Review

. 2022 Apr;18(4):824-857.

doi: 10.1002/alz.12422. Epub 2021 Sep 28.

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

Dallas P Veitch^{1

2}, Michael W Weiner^{1

3

4

5

6}, Paul S Aisen⁷, Laurel A Beckett⁸, Charles DeCarli⁹, Robert C Green¹⁰, Danielle Harvey⁸, Clifford R Jack Jr¹¹, William Jagust¹², Susan M Landau¹², John C Morris¹³, Ozioma Okonkwo¹⁴, Richard J Perrin^{13

15

16}, Ronald C Petersen¹⁷, Monica Rivera-Mindt¹⁸, Andrew J Saykin^{19

20}, Leslie M Shaw²¹, Arthur W Toga²², Duygu Tosun³, John Q Trojanowski²¹; Alzheimer's Disease Neuroimaging Initiative

Affiliations

¹ Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, California, USA.
² Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education (NCIRE), San Francisco, California, USA.
³ Department of Radiology, University of California, San Francisco, San Francisco, California, USA.
⁴ Department of Medicine, University of California, San Francisco, San Francisco, California, USA.
⁵ Department of Psychiatry, University of California, San Francisco, San Francisco, California, USA.
⁶ Department of Neurology, University of California, San Francisco, San Francisco, California, USA.
⁷ Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA.
⁸ Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, California, USA.
⁹ Department of Neurology and Center for Neuroscience, University of California Davis, Davis, California, USA.
¹⁰ Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labs, and Harvard Medical School, Boston, Massachusetts, USA.
¹¹ Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
¹² Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA.
¹³ Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA.
¹⁴ Wisconsin Alzheimer's Disease Research Center and Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
¹⁵ Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA.
¹⁶ Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, USA.
¹⁷ Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA.
¹⁸ Department of Psychology, Fordham University, New York, New York, USA.
¹⁹ Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
²⁰ Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
²¹ Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
²² Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.

PMID: 34581485
PMCID: PMC9158456
DOI: 10.1002/alz.12422

Review

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

Dallas P Veitch et al. Alzheimers Dement. 2022 Apr.

. 2022 Apr;18(4):824-857.

doi: 10.1002/alz.12422. Epub 2021 Sep 28.

Authors

Affiliations

¹ Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, California, USA.
² Department of Veterans Affairs Medical Center, Northern California Institute for Research and Education (NCIRE), San Francisco, California, USA.
³ Department of Radiology, University of California, San Francisco, San Francisco, California, USA.
⁴ Department of Medicine, University of California, San Francisco, San Francisco, California, USA.
⁵ Department of Psychiatry, University of California, San Francisco, San Francisco, California, USA.
⁶ Department of Neurology, University of California, San Francisco, San Francisco, California, USA.
⁷ Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA.
⁸ Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, California, USA.
⁹ Department of Neurology and Center for Neuroscience, University of California Davis, Davis, California, USA.
¹⁰ Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labs, and Harvard Medical School, Boston, Massachusetts, USA.
¹¹ Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
¹² Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California, USA.
¹³ Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA.
¹⁴ Wisconsin Alzheimer's Disease Research Center and Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
¹⁵ Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA.
¹⁶ Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, USA.
¹⁷ Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA.
¹⁸ Department of Psychology, Fordham University, New York, New York, USA.
¹⁹ Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
²⁰ Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
²¹ Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
²² Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.

PMID: 34581485
PMCID: PMC9158456
DOI: 10.1002/alz.12422

Abstract

Introduction: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

Methods: We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

Results: Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

Discussion: ADNI has had a profound impact in improving clinical trials for AD.

Keywords: AV1541 tau positron emission tomography; Alzheimer's disease; amyloid; disease progression; mild cognitive impairment; plasma biomarker; tau.

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Conflict of interest statement

Dr. Veitch was supported for the present work by NIH grant U19‐AG024904 and has no other support or conflicts of interest to declare. Dr. Weiner is the principal investigator of NIH‐funded grants. Over the past 36 months he received funding administered through his institutions (NIH grants: 1RF1AG059009‐01 and 1R01AG058676‐01A1; CA Dept. of Health grant: 19‐10616; NIH Subaward from Dr. Richard Gershon: 1U2CA060426‐01), consulting fees (Cerecin/Accera, Inc., BioClinica, Nestle/Nestec, Roche, Genentech, NIH, The Buck Institute for Research on Aging, FUJIFILM‐Toyama Chemical [Japan], Garfield Weston, Baird Equity Capital, University of Southern California [USC], Cytox, and Japanese Organization for Medical Device Development, Inc. [JOMDD], and T3D Therapeutics), and payment for lecturing (The Buck Institute for Research on Aging). He holds stock options in Anven, Alzheon, and Aleca. He receives other grant support for his work (NIH: 5U19AG024904‐14; 1R01AG053798‐01A1; R01 MH098062; U24 AG057437‐01; 1U2CA060426‐01; 1R01AG058676‐01A1; and 1RF1AG059009‐01, DOD: W81XWH‐15‐2‐0070; 0W81XWH‐12‐2‐0012; W81XWH‐14‐1‐0462; W81XWH‐13‐1‐0259, PCORI: PPRN‐1501‐26817, California Dept. of Public Health: 16‐10054, U. Michigan: 18‐PAF01312, Siemens: 444951‐54249, Biogen: 174552, Hillblom Foundation: 2015‐A‐011‐NET, Alzheimer's Association: BHR‐16‐459161; The State of California: 18‐109929). He also receives support from Johnson & Johnson, Kevin and Connie Shanahan, GE, VUmc, Australian Catholic University (HBI‐BHR), The Stroke Foundation, and the Veterans Administration. He has served on advisory boards for Eli Lilly, Cerecin/Accera, Roche, Alzheon, Inc., Merck Sharp & Dohme Corp., Nestle/Nestec, PCORI/PPRN, Dolby Family Ventures, National Institute on Aging (NIA), Brain Health Registry, and ADNI. He has no other support or conflicts of interest to declare. Dr. Aisen has received support over the last 36 months from payments to his institution from NIA, Alzheimer's Association, Janssen, Lilly, and Eisai. He has served on the advisory boards of Biogen, Merck, Roche, Abbvie, Rainbow Medical, ImmunoBrain Checkpoint, Shionogi, and has no other support or conflicts of interest to declare. Dr. Beckett was supported in the current work by NIA/NIH U01AG024904 to her institution and received support over the last 36 months from payments to her institution from U01AG024904 (Dr. Weiner, UCSF/NCIRE), R01AG062517 (Dr. Dugger), B639943 (Dr. Coleman), and the National Institute of Justice 2014‐R2‐CX‐0012 (Dr. Wintemute). She has served on the external advisory board for Alzheimer's Disease Centers at UCSD, Washington University, University of Pittsburgh and Data and Safety Monitoring Boards for NIH‐funded clinical trial (UCSF), all paid directly to her. Her travel to the AAIC was supported by U01AG024904. She has no other support or conflicts of interest to declare. Dr. DeCarli was supported in the current work by NIH and has received support over the last 36 months from payments to his institution from the NIH. He served on an advisory board at Novartis on a safety study of heart failure treatment and has no other support or conflicts of interest to declare. Dr. Green was supported in the current work by NIH funding. Over the last 36 months, he has received support from multiple NIH grants to his institution, consulting fees (AIA, Genomic Life, Grail, Humanity, Kneed Media, Plumcare, UnitedHealth, Verily, VibrentHealth, and Genome Medical), and lectures in non‐Alzheimer's fields. He has no other support or conflicts of interest to declare. Dr. Harvey has no support or conflicts of interest to declare. Dr. Jack was supported in the current work by NIH funding. Over the last 36 months, he has received support from NIH grants to his institution and has served on the advisory boards of iDMC and Roche with no payments made. He has no other support or conflicts of interest to declare. Dr. Jagust has received support over the last 36 months from grants to his institution (NIH grants R01 AG034570 [Dr. Jagust], R01AG062542 [Dr. Jagust], U24 AG067418 [Dr. Jagust], P01AG019724 [Dr. Bruce Miller], R01 AG031164 [Dr. Matthew Walker], RF1 AG054019 [Dr. Matthew Walker], U01 AG024904 [Dr. Weiner], RF1 AG054106‐01A1 [Dr. Matthew Walker], R44AG046025‐03 [Dr. Daojing Wang], R01 AG061303 [Dr. Lexin Li], MH112775 [Dr. Ming Hsu], 1R01AG062689‐01 [Dr. Landau], AG062624 [Dr. José Luchsinger], and R01AG069090), direct consulting fees (Biogen, Bioclinica, Genentech/Roche, CuraSen, Grifols), and has served on the advisory board of the Alzheimer's Prevention Initiative. He has no other support or conflicts of interest to declare. Dr. Landau has received support over the last 36 months from grants through her institution (R01 AG062689 [Dr. Landau], U19 AG024904 [Dr. Weiner], R01 AG061303 [Dr. Li], R01AG062542 [Dr. Jagust], U24 AG067418 [Dr. Jagust]), an honorarium for speaking (4th annual Hillblom Symposium, University of CA, San Francisco), travel expenses and conference registration (AAIC 2017‐2019 as member of the Scientific Program Committee during this period), and has served on the advisory board of KeifeRx. She has no other support or conflicts of interest to declare. Dr. Morris over the past 36 months received honoraria (Grand Rounds lecture advisory board member) and support to attend national and international meetings, external advisory meetings, and board member meetings. He has no other support or conflicts of interest to declare. Dr. Okonkwo over the past 36 months received grants through his institution from the NIH and served in the International Neuropsychological Society. He has no other support or conflicts of interest to declare. Dr. Perrin over the past 36 months received grants through his institution (U19 AG024904, R01 NS092865, RF1 AG053550, R01 AG054513, R01 AG054567, R01 AG052550, P01 AG00399,1 U19 AG032438, P30 AG066444, U19 AG032438, R01 AG068319, R01AG053267, R01 AG070883, R01 NS103276). He has no other support or conflicts of interest to declare. Dr. Petersen over the past 36 months received grants through his institution (P30 AG062677, U01 AG006786); licenses or royalties from Oxford University Press and UpToDate; and consulting fees from Roche, Merck, Biogen, Genentech, and Eisai. He served on the advisory board of Genentech and has no other support or conflicts of interest to declare. Dr. Rivera‐Mindt over the past 36 months received grants through her institution (NIH/NIA R13 AG071313‐01 [Drs. M. Rivera Mindt, R. Turner‐II, M. Carrillo], Genentech Health Equity Innovations 2020 Fund G‐89294 [Drs. M. Rivera Mindt, R. Nosheny & C. Hill], NIH/NIA R01AG065110 ‐ 01A1 [Dr. Rivera Mindt], NIH/NIA 5U19AG024904‐14 [Dr. Weiner], R01AG066471‐01A1 [Drs. A. Federman & J.P. Wisnivesky], AARGD‐16‐446038 [Dr. Rivera Mindt; PI of subcontract to Mt. Sinai: J. Robinson‐Papp], and NIH/NIMH U24MH100931‐03 [Dr. S. Morgello]), speaking fees for talks at various universities across the country (e.g., Brown, Columbia, University of Arizona), and travel support from NIH grants. She has served on the Society for Black Neuropsychology; is a Present Member, Centers for Disease Control and Prevention (CDC) BOLD Public Health Center of Excellence on Dementia Risk Reduction Expert Panel; and Present Member, CDC/National Alzheimer's Project Act (NAPA) Physical Activity, Tobacco Use, and Alcohol Workgroup; and was paid directly for her role on the 2019 Data Safety & Monitoring Board (DSMB) Member Project Title: Reducing HIV Risk Behavior in Depressed and Non‐Depressed Older Adults with HIV; Grant #: R01AG05308101 (PI: T. Lovejoy). She is a Present Board Member, Alzheimer's Association NYC Chapter Board of Directors, President‐Elect and Past‐President (Elected Position), Hispanic Neuropsychological Society, on the Board of Directors, Harlem Community & Academic Partnership, all unpaid. She has no other support or conflicts of interest to declare. Dr. Saykin was supported in this work by grants from NIH and Department of Defense (NIH grants U01 AG024904, P30 AG010133, R01 AG019771, R01 LM013463, R01 LM011360 and DoD grants W81XWH‐13‐1‐0259 and W81XWH‐12‐2‐0012), and over the past 36 months received support from grants to his institution (as detailed above). He served on the Bayer Oncology Advisory Board and received PET tracer precursor from Avid Radiopharmaceuticals. He has no other support or conflicts of interest to declare. Dr. Shaw has received over the past 36 months grants through his institution (NIH grants U01 AG024904 [ADNI3], UPenn ADRC NIA grant for Biomarker Core; Michael J. Fox Foundation for Parkinson's Research for AD biomarker studies; Roche IIS for AD biomarker studies), fees for the Biogen Teaching program on AD Biomarkers, and travel funds from NIA ADNI3 Biomarker Core. He has served on the Roche Advisory Board, LEADS Advisory board, and Fujirebio Advisory Board. He received in‐kind support from Roche (immunoassay reagents and equipment) for ADNI3. He has no other support or conflicts of interest to declare. Dr. Toga was supported in this work by grants from NIH and has received over the past 36 months grants through his institution from NIH and speaking fees from Biogen. He has no other support or conflicts of interest to declare. Dr. Tosun has received over the past 36 months grants through her institution (NIH U01 AG024904). She has no other support or conflicts of interest to declare. Dr. Trojanowski has received over the past 36 months grants through his institution (AG10124). He has no other support or conflicts of interest to declare.

Figures

**FIGURE 1**
Prediction of longitudinal tau‐PET change. A, Hypothetical network spreading model of tau pathology. Each node within the network represents a brain region, where color indicates local tau pathology, distance between regions indicates connection length (i.e., Euclidean distance), and edge thickness indicates functional connectivity strength. Example formulae for models 1 to 3 illustrate how tau‐weighted distance (Model 1), tau‐weighted functional connectivity (Model 2). or tau‐ & distance‐weighted functional connectivity (Model 3) that were used to model group‐mean annual tau‐PET change in the 53 Aβ+ ADNI (B–D) and 41 Aβ + BioFINDER subjects (E–G) were computed. For ADNI, the computed association is illustrated in (B–D) for 1000 bootstrapped samples. H‐J, Resulting β‐value distributions (y‐axis) were compared between Models 1–3 using an ANOVA with post‐hoc Tukey‐test (x‐axis). F, Prediction Models 1–3 were assessed on the subject‐level for 53 ADNI Aβ+ and 41 BioFINDER Aβ+ subjects using subject‐level annual tau‐PET change and subject‐level connectivity (ADNI) or HCP‐derived group‐level functional connectivity (BioFINDER). Subject‐derived β‐value distributions were compared across Models 1–3 using an ANOVA. Linear model fits are indicated together with 95% confidence intervals. Aβ, amyloid beta; ADNI, Alzheimer's Disease Neuroimaging Initiative; ANOVA, analysis of variance; DAN, dorsal attention network; DMN, default mode network; FPCN, frontoparietal control network; HCP, host cell protein; PET, positron emission tomography; ROIs, regions of interest; VAN, ventral attention network. Reproduced with permission from Franzmeier et al.

**FIGURE 2**
(A) Baseline distribution of staging clasification per cohort. Stages refer to a model developed in CU individuals based on the sequential addition of four clusters of regional Aβ: Stage 0: no tracer uptake; Stage 1: cingulate regions; Stage 2: precuneus, paracentral gyrus, lateral orbital cortex, and insula; Stage 3: basal temporal, frontal, and additional associative cortices; Stage 4: other temporal and occipital regions. Classification based on Aβ staging model vs (B) global Aβ PET classification. (C) syndromic diagnosis, (D) genetic risj, (E) z‐scored CSF Aβ42 levels, and (F) log‐transformed z‐scored phosphorylated tau (p‐tau) values. Aβ, amyloid beta; ABIDE, Alzheimer's Biomarkers in Daily Practice; ADC, Amsterdam Dementia Cohort; ALFA, Alzheimer's and Family cohort; EMIF‐AD, European Medical Information Framework for AD; FBP, florbetapir; PIB, Pittsburgh compound B. Reproduced with permission from Collij et al.

**FIGURE 3**
Association of tau positron emission tomography uptake with cortical thickness and atrophy. Top, Mean standardized uptake value ratio of 18F‐AV‐1451 uptake in amyloid beta (Aβ)– (left) and Aβ+ (middle) individuals. Right panel shows areas of significantly greater tracer uptake in amyloid‐positive group (P < .01 family‐wise error rate). Bottom, Areas of significantly greater thickness (left) and lower rate of thickness change (right) in Aβ– individuals. Maps are shown at an uncorrected threshold of P < .1 for visualizing trends in the data. Effects were not statistically significant after correction for multiple comparisons. Reproduced with permission from Das et al.

**FIGURE 4**
Discrepancies in AT(N) classification using different biomarker combinations. Percent of AT(N) misclassifications for the different biomarker combinations in (A) the whole sample, (B) cognitively unimpaired, (C) mild cognitive impairment, and (D) dementia subjects. The percent of participants classified in different categories are shown for each biomarker combination compared to classification with CSF Aβ42, p‐tau181, and t‐tau. Percent of misclassifications are shown in green when one biomarker was changed, and in orange when two biomarkers were changed. Aβ, amyloid beta; ADsig, Alzheimer's disease cortical signature; aHV, adjusted hippocampal volume; FBP PET, [18F] florbetapir positron emission tomography; FDG PET, [18F] fluorodeoxyglucose positron emission tomography; p‐tau181, phosphorylated tau; t‐tau, total tau. Reproduced with permission from Illán‐Gala et al.

**FIGURE 5**
Progression of regional atrophy of subgroups of temporal and phenotypic heterogeneity. Rows show the progression pattern of three major subtypes: a typical, a cortical and a subcortical subtype, as well as an additional very small outlier parietal group (only 4%) that may represent outliers with a posterior cortical atrophy phenotype. CVS, cross‐validations. Reproduced with permission from Young et al.

**FIGURE 6**
Cerebrovascular impacts in AD supported by recent ADNI studies. (1) Cardiovascular risk factors are associated with markers of neurodegeneration and cognitive decline., , , (2) The apolipoprotein E (*APOE*) ε4 allele may increase WMHs via induction of CAA. (3) *APOE* ε4 exacerbates effect of vascular risk factors on cognition. (4) CVD has an age‐related effect on general cognition which increases MCI to AD transition., (5) Aβ‐mediated and direct effects of CVD are additive. Other studies have found both direct and additive interactions. (6) Aβ is associated with WMHs independently of tau., (7) Aβ mediates association between WMH and cognition. This could occur directly or via tau. (8) Tau was associated with WMHs independent of Aβ. CAA, cerebral amyloid angiopathy; CSF, cerebrospinal fluid; CVD, cerebrovascular disease; MCI, mild cognitive impairment; WMHs, white matter hyperintensities

**FIGURE 7**
Paths to cognitive resilience or successful cognitive aging. Aβ, amyloid‐β. Reproduced with permission from Arenaza‐Urquijo et al.

**FIGURE 8**
Sex differences in tau‐based brain networks in four subject groups. Each node represents a brain region. The position of each node within the network is determined based on the strength of its PET SUVR correlations with other nodes. Regions that have higher PET correlations with each other than with the rest of the brain congregate to each other as communities depicted with different colors. CN, cognitively unimpaired; hemi., hemisphere; MCI, mild cognitive impairment; PET, positron emission tomography; SUVR, standardized uptake value ratio. Reproduced with permission from Shohouki et al.

**FIGURE 9**
Regional associations of plasma p‐tau181 with Aβ and tau PET. A, Regional associations between baseline plasma p‐tau181 levels and baseline Aβ PET SUVR. B, Regional associations between baseline plasma p‐tau181 levels and tau PET 6 years later. Color panels on the right display Pearson correlation coefficients ® of the effects on global measures. AD, Alzheimer's disease; CN, cognitively unimpaired; CI, cognitively impaired; MCI, mild cognitive impairment; FBP, florbetapir (Aβ) PET; FTP, flortaucipir (tau) PET. Reproduced with permission from Moscoso et al.

**FIGURE 10**
Survival curves for biomarker‐based models of decline. Observed progression is analyzed by Kaplan‐Meier whereas predicted progression is analyzed with Cox models. Findings are based on data from four cohorts. ATN, amyloid, tauopathy, and neurodegeneration; CSF, cerebrospinal fluid. Reproduced with permission from van Maurik et al.

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References

1. Weiner MW, Veitch DP, Aisen PS, et al. Impact of the Alzheimer's disease neuroimaging initiative, 2004 to 2014. Alzheimer's Dement. 2015;11:865‐884. - PMC - PubMed
1. Weiner MW, Veitch DP, Aisen PS, et al. The Alzheimer's disease neuroimaging initiative 3: continued innovation for clinical trial improvement. Alzheimer's Dement. 2017;13:561‐571. - PMC - PubMed
1. Apostolova LG, Hwang KS, Avila D, et al. Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures. Neurology. 2015;84:729‐737. - PMC - PubMed
1. Petersen RC, Aisen P, Boeve BF, et al. Criteria for mild cognitive impairment due to Alzheimer's disease in the community. Ann Neurol. 2013;74(2):199‐208. - PMC - PubMed
1. Weiner MW, Veitch DP, Aisen PS, et al. The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimer's Dement. 2012;8:S1‐S68. - PMC - PubMed

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Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

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Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

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