 14
Dysexecutive and amnesic AD subtypes defined by single indicator and modern psychometric approaches: relationships with SNPs in ADNI
Initiative, A. D. N., Mukherjee, S., Trittschuh, E. H., Gibbons, L. E., Mackin, R. S., Saykin, A. J., & Crane, P. K. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9207-y · Cited by 14
Self-citation
Residual vectors for Alzheimer disease diagnosis and prognostication
Clark, D. G. (2011) · Brain and Behavior · doi:10.1002/brb3.19 · Cited by 13
Geriatric Medicine, Japanese Alzheimer's Disease Neuroimaging Initiative and Biomarker Development
Arai, H., Okamura, N., Furukawa, K., & Kudo, Y. (2010) · The Tohoku Journal of Experimental Medicine · doi:10.1620/tjem.221.87 · Cited by 13
Software Tool for Improved Prediction of Alzheimer’s Disease
Soininen, H. S., Mattila, J., Koikkalainen, J. R., van Gils, M., Simonsen, A. H., Waldemar, G., Rueckert, D., Thurfjell, L., Lötjönen, J. M. P., & Initiative, A. D. N. (2011) · Neurodegenerative Diseases · doi:10.1159/000332600 · Cited by 13
Self-citation
Discrimination of AD and normal subjects from MRI: Anatomical versus statistical regions
Peláez‐Coca, M. D., Bossa, M. N., & Olmos, S. C. (2010) · Neuroscience Letters · doi:10.1016/j.neulet.2010.10.007 · Cited by 13
Establishing the psychometric underpinning of cognition measures for clinical trials of Alzheimer's disease and its precursors: A new approach
Posner, H. B., Cano, S., Carrillo, M. C., Selnes, O. A., Stern, Y., Thomas, R. G., Zajicek, J., Hobart, J. C., & Initiative, A. D. N. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.10.008 · Cited by 13
Self-citation
Safety profile of Alzheimer's disease populations in Alzheimer's Disease Neuroimaging Initiative and other 18‐month studies
Henley, D. B., Sundell, K. L., Sethuraman, G., & Siemers, E. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.05.2413 · Cited by 13
The Alzheimer's Disease Neuroimaging Initiative: Perspectives of the Industry Scientific Advisory Board
Schmidt, M. E., Siemers, E., Snyder, P. J., Potter, W. Z., Cole, P. E., & Soares, H. D. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.010 · Cited by 11
Data Fusion and Feature Selection for Alzheimer’s Diagnosis
Lemoine, B., Rayburn, S., & Benton, R. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15314-3_30 · Cited by 10
Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease
van Gils, M., Koikkalainen, J. R., Mattila, J., Herukka, S., Lötjönen, J. M. P., & Soininen, H. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626311 · Cited by 10
The effect of reference panels and software tools on genotype imputation.
Nho, K. T., Shen, L., Kim, S., Swaminathan, S., Risacher, S. L., & Saykin, A. J. (2011) · PubMed Central · Cited by 9
Self-citation
Standing on the Shoulders of Giants: Improving Medical Image Segmentation via Bias Correction
Wang, H., Das, S. R., Pluta, J. B., Craige, C. P., Altinay, M. I., Avants, B., Weiner, M. W., Mueller, S. G., & Yushkevich, P. A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15711-0_14 · Cited by 9
Self-citation
ICA-Based Automatic Classification of Magnetic Resonance Images from ADNI Data
Yang, W., Chen, X., Xie, H., & Huang, X. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15615-1_41 · Cited by 9
Obesity and the brain: a possible genetic link
Bertram, L., & Heekeren, H. R. (2010) · Alzheimer s Research & Therapy · doi:10.1186/alzrt51 · Cited by 9
Basics of Multivariate Analysis in Neuroimaging Data
Habeck, C. G. (2010) · Journal of Visualized Experiments · doi:10.3791/1988-v · Cited by 9
Microcephaly Genes and Risk of Late-onset Alzheimer Disease
Erten‐Lyons, D., Wilmot, B., Anur, P., McWeeney, S. K., Westaway, S. K., Silbert, L. C., Kramer, P., & Kaye, J. A. (2011) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0b013e31820a1d32 · Cited by 8
Relative contributions of biomarkers in Alzheimer’s disease
Haight, T. J., & Jagust, W. (2012) · Annals of Epidemiology · doi:10.1016/j.annepidem.2012.09.004 · Cited by 8
Self-citation
[Alzheimer's disease neuroimaging initiative and mild cognitive impairment].
Arai, H. (2007) · PubMed · Cited by 7
Advanced psychometric analysis and the Alzheimer’s Disease Neuroimaging Initiative: reports from the 2011 Friday Harbor conference
Mungas, D., Crane, P. K., Gibbons, L. E., Manly, J. J., Glymour, M. M., & Jones, R. N. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9211-2 · Cited by 7
Using blood markers for Alzheimer disease in clinical practice?
O’Bryant, S. E. (2012) · Neurology · doi:10.1212/wnl.0b013e318266fd21 · Cited by 7
Feed-forward hierarchical model of the ventral visual stream applied to functional brain image classification
Keator, D. B., Fallon, J. H., Lakatos, A., Fowlkes, C. C., Potkin, S. G., Ihler, A., & Initiative, A. D. N. (2012) · Human Brain Mapping · doi:10.1002/hbm.22149 · Cited by 7
Self-citation
Generating a Minimal Set of Templates for the Hippocampal Region in MR Neuroimages
Cataldo, R., Agrusti, A., Nunzio, G. D., Carlà, A., Mitri, I. D., Favetta, M., Quarta, M., Monno, L., Rei, L., Fiorina, E., et al. (2012) · Journal of Neuroimaging · doi:10.1111/j.1552-6569.2012.00713.x · Cited by 6
Vascular Risk Factors and Cardiovascular Outcomes in the Alzheimer’s Disease Neuroimaging Initiative
Epstein, N. U., Xie, H. W., Ruland, S. D., & Pandey, D. K. (2012) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/1533317512449730 · Cited by 6
Bioprofile Analysis: A New Approach for the Analysis of Biomedical Data in Alzheimer's Disease
Escudero, J., Initiative, A. D. N., Ifeachor, E., & Zajicek, J. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-121024 · Cited by 6
Self-citation
[Overview on Alzheimer's disease].
Ihara, Y. (2007) · PubMed · Cited by 6
[Neuropathology of mild cognitive impairment Alzheimer's disease].
Murayam, S., & Saito, Y. (2007) · PubMed · Cited by 6
[Neuroimaging in mild cognitive impairment].
Fukuyama, H. (2006) · PubMed · Cited by 5
A web‐based normative data tool for assessing cognitive performance in healthy older Australians
McBride, S. J., Szoeke, C. E. I., Good, N. M., Ames, D. J., Martins, R. N., Masters, C. L., Maruff, P. T., Rowe, C. C., Savage, G. R., & Ellis, K. A. (2011) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.2011.tb02936.x · Cited by 5
Effects of T2-weighted MRI based cranial volume measurements on studies of the aging brain
Vuong, P., Drucker, D., Schwarz, C. G., Fletcher, E., DeCarli, C. S., & Carmichael, O. T. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2006727 · Cited by 5
Lack of Association Between COMT Polymorphisms and Apathy in Alzheimer's Disease
David, R., Initiative1, A. F. T. A. D. N., Friedman, L., Mulin, E., Noda, A. M., Duff, F. L., Kennedy, Q., Garcia, R., Robert, P. H., Yesavage, J. A., et al. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-110491 · Cited by 5
Self-citation
Advances in structural and molecular neuroimaging in Alzheimer's disease
Ellis, K. A., Rowe, C. C., Szoeke, C. E. I., Villemagne, V. L., Ames, D. J., Chételat, G., Martins, R. N., Masters, C. L., Fripp, J., Acosta, O., et al. (2011) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.2011.tb02938.x · Cited by 4
P4‐133: Genome‐wide association study of age at onset of Alzheimer's disease
Harold, D. H., Hollingworth, P., Hamshere, M. L., Holmans, P., Abraham, R., Sims, R., Gerrish, A., Lovestone, S., Brayne, C., Gill, M., et al. (2009) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2009.04.801 · Cited by 3
Plasma multianalyte profiling in mild cognitive impairment and Alzheimer disease
(2012) · Neurology · doi:10.1212/wnl.0b013e3182771e89 · Cited by 3

