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Introduction The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials. Methods We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/). Results (1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE e4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE e4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies. Discussion ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.

INTRODUCTION: We sought to establish the relationships between standard post mortem measures of AD neuropathology and ante mortem [(11)C]PIB-PET analyzed with the Centiloid (CL) method, a standardized scale for Aβ-PET quantification. METHODS: Four centers contributed 179 participants encompassing a broad range of clinical diagnoses, PET data and autopsy findings. RESULTS: CL values increased with each CERAD neuritic plaque score increment (median −3 CL for no plaques, 92 CL for frequent plaques) and non-linearly with Thal Aβ phases (increases were detected starting at phase 2) with overlap between scores/phases. Findings were comparable across sites and when restricted to 56 patients who died within 2 years of PET. A threshold of 12.2 CL detected CERAD moderate-to-frequent neuritic plaques (AUC=0.910, Sensitivity=89.2%, Specificity=86.4%) while 24.4 CL identified intermediate-to-high AD neuropathological changes (AUC=0.894, Sensitivity=84.1%, Specificity=87.9%). DISCUSSION: Our study demonstrated the robustness of a multi-site Centiloid [(11)C]PIBPET study and established a range of pathology-based CL thresholds.

Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials. Methods We searched for ADNI publications using established methods. Results ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis. Discussion ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials.

Although amyloid imaging with PiB-PET ([C-11]Pittsburgh Compound-B positron emission tomography), and now with F-18-labeled tracers, has produced remarkably consistent qualitative findings across a large number of centers, there has been considerable variability in the exact numbers reported as quantitative outcome measures of tracer retention. In some cases this is as trivial as the choice of units, in some cases it is scanner dependent, and of course, different tracers yield different numbers. Our working group was formed to standardize quantitative amyloid imaging measures by scaling the outcome of each particular analysis method or tracer to a 0 to 100 scale, anchored by young controls (≤45 years) and typical Alzheimer's disease patients. The units of this scale have been named "Centiloids." Basically, we describe a "standard" method of analyzing PiB PET data and then a method for scaling any "nonstandard" method of PiB PET analysis (or any other tracer) to the Centiloid scale.

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 study aimed to enroll 400 subjects with early mild cognitive impairment (MCI), 200 subjects with early AD, and 200 normal control subjects; $67 million funding was provided by both the public and private sectors, including the National Institute on Aging, 13 pharmaceutical companies, and 2 foundations that provided support through the Foundation for the National Institutes of Health. This article reviews all papers published since the inception of the initiative and summarizes the results as of February 2011. 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 consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimers 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 combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, CSF biomarkers, and clinical tests; (4) 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; (5) 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. Baseline cognitive and/or MRI measures generally predicted future decline better than other modalities, whereas MRI measures of change were shown to be the most efficient outcome measures; (6) the confirmation of the AD risk loci CLU, CR1, and PICALM and the identification of novel candidate risk loci; (7) worldwide impact through the establishment of ADNI-like programs in Europe, Asia, and Australia; (8) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker data with clinical data from ADNI 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 (9) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world. The ADNI study was extended by a 2-year Grand Opportunities grant in 2009 and a renewal of ADNI (ADNI-2) in October 2010 through to 2016, with enrollment of an additional 550 participants.

The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of developing criteria for the symptomatic predementia phase of Alzheimer's disease (AD), referred to in this article as mild cognitive impairment due to AD. The workgroup developed the following two sets of criteria: (1) core clinical criteria that could be used by healthcare providers without access to advanced imaging techniques or cerebrospinal fluid analysis, and (2) research criteria that could be used in clinical research settings, including clinical trials. The second set of criteria incorporate the use of biomarkers based on imaging and cerebrospinal fluid measures. The final set of criteria for mild cognitive impairment due to AD has four levels of certainty, depending on the presence and nature of the biomarker findings. Considerable work is needed to validate the criteria that use biomarkers and to standardize biomarker analysis for use in community settings.

The Clinical Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has provided clinical, operational, and data management support to ADNI since its inception. This article reviews the activities and accomplishments of the core in support of ADNI aims. These include the enrollment and follow-up of more than 800 subjects in the three original cohorts: healthy controls, amnestic mild cognitive impairment (now referred to as late MCI, or LMCI), and mild Alzheimer's disease (AD) in the first phase of ADNI (ADNI 1), with baseline longitudinal, clinical, and cognitive assessments. These data, when combined with genetic, neuroimaging, and cerebrospinal fluid measures, have provided important insights into the neurobiology of the AD spectrum. Furthermore, these data have facilitated the development of novel clinical trial designs. ADNI has recently been extended with funding from an NIH Grand Opportunities (GO) award, and the new ADNI GO phase has been launched; this includes the enrollment of a new cohort, called early MCI, with milder episodic memory impairment than the LMCI group. An application for a further 5 years of ADNI funding (ADNI 2) was recently submitted. This funding would support ongoing follow-up of the original ADNI 1 and ADNI GO cohorts, as well as additional recruitment into all categories. The resulting data would provide valuable data on the earliest stages of AD, and support the development of interventions in these critically important populations.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) beginning in October 2004, is a 6-year research project that studies changes of cognition, function, brain structure and function, and biomarkers in elderly controls, subjects with mild cognitive impairment, and subjects with Alzheimer's disease (AD). A major goal is to determine and validate MRI, PET images, and cerebrospinal fluid (CSF)/blood biomarkers as predictors and outcomes for use in clinical trials of AD treatments. Structural MRI, FDG PET, C-11 Pittsburgh compound B (PIB) PET, CSF measurements of amyloid beta (Abeta) and species of tau, with clinical/cognitive measurements were performed on elderly controls, subjects with mild cognitive impairment, and subjects with AD. Structural MRI shows high rates of brain atrophy, and has high statistical power for determining treatment effects. FDG PET, C-11 Pittsburgh compound B PET, and CSF measurements of Abeta and tau were significant predictors of cognitive decline and brain atrophy. All data are available at UCLA/LONI/ADNI, without embargo. ADNI-like projects started in Australia, Europe, Japan, and Korea. ADNI provides significant new information concerning the progression of AD.

With the increasing life expectancy in developed countries, the incidence of Alzheimer’s disease (AD) and thus its socioeconomic impact are growing. Increasing knowledge over the last years about the pathomechanisms involved in AD allow for the development of specific treatment strategies aimed at slowing down or even preventing neuronal death in AD. However, this requires also that (1) AD can be diagnosed with high accuracy, because non-AD dementias would not benefit from an AD-specific treatment; (2) AD can be diagnosed in very early stages when any intervention would be most effective; and (3) treatment efficacy can be reliably and meaningfully monitored. Although there currently is no ideal biomarker that would fulfill all these requirements, there is increasing evidence that a combination of currently existing neuroimaging and cerebrospinal fluid (CSF) and blood biomarkers can provide important complementary information and thus contribute to a more accurate and earlier diagnosis of AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is exploring which combinations of these biomarkers are the most powerful for diagnosis of AD and monitoring of treatment effects.