Research works printed but not linked: 14

Trojanowski J, 2004, Searching for the biomarkers of Alzheimer's, Pract Neurol, 3, 30
Mueller SG, 2006, Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative, Cogn Dement, 5, 56
Cruchaga C, 2009, SNPs in the regulatory subunit of calcineurin are associated with CSF tau protein levels, brain mRNA levels, Alzheimers Dement, 5, P471
LiY WangY WuG ShiF ZhouL LinW et al.Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features.Neurobiol Aging (in press).
Thambisetty M, 2012, Effect of Complement CR1 on Brain Amyloid Burden During Aging and Its Modification byAPOEGenotype, Biol Psychiatry, 73, 334
Weiner MW, 2012, The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception, Alzheimers Dement, 8, S1
10.1016/j.bbadis.2011.09.008 · DOI
10.1016/j.neuroimage.2009.05.056 · DOI
10.3389/fnins.2012.00115 · DOI
10.1007/s11682-012-9184-1 · DOI
10.1007/s11682-012-9196-x · DOI
10.3389/fgene.2011.00073 · DOI
10.1016/j.neuroimage.2011.03.077 · DOI
10.2174/156720512802455331 · DOI

Sorted by citations

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622
Changes in the Synaptic Proteome in Tauopathy and Rescue of Tau-Induced Synapse Loss by C1q Antibodies
Dejanovic, B., Huntley, M. A., De Mazière, A., Meilandt, W. J., Wu, T., Srinivasan, K., Jiang, Z., Gandham, V., Friedman, B. A., Ngu, H., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.014 · Cited by 517
The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement
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 523
Self-citation
Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative
Veitch, D. P., Weiner, M. W., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., Morris, J. C., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.08.005 · Cited by 454
Self-citation
Common polygenic variation enhances risk prediction for Alzheimer’s disease
Escott-Price, V., Sims, R., Bannister, C., Harold, D., Vronskaya, M., Majounie, E., Badarinarayan, N., GERAD/PERADES, consortia, I., Morgan, K., et al. (2015) · Brain · doi:10.1093/brain/awv268 · Cited by 414

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks