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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Alzheimers Dement. Author manuscript; available in PMC 2010 August 24.
Published in final edited form as:
PMCID: PMC2927112
NIHMSID: NIHMS217119

The Alzheimer’s Disease Neuroimaging Initiative: Progress report and future plans

Abstract

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) beginning in October 2004, is a 6-year re-search 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 β (Aβ) 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 Aβ 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.

Keywords: ADNI, Alzheimer’s disease, MRI, PET, Amyloid, Memory, Tau

1. Introduction

1.1. Historical background and rationale for ADNI

Alzheimer’s disease currently affects more than five million patients in the U.S. and will rise to 16 million by 2050 [1], costing the U.S. economy more than $140 billion/yr [1,2]. Globally, an estimated 35.6 million people have dementia (largely because of AD), which is expected to reach 65.7 million in 2030 and 115.4 million in 2050 [3]. It is generally accepted that there is a pressing need to develop effective disease-modifying treatments to slow or halt progression of AD pathology to be used in subjects with dementia, mild cognitive impairment (MCI), and in control subjects at risk for development of cognitive decline and dementia. Presently, no treatments have been convincingly demonstrated to slow the progression of AD pathology.

The historical background to Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a long and complex story, best summarized in Reference [4]. Because AD is a disorder which affects cognition (especially memory) and leads to dementia, for many years a major focus was the behavioral characterization of the disorder including the development of standardized methods for assessment, diagnosis, and monitoring of progression of clinical symptoms and impairments. The recognition that AD dementia slowly develops as part of a spectrum from normal aging to MCI sprang out of the clinical and behavioral context. At the same time, for the past 20–30 years, a number of biological methods have been increasingly used to obtain quantitative information concerning changes in the brain and in biological fluids which occur in AD. Most notably, the development of FDG PET and MRI in the 1970s has led to an increasingly large body of knowledge about the changes in AD. Furthermore, changes in cerebrospinal fluid (CSF) proteins, notably abeta and tau, have also been studied for many years. One important highlight in the use of biomarkers was an National Institutes of Health (NIH) conference in 2000, organized by Dr Neil Buckholz, concerning the use of Biomarkers in AD. Shortly thereafter, the Alzheimer’s Imaging Consortium was established as a forum for discussion and exchange of ideas and information concerning using MRI and PET to study AD. In summary, during the 1980s and particularly the 1990s, there was increasing research activity using a wide variety of biomarkers, especially MRI, FDG PET, and measurements of CSF to study this disorder. Many investigators were reporting studies using different methods on different cohorts of subjects. Thus, it was somewhat difficult to compare the value of all these different methods. The need to develop a large cohort, in which the methods could be compared, became increasingly obvious to all in the field.

The original impetus for the ADNI began around 2000, when it was observed that many academic investigators, pharmaceutical companies, and biotech companies were beginning to develop treatments aimed at slowing the progression of AD. Measurements of cognition or conversion from MCI (generally accepted as a precursor to dementia) to dementia could not in themselves demonstrate that the treatments were slowing disease progression, because impaired cognition in AD and MCI can be improved with symptomatic treatments such as acetylcholinesterase inhibitors. Additionally, in 2000, there were insufficient standards for obtaining or measuring imaging and/or biomarkers for AD for the numerous investigators who were studying disease progression by measuring various imaging and CSF/blood biomarkers. Also lacking at the time was sufficient data to determine the relative value of biomarker measures to detect progression of AD in treatment trials. A comprehensive understanding of the sequence of pathophysiological events that cause AD and lead to dementia at the molecular, cellular, brain, and clinical levels was clearly needed. In addition, measurements that identify the various elements and the factors that influence AD pathology in living human subjects needed to be developed for use in early diagnosis and as risk factors and/or predictors for cognitive decline or dementia. Such measurements could eventually have utility in clinical trials and practice and thus support the ultimate goal of AD research to develop treatments that can slow the progression of AD and ultimately prevent the development of AD (either secondary, prevention, or primary prevention).

1.2. Disease model

ADNI research is based on a model (Fig. 1) positing that AD begins with Aβ accumulation in the brain, which ultimately leads to synaptic dysfunction, neurodegeneration, and cognitive or functional decline. This predicts that the earliest detectable changes (measured in the ADNI project) are those related to Aβ (detected in CSF and by PET amyloid imaging). Subsequently, neurodegeneration is detected by a rise of CSF tau species, synaptic dysfunction (measured by FDG-PET), and neuron loss indicated by atrophy, most notably in medial temporal lobe (measured with MRI). The temporal sequence of changes in Aβ deposition, CSF tau species, and imaging using FDG-PET and MRI remains to be determined. These changes ultimately lead to memory loss, general cognitive decline, and eventually dementia. Expression of each element of AD pathology (e.g., Aβ and tau deposits, atrophy) is influenced by many modifying factors including age, APOE genotype, and cerebrovascular disease (white matter lesions detected by fluid attenuated recovery [FLAIR MRI]) and microbleeds (detected by T2* MRI), and there are expected to be wide differences among individuals.

Fig. 1
Overall model of changes in the progression from normal aging to MCI to AD.

Although this simple model does not convey the complexities of the relationships among aging, tau phosphorylation and conformational change, amyloid peptide accumulation and conformational change, synaptic dysfunction and neuronal loss, we believe it is useful for the interpretation of biomarker, cognitive and clinical data from ADNI and other studies, and in the incorporation of biomarker measures into trial designs.

The ADNI project, however, is not built around, and does not depend on, the amyloid hypothesis. Despite the evidence in favor of this hypothesis [5], other evidence does not necessarily support all aspects of it. For example, the early Braak stage consists of tau tangles and synapse loss in the entorhinal cortex and hippocampus without amyloid accumulation [68]. In addition, there is poor correlation between brain amyloid level and cognitive impairment. A follow-up study of subjects in the Wyeth Elan 1792 vaccine trial showed amyloid removal (at pathology) in some subjects, while they continued to decline cognitively [9]. One possibility is that subjects with dementia have such severe brain damage that amyloid removal does not slow progression of symptoms. However, the failure of anti-amyloid clinical trials could be due to many reasons, including the possibility that the treatments did not sufficiently reduce brain amyloid. In possible support of this model, it has been recently reported (in one subject) that CSF amyloid falls before development of C-11 Pittsburgh compound B (PIB) positivity, which precedes cognitive impairment [10]. Replication and extension of this sequence of events in a multisite study with large sample size will provide critical information concerning the neuroscience of AD.

An important point to emphasize is that we have limited information concerning the pathophysiological sequence of events of AD in human beings from autopsy studies and from studies measuring only cognition. Our model suggests that different imaging modalities, measurements, and different biochemical markers will usefully serve as predictors (measurements which predict future change) and outcomes (measurements that detect change) at different stages in the transition from normal aging, to MCI, to dementia. Furthermore, the model suggests that the measurements most likely to predict decline in normal subjects will be the detection of Aβ in CSF, using PET perhaps in combination with measurements of CSF tau species, the use of brain imaging by FDG-PET, and MRI. Although amyloid biomarkers may be useful predictors of decline in early MCI (EMCI), CSF tau measurements, FDG-PET, and MRI measures of regional atrophy, which likely change after amyloid markers change, may be more predictive. In late MCI (LMCI) and AD, we hypothesize that the most effective biomarkers for prediction of further decline will be FDG-PET, MRI, and cognition. Biomarkers that are most likely to correlate with, and augment the utility of, cognitive and clinical measures as outcomes in clinical trials are FDG-PET, possibly MRI measures of volume (especially of hippocampus and temporal cortex) at early stages, and atrophy throughout the brain at later stages. However, it is recognized that the performance of the various imaging and CSF/blood measurements depends both on the biological sequence of events as well as the sensitivity, accuracy, and precision of the various measurements. Thus, for example, a test which best predicts future cognitive decline in normal subjects may not necessarily represent the earliest biological change, but rather the earliest change that is detected by a sensitive and robust test.

1.3. Goals of ADNI

The overarching goals of ADNI, therefore, were to determine the relationships among the clinical, cognitive, imaging, genetic, and biochemical biomarker characteristics of the entire spectrum of AD as the pathology evolves from normal aging through very mild symptoms, to MCI, to dementia, and to establish standardized methods for imaging/biomarker collection and analysis for ultimate use in clinical trials.

Initially, ADNI primarily aimed to ascertain the relative value of various imaging, and CSF/blood biomarkers as outcome measures in trials of AD and MCI subjects. Specific goals to this end included the validation of MRI and PET imaging, and of blood and CSF biomarker measures by examining their relationships with cognitive and functional measures, the identification of the most effective measures for monitoring treatment effects in different stages in the progression of normal aging, through MCI to AD, and the development of statistical models of cross-sectional and longitudinal clinical, imaging, and biomarker data, which could be used for future hypothesis generation and testing. Other goals of ADNI were to develop improved standardized methods for performing AD trials by creating uniform standards for MRI and PET acquisition, to develop improved methods of acquiring and processing multisite longitudinal data that would increase cost-effectiveness and power of future treatment trials, and to develop statistical models of cross-sectional and longitudinal clinical, imaging, and biomarker data that could be used for future hypothesis generation and testing. Finally, ADNI aimed to create a data repository for academics and industry for a multiplicity of purposes. This repository would provide further information regarding longitudinal changes in brain structure, function, cognition, blood, urine, and CSF biomarkers that occur in normal aging, MCI, and AD as well as transitions from one of these states to another. Data generated by ADNI would be available to qualified scientists worldwide without embargo.

In the 5 years since the funding of ADNI in 2004, there has been increased interest in the use of imaging and CSF/blood biomarkers to identify AD pathology in subjects before dementia, and to develop diagnostic criteria that use these measurements [11]. Data from ADNI have proved to be a valuable resource to address these issues, and thus the development of imaging and CSF/blood biomarkers as predictors has become an important goal of ADNI.

1.4. Identification of outcomes and predictors

Different biomarkers will be effective predictors of cognitive decline or dementia or outcomes (measures of change) at different stages across the continuum from normal cognition to AD dementia. Understanding the sequential change of biomarkers and their relative value as predictors and outcomes at the presymptomatic, mild symptoms or MCIs, and dementia stages of the disease is crucial to understanding the neuroscience of AD, and may lead to improved diagnostic tests and facilitate design and power calculations of clinical trials for disease-modifying agents.

Measures of rates of change serve as outcomes in clinical trials. A problem with AD clinical trials is the length of time and large sample sizes required because of the high variability of clinical and cognitive measures. Numerous reports suggested that changes in brain structure (detected by MRI) or brain glucose metabolism (detected by FDG-PET) had higher statistical power to detect change than clinical or cognitive measures because of their low variability. Interest in biomarkers was further increased because measures of function and cognition are affected by many things (e.g., depression, other illnesses) in addition to features of AD, are potentially affected by drugs such as cognitive enhancers, have low statistical power to determine effects of disease-modifying treatments, and only indirectly reflect disease progression.

Furthermore, biomarkers that directly or indirectly measure AD pathology may be used as predictors of cognitive decline or dementia. Such predictors will assist in the enrichment and selection of subjects with mild impairment and in normal elderly subjects for treatment trials and even prevention trials. It is generally accepted that AD pathology (amyloid plaques, tau tangles, synapse loss, gross neuron loss, and brain shrinkage) begins many years before dementia and often exists with no evidence of cognitive impairment. The cognitive impairment caused by AD pathology is thought to occur within the context of the cognitive changes which occur in normal aging, and is characterized initially by problems with memory functioning. This progresses to deficits in other cognitive domains, functional abilities, and frank dementia. Evidence exists that the pathological and cognitive changes are nonlinear in that there is a gradual acceleration of pathological and cognitive changes. There is a compelling need to identify measurements that identify the presence and extent of AD pathology in the living brain, thus characterizing the stage of disease. Because of the nonlinear nature of the process, knowledge of the stage of progression could potentially be used to predict the future rate of cognitive decline and the future occurrence of dementia (the more advanced the progression, the greater the rate of future change). As amyloid plaques develop, considerable evidence suggests that CSF Aβ amyloid decreases [12,13]. Thus, CSF Aβ is a putative measure of brain amyloid deposition. Brain amyloid is directly detected by PET amyloid ligands. CSF tau increases in the progression of controls to MCI to AD [12,13], and is a putative measure of the deposition of tau tangles and neurodegeneration. No direct measures of brain synaptic density exist in human beings, but brain activity is reduced as synaptic density falls, and FDG-PET is a quantitative measure of brain activity that appears to identify early AD. Structural MRI detects brain atrophy, and hippocampal volume shrinkage has been correlated with neuronal loss [14] and neurofibrillary tangles. Thus, each of these measures has predictive value, but their relative values at the different stages across the continuum have not been established. Several investigators have proposed that imaging and CSF biomarkers could be used to identify AD pathology in subjects who are not demented, and could thus be used for diagnosis of AD [11]. Several pharmaceutical companies and the Alzheimer’s Disease Cooperative Study (ADCS) have proposed performing AD treatment studies using subjects with early AD, meaning nondemented subjects with cognitive impairments who have imaging/CSF biomarker evidence of AD pathology (especially low CSF Aβ and/or C-11 PIB positivity), but the value of this approach has not been established. Genetics may also be considered a predictor in AD. ADNI included analysis of the APOE ε4 gene during enrolment to balance the frequency of this gene in the PET and CSF sub-studies. Subsequently, a genome-wide association analysis was performed on the DNA of all ADNI subjects.

2. Methods

2.1. ADNI structure and organization

ADNI is structured as eight cores under the auspices of the Administrative Core, directed by Dr Weiner, the principle Investigator. ADNI is a U01 cooperative agreement grant, and the NIA requires that this project be governed by a Steering Committee consisting of Dr Weiner and all funded Core leaders, all Site Principal Investigators, representatives from the NIH and US Food and Drug Administration (FDA), and representatives from each of the contributing companies as observers only. The day to day decisions are made by the ADNI Executive Committee (Excom) which includes the Principal Investigator, the Core leaders, a representative of the NIA (Dr Buckholtz), the current, past, and future Chairs of the Industry Scientific Advisory Board (ISAB; as observers), and David Lee of the Foundation for the NIH (as an observer). The governance and organization of ADNI are depicted in Fig. 2.

Fig. 2
Patient recruitment sites in ADNI (provided by Sarah Walter at ADCS).

The original cooperative agreement (U01) grant, termed ADNI1, was funded as a public/private partnership with $40 million from NIA and $20 million from 13 companies in the pharmaceutical industry and two Foundations, for a total of $60 million. Since then, additional companies have joined, bringing the total to 22. An additional $7 million has been provided in the form of supplements for (1) the C-11 PIB sub-study; (2) the lumbar puncture extension (beyond the original 1 year of funding); and (3) the genome-wide association analysis of the DNA of all ADNI subjects. All funds from industry are provided to the Foundation for NIH which then provides the combined funds to NIA who awards funds in the form of a UO1 grant to ADNI. (Table 1).

Table 1
ADNI site table

All sites are managed by the ADNI Clinical Core at the ADCS, University of California, San Diego (Paul Aisen, PI). The Data and Publications Committee (DPC) vets all publications using ADNI data (see description in the supplemental references, online only). The ISAB is composed of representatives from all companies which provide funds to ADNI and is managed by the Foundation for NIH. The ISAB is chaired on a rotating basis. Chairs have included William Potter (Merck 2005, 2006), Eric Siemers (Lilly, 2007), Patricia Cole (Eisai, 2008), Holly Soares (Pfizer, 2009), and in 2010 Mark Schmidt (Novartis).

The Scientific Advisory Board is Chaired by Zaven Khachaturian, meets annually, and has consisted of many prominent physicians and scientists including Lewis Kuller, Dennis Choi, Gregory Sorensen, Peter Snyder, William Thies, Howard Fillit, William Friedewald, Richard Frank, Richard Frakowiack, and Thomas Budinger.

All requests for specimens (blood, plasma, CSF, DNA, immortalized cell lines) go directly to the Resource Allocation Review Committee (RARC), consisting of members independent of ADNI, approved by the NIA, and chaired by Dr Tom Montine at the University of Washington, Seattle, Washington. After approval by the RARC, the NIA must approve release of all specimens.

2.1.1. Administrative core

The Administrative Core is located at the San Francisco VA Medical Center/University of California, San Francisco/NCIRE. It consists of the Principal Investigator of ADNI (M.W. Weiner), his administrative staff, statistical support, and the DPC administered by Robert Green at Boston University. Dr Weiner has responsibility for all administrative, financial, and scientific aspects of ADNI. In addition to the highly complex administration of grant finances, some image analysis of ADNI data, using FreeSurfer (from Massachusetts General Hospital, Bruce Fischl, PI) is performed at the San Francisco, VA, overseen by Dr Schuff. This work is part of the MRI Core (Fig. 3).

Fig. 3
Governance and organization of ADNI.

2.1.2. Data and publications committee (DPC: PI Robert Green)

The DPC performs three tasks: (1) It develops and proposes policy to the Executive and Steering Committees regarding data access and publication; (2) It screens all applications for access to ADNI data; and (3) It reviews all publications for adherence to ADNI publication policy guidelines. The DPC helped develop policies for open data access such that virtually all requests for data access are granted. Persons requesting access to the data fill out a brief online application form in which they indicate their academic affiliation and reason for requesting access, or a statement about the project area they are interested in. Each of these applications is individually reviewed by the DPC Chair. A table of individuals with access to the data and the projects they are pursuing is publically available so that data users can be aware of the interests of others and reach out to other data users to form collaborations if they wish. The DPC Administrator reviews manuscripts and requires all scientists who are developing manuscripts using ADNI data to adhere to ADNI publication guidelines. These guidelines request that authorship be stated in the “modified corporate authorship” format, in which the particular writing team is named, and the authorship list is followed by the words, “for the ADNI Study*”; the asterisk here refers to a web page where the ADNI leadership and individual site directors and co-investigators at each ADNI site are named. In this manner, the ADNI leadership and ADNI site investigators can obtain group authorship credit that provides at least modest academic credit for the work they are doing toward all ADNI publications.

A member of the DPC also reviews each manuscript for any that may have egregiously poor quality, but importantly, does not attempt to review manuscripts for scientific quality or for duplication. It has been our conscious policy to avoid practices that would inhibit or slow the utilization of ADNI data by the worldwide scientific community. Therefore, we have decided that scientific review should occur at the level of publication review, and that we will tolerate, and even encourage, multiple examinations of ADNI data by multiple investigators. Although this philosophy raises the possibility that two papers could present conflicting analyses or interpretations, we have elected to let such potential conflicts play out in the marketplace of ideas.

2.1.3. Other cores

Briefly, the eight cores for which the administrative core is responsible are as follows: (1) Clinical Core, based at the University of California, San Diego, and the Mayo Clinic, and is responsible for the recruitment of subjects, the development of an electronic data capture system at each site, and of protocols and procedures for ADNI; (2) MRI Core, based at the Mayo Clinic, and responsible for all MRI procedures and for developing standardized imaging methods; (3) PET Core, based at the University of California, Berkeley, and responsible for all PET procedures and for developing standardized imaging methods; (4) Biomarker Core, based at the University of Pennsylvannia, and responsible for the collection and analysis of biomarkers in biofluids, and for the establishment of an archive of biofluids; (5) Genetics Core, based at Indiana University and responsible for genotyping participants; (6) Neuropathology Core, based at Washington University, and responsible for the establishment of protocols to facilitate brain autopsies of ADNI patients who die; (7) Biostatistics Core, based at the University of California, Davis, and responsible for the statistical analysis of data generated from the other Cores; (8) Informatics Core, based at the University of California, Los Angeles, and responsible for the establishment of a website to facilitate data-sharing of data generated by ADNI projects.

Detailed summaries of the results of the ADNI Cores are provided in the accompanying articles of this special issue.

3. Limitations of ADNI

One limitation of ADNI is that our population represents a clinical trial population and not an epidemiologically selected real life population. Our subjects do not include those with cortical strokes, cancer, heart failure, substance abuse, etc. Therefore, the extent to which the results from ADNI can be generalized to the entire population remains to be determined. Future population-based studies will be required to determine whether the information derived from ADNI is relevant to the greater population. One approach has been for ADNI investigators to develop collaborations with investigators who are conducting population-based studies, so that ADNI methods can be used in such studies. A second limitation is that ADNI only studies subjects aged 55–90 years, and there is considerable evidence that AD pathology may begin to occur in the human brain well before this age. Autopsy studies and amyloid imaging have suggested that a substantial fraction of cognitively normal subjects in their 70s have AD pathology. A full understanding of the pathophysiological sequence of events that occur in AD will require longitudinal studies of subjects beginning at a young age. A third limitation of ADNI is the type of data that are not being collected including computerized neuropsychological testing, electroencephalogram, magnetoencephalography, magnetic resonance spectroscopy, metabolic and inflammatory markers, and lifestyle information. The decision concerning which measures to include was reached by consensus among the Site Principal Investigators (PIs), Core leaders, and the NIA. Although many of these measures might provide useful information, they are not included because of the following reasons: (1) The measures have not yet been demonstrated to have high value as either predictors or outcomes, and are not currently being incorporated into clinical trials; (2) The subject burden of ADNI is already quite great (clinical/cognitive battery, MRI, FDG/amyloid PET, lumbar puncture) and there are concerns that adding additional tests will impair enrollment and increase dropout; (3) The additional cost of these measures is not supported by evidence for inclusion. One final limitation of ADNI has been that not all measurements (like FDG-PET and lumbar puncture) were obtained on all subjects, limiting the ability to compare methods. This is being overcome in the current study in which all subjects will have (at least) baseline lumbar puncture and AV-45 amyloid imaging as well as the other measurements.

4. Results

4.1. Overall ADNI impact

The effect of ADNI thus far falls into three main areas. First, the establishment of standardized methods for imaging/biomarker collection and analysis has been a key step forward, and these methods are starting to be used in clinical trials. For instance, ADNI results on LMCI subjects replicated rates of conversion in a similar group of MCI subjects enrolled using the Petersen criteria in the ADCS Vitamin E/Donepezil trial, and the standardized neuropsychological battery used by ADNI is now being used by industry and ADCS trials. The MRI core developed a structural MRI protocol, identical across vendors, with an MRI phantom for calibration which has since been used in numerous phase 2 and 3 treatment trials. The PET core established methods for multisite FDG-PET, and the first multisite C-11 PIB study. The biomarker core established standardized methods for measurements of CSF Aβ amyloid and species of tau. The importance of these standardization efforts should not be underemphasized because the ADNI methods have now been adopted for other ADNI-like studies outside of the U.S. and this will facilitate comparisons of results among countries, cultures, and ethnicities, and provide an infrastructure for worldwide clinical trials by the pharmaceutical industry. Second, ADNI has resulted in the provision of a large data base of images, genetic, fluid biomarker, and clinical data that are being used by many investigators and industry. Finally, ADNI has generated new results in many areas, such as the identification of outcome measures with high power to detect treatment effects, and of predictors such as CSF biomarkers which have been shown to predict future rates of brain atrophy, brain glucose metabolism, and cognition in MCI. Contributing to the knowledge of AD neuroscience is the finding that there is evidence that AD pathology in normal subjects is associated with greater rates of change of brain structure and brain glucose metabolism. Amyloid imaging and CSF Aβ have been found to provide similar information. An important long-term goal of our field is to identify and validate imaging/biomarkers for AD progression which can be used as surrogate markers in place of clinical/cognitive tests in clinical trials. This is a very long way off, because such surrogate markers must be validated in the treatment setting, across various types of treatments. Nevertheless, the ADNI results are providing an important first step toward this goal.

ADNI has also had a great effect in a global sense (Fig. 4). At the time when ADNI1 was funded, there were no plans for similar efforts in other countries. However, the establishment of ADNI stimulated many such efforts resulting in the following: (1) The Australian study, AIBL (PI Colin Masters) [15], is a two-site longitudinal study of 1,100 subjects with MRI (using ADNI protocol), a subset with C-11 PIB, and cognitive measures (similar to ADNI). In fact, AIBL was conceived of before and began independent of U.S. ADNI; (2) Japanese ADNI (PI Takeshi Iwatsubo) [16], which studies 220 subjects using methods identical to ADNI in all respects except for language; (3) European ADNI (PI Giovanni Frisoni), which is enrolling 150 subjects. There are also several large longitudinal projects beginning in China using imaging/CSF biomarkers and a Korean ADNI is being planned. The Alzheimer’s Association has organized a quarterly teleconference of all worldwide ADNI PIs, is working to fund more data-sharing efforts among the projects, and Dr Iwatsubo hosted the first worldwide ADNI meeting in Sendai, Japan, in November, 2009.

Fig. 4
Map showing all ADNI and ADNI-like efforts in the world (Figure kindly provided by Maria Carillo of the Alzheimer’s Association).

Thus, the effect of ADNI and these numerous projects around the world on AD research is huge, as is the value of the information gained to academic scientists and to the pharmaceutical industry as a result of the sharing of all data. To our knowledge, ADNI is the only neuroscience project in the world that is having such a worldwide effect in the AD field. To date, there have been more than 60 publications arising from AD, both directly, or indirectly through shared data (Appendix list, online only).

4.2. Grand opportunities grant

A Grand Opportunities (GO) grant (American Recovery Act funds, i.e., stimulus funds) was recently awarded to the identical team of investigators overseeing ADNI. Dr Weiner is also PI of the GO grant, which closely relates to ADNI and is separately administered with its own account/fund and separate subcontracts. This grant will provide an additional $24 million of funding over 2 years to enroll 200 EMCI subjects, some of whom will have early biomarker signals of AD pathology. This category of subjects has not been enrolled in ADNI thus far, and so it will bridge the gap between normal elderly and LMCI subjects who are more amnestic than EMCI subjects. These GO subjects will have clinical/cognitive, blood/CSF/genetic, FDG and amyloid PET, and MRI measurements during the 2-year period of the GO grant. This grant will also fund F18 amyloid PET imaging on all existing normal control and LMCI subjects, and newly enrolled EMCI subjects, which will allow correlation and comparison of this modality with all of the other clinical/cognitive, neuroimaging, genetic, and biomarker data collected in the project. The GO grant will extend the follow-up of LMCI and normal subjects who were enrolled in ADNI1 and are being carried forward in GO, and will allow analysis of all of the ADNI data that was not able to be done in the initial grant (since it was a data collection grant, and few funds were provided for analysis) as well as analysis of the data from this GO project, to test hypotheses and perform data explorations.

4.3. Future directions of ADNI

Funding for ADNI1 ends October 1, 2010. The future of ADNI will depend on a successful competitive renewal (termed ADNI2). ADNI2 will be focused on predictors, outcomes, and clinical trial design, but fulfillment of these aims will add considerably to what is known about the pathophysiological sequence of changes in the brain that occur across the continuum from normal aging to MCI to AD dementia. Now the major goals of ADNI, therefore, are as folllows: (1) To identify and validate imaging and blood/CSF biomarker predictors of cognitive decline/dementia for early detection of AD; (2) To identify and validate imaging and blood/CSF biomarker outcomes that reflect progression of AD pathology; and (3) To develop information leading to improved clinical trials of treatments to slow disease progression, ultimately contributing to the prevention of AD dementia.

5. Summary

Taken together, ADNI is the only multisite longitudinal observational clinical/imaging/biomarker study being performed in the U.S. ADNI data are widely available to all scientists throughout the world without embargo through the UCLA/LONI/ADNI website. ADNI has already demonstrated its high value by providing a great deal of scientific information, and providing information for development of clinical trial protocols that are being used in several current phase 3 studies. ADNI also serves as a model of ADNI-like efforts in other countries. The continuation of this study, through the GO and, hopefully, ADNI2 grant will contribute considerably to the development of new diagnostic approaches, improved clinical trials, and to the identification of effective treatments that slow the progression of AD pathology in demented and nondemented subjects. Ultimately, the results from ADNI will contribute considerably to the development of AD treatment trials and to effective measures that prevent the development of AD.

Acknowledgments

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. Complete listing of ADNI investigators is available at www.loni.ucla.edu/ADNI/Collaboration/ADNI_Manuscript_Citations.pdf.

This article is dedicated to Leon Thal, who passed away 1 year after ADNI began. His vision was critical in the creation and successful funding of ADNI.

Appendix: End notes

A1The Tables below show the schedule of events and scope of work performed for ADNI1, and the proposed work for the funded GO grant and ADNI2 which was under submission at the time of writing this manuscript. Table A1 shows the years for ADNI1, GO, and ADNI2 and how Year 1of the GO grant overlaps with Year 6 of ADNI1, and how Year 2 of the GO grant overlaps with Year 1 of ADNI2.

Table A1
Work flow for ADNI1, GO grant and ADNI2 for each year of activity

A2Table A2 shows the schedule of events for ADNI1. Year 1 was the preparatory phase with little enrolment. Year 6 just began, and thus the actual number of subjects or scans is not known.

Table A2
ADNI1 scope of work

A3Table A3 shows the subjects enrolled in the GO grant study, including existing subjects from ADNI1 and newly enrolled subjects.

Table A3
GO grant

A4Table A4 shows the schedule of events for the proposed ADNI2, which, if funded, would begin on September 1, 2010.

Table A4
ADNI2 schedule of activities per year and type of subject

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9. Holmes C, Boche D, Wilkinson D, Yadegarfar G, Hopkins V, Bayer A, et al. Long-term effects of Abeta42 immunisation in Alzheimer’s disease: follow-up of a randomised, placebo-controlled phase I trial. Lancet. 2008;372:216–223. [PubMed]
10. Cairns NJ, Ikonomovic MD, Benzinger T, Storandt M, Fagan AM, Shah AR, et al. PiB-PET detection of cerebral abeta may lag clinical, cognitive, and CSF markers of Alzheimer’s disease: a case report. Arch Neurol. (in press)
11. Gauthier S, Dubois B, Feldman H, Scheltens P. Revised research diagnostic criteria for Alzheimer’s disease. Lancet Neurol. 2008;7:668–670. [PubMed]
12. Visser PJ, Verhey F, Knol DL, Scheltens P, Wahlund LO, Freund-Levi Y, et al. Prevalence and prognostic value of CSF markers of Alzheimer’s disease pathology in patients with subjective cognitive impairment or mild cognitive impairment in the DESCRIPA study: a prospective cohort study. Lancet Neurol. 2009;8:619–627. [PubMed]
13. Mattsson N, Zetterberg H, Hansson O, Andreasen N, Parnetti L, Jonsson M, et al. CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment. JAMA. 2009;302:385–393. [PubMed]
14. Bobinski M, de Leon MJ, Wegiel J, Desanti S, Convit A, Saint Louis LA, et al. The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer’s disease. Neuroscience. 2000;95:721–725. [PubMed]
15. Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, et al. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int Psychogeriatr. 2009;21:672–787. [PubMed]
16. Arai H. Alzheimer’s disease neuroimaging initiative and mild cognitive impairment. Rinsho Shinkeigaku. 2007;47:905–907. [PubMed]

List of all publications based on ADNI data

1. Trojanowski JQ. Searching for the biomarkers of Alzheimer’s. Pract Neurol. 2004;3:30–34.
2. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, Trojanowski JQ, Toga AW, Beckett L. Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Alzheimers Dement. 2005;1:55–66. [PMC free article] [PubMed]
3. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, Trojanowski JQ, Toga AW, Beckett L. The Alzheimer’s Disease Neuroimaging Initiative. Neuroimaging Clin N Am. 2005;15:869–877. [PMC free article] [PubMed]
4. Fukuyama H. Neuroimaging in mild cognitive impairment. Rinsho Shinkeigaku. 2006;46:791–794. [PubMed]
5. Iwatsubo T. Beta-and gamma-secretases. Rinsho Shinkeigaku. 2006;46:925–926. [PubMed]
6. Leow AD, Klunder AD, Jack CR, Jr, Toga AW, Dale AM, Bernstein MA, et al. ADNI preparatory phase study: longitudinal stability of MRI for mapping brain change using tensor-based morphometry. Neuroimage. 2006;31:627–640. [PMC free article] [PubMed]
7. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, Trojanowski JQ, Toga AW, Beckett LA. Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative. Cognition and Dement. 2006;5:56–62. [PMC free article] [PubMed]
8. Arai H. Alzheimer’s Disease Neuroimaging Initiative and mild cognitive impairment. Rinsho Shinkeigaku. 2007;47:905–907. [PubMed]
9. Fletcher PT, Powell S, Foster NL, Joshi SC. Quantifying metabolic asymmetry modulo structure in Alzheimer’s disease. Inf Process Med Imaging. 2007;20:446–457. [PubMed]
10. Ihara Y. Overview on Alzheimer’s disease. Rinsho Shinkeigaku. 2007;47:902–904. [PubMed]
11. Murayam S, Saito Y. Neuropathology of mild cognitive impairment Alzheimer’s disease. Rinsho Shinkeigaku. 2007;47:912–914. [PubMed]
12. Haschke M, Zhang YL, Kahle C, Klawitter J, Korecka M, Shaw LM, Christians U. HPLC-atmospheric pressure chemical ionization MS/MS for quantification of 15-F2t-isoprostane in human urine and plasma. Clin Chem. 2007;53:489–497. [PubMed]
13. Shaw LM, Korecka M, Clark CM, Lee VM-Y, Trojanowski JQ. Biomarkers of neurodegenertaion for diagnosis and monitoring therapeutics. Nat Rev Drug Discov. 2007;6:295–303. [PubMed]
14. Fan Y, Batmanghelich N, Clark CM, Davatzikos C. the Alzheimer’s Disease Neuroimaging Initiative. Spatial patterns of brain atrophy in MCI Patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage. 2008;39:1731–1743. [PMC free article] [PubMed]
15. Hampel H, Burger K, Teipel SJ, Bokde ALW, Zetterberg H, Blennow K. Core candidate neurochemical and imaging biomarkers of Alzheimer’s Disease. Alzheimers Dement. 2008;4:38–48. [PubMed]
16. Nestor SM, Rupsingh R, Borrie M, Smith M, Accomazzi V, Wells JL, Fogarty J, Bartha R. the Alzheimer’s Disease Neuroimaging Initiative. Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s Disease Neuroimaging Initiative database. Brain. 2008;131(Pt. 9):2443–2454. [PMC free article] [PubMed]
17. Shaw LM. PENN biomarker core of the Alzheimer’s Disease Neuroimaging Initiative. Neurosignals. 2008;16:19–23. [PMC free article] [PubMed]
18. Visser PJ, Verhey FRJ, Boada M, Bullock R, De Deyn PP, Frisoni GB, et al. DESCRIPA study group. Development of screening guidelines and clinical criteria for predementia Alzheimer’s Disease. Neuroepidemiology. 2008;30:254–265. [PMC free article] [PubMed]
19. Boyes RG, Gunter JL, Frost C, Janke AL, Yeatman T, Hill DL, et al. for the ADNI study. Intensity non-uniformity corrections using N3 on 3-T scanners with multichannel phased array coils. Neuroimage. 2008;39:1752–1762. [PMC free article] [PubMed]
20. Jack CR, Jr, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. HDNI Study. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27:685–691. [PMC free article] [PubMed]
21. Hua X, Leow AD, Lee S, Klunder AD, Toga A, Lepore N, et al. the Alzheimer’s Disease Neuroimaging Initiative. 3D characterization of brain atrophy in Alzheimer’s disease and mild cognitive impairment using tensor-based morphometry. Neuroimage. 2008;41:19–34. [PMC free article] [PubMed]
22. Frisoni GB, Henneman WJP, Weiner MW, Scheltens P, Vellas B, Reynish E, et al. the Alzheimer’s Disease Neuroimaging Initiative. The pilot European Alzheimer’s Disease Neuroimaging Initiative of the European Alzheimer’s Disease Consortium. Alzheimers Dement. 2008;4:255–264. [PMC free article] [PubMed]
23. Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, et al. the Alzheimer’s Disease Neuroimaging Initiative. Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer’s disease mild cognitive impairment, and elderly controls. Neuroimage. 2008;43:59–68. [PMC free article] [PubMed]
24. Hua X, Leow AD, Parikshak N, Lee S, Chiang MC, Toga AW, Jack CR, Jr, Weiner MW, Thompson PM. the Alzheimer’s Disease Neuroimaging Initiative. Tensor-based morphometry as a neuroimaging biomarker for Alzheimer’s disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage. 2008;43:458–469. [PMC free article] [PubMed]
25. Becker RE, Greig NH. Alzheimer’s disease drug development: old problems require new priorities. CNS Neurol Disord Drug Targets. 2008;7:499–511. [PubMed]
26. Walhovd KB, Fjell AM, Dale AM, McEvoy LK, Brewer J, Karow DS, Salmon DP, Fennema-Notestine C. the Alzheimer’s Disease Neuroimaging Initiative. Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiol Aging. (in press) [PMC free article] [PubMed]
27. Clark CM, Davatzikos C, Borthakur A, Newberg A, Leight S, Lee VM-Y, Trojanowski JQ. Biomarkers for early detection of Alzheimer pathology. NeuroSignals. 2008;16:11–18. [PMC free article] [PubMed]
28. Mormino EC, Kluth JT, Madison CM, Rabinovici GD, Baker SL, Miller BL, et al. the Alzheimer’s Disease Neuroimaging Initiative. Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain. 2009;132(Pt. 5):1310. [PMC free article] [PubMed]
29. Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, et al. the Alzheimer’s Disease Neuroimaging Initiative. Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Neuroimage. 2009;45 Suppl 1:S3–S15. [PMC free article] [PubMed]
30. Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, et al. the Alzheimer’s Disease Neuroimaging Initiative. Cerebrospinal fluid biomarker signature in Alzheimer’s Disease Neuroimaging Initiative subjects. Ann Neurol. 2009;65:403–413. [PMC free article] [PubMed]
31. Morra J, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, et al. Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Human Brain Mapping. 2009;30:2766–2788. [PMC free article] [PubMed]
32. Misra C, Fan Y, Davatzikos C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage. 2009;44:1415–1422. [PMC free article] [PubMed]
33. Leow AD, Yanovsky I, Parikshak N, Hua X, Lee S, Toga AW, et al. the Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s Disease Neuroimaging Initiative: a one-year follow-up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition. Neuroimage. 2009;45:645–655. [PMC free article] [PubMed]
34. Langbaum JBS, Chen K, Lee W, Reschke C, Bandy D, Fleisher AS, et al. the Alzheimer’s Disease Neuroimaging Initiative. Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Neuroimage. 2009;45:1107–1116. [PMC free article] [PubMed]
35. Weiner MW. Imaging and biomarkers will be used for detection and monitoring progression of early Alzheimer’s Disease [Editorial] J Nutr Health Aging. 2009;13:332. [PubMed]
36. Schuff N, Woerner N, Boreta L, Kornfield T, Shaw LM, Trojanowski JQ, Thompson PM, Jack CR, Jr, Weiner MW. the Alzheimer’s Disease Neuroimaging Initiative. MRI of hippocampal volume loss in early Alzheimer’s Disease in relation to ApoE genotype and biomarkers. Brain. 2009;132(Pt. 4):1067–1077. [PMC free article] [PubMed]
37. McEvoy LK, Fennema-Notestine C, Cooper JC, Hagler D, Jr, Holland D, Karow DS, Pung CJ, Brewer JB, Dale AM. for the Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s Disease: quantitative structural neuroimaging for detection and prediction clinical and structural changes in mild cognitive impairment. Radiology. 2009;251:195–205. [PubMed]
38. Chupin M, Gerardin E, Cuingnet R, Boutet C, Lemieux L, Lehericy S, Benali H, Garnero L, Colliot O. Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus. 2009;19:579–587. [PMC free article] [PubMed]
39. Jack CR, Jr, Lowe VJ, Weigand SD, Wiste HJ, Senjem ML, Knopman DS, et al. the Alzheimer’s Disease Neuroimaging Initiative. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: implications for sequence of pathological events in Alzheimer’s disease. Brain. 2009;132(Pt. 5):1355–1365. [PMC free article] [PubMed]
40. Potkin SG, Guffanti G, Lakatos A, Turner JA, Kruggel F, Fallon JH, et al. Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer’s disease. PLoS ONE. 2009;4:e6501–e6515. [PMC free article] [PubMed]
41. Chou YY, Leporé N, Avedissian C, Madsen SK, Parikshak N, Hua X, et al. the Alzheimer’s Disease Neuroimaging Initiative. Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 Subjects with Alzheimer’s disease, mild cognitive impairment and elderly controls. Neuroimage. 2009;46:394–410. [PMC free article] [PubMed]
42. Kovacevic S, Rafii MS, Brewer BJ, the Alzheimer’s Disease Neuroimaging Initiative High-throughput, fully-automated volumetry for prediction of MMSE and CDR decline in mild cognitive impairment. Alzheimer Dis Assoc Disord. 2009;23:139–145. [PMC free article] [PubMed]
43. Clarkson MJ, Ourselin S, Neilsen C, Leung KK, Barnes J, Whitwell JL, et al. Comparison of phantom and registration scaling corrections using the ADNI cohort. Neuroimage. 2009;47:1506–1513. [PMC free article] [PubMed]
44. Hua X, Lee S, Yanovsky I, Leow AD, Chou YY, Ho AJ, et al. the Alzheimer’s Disease Neuroimaging Initiative. Optimizing power to track brain degeneration in Alzheimer’s disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects. Neuroimage. 2009;48:668–681. [PMC free article] [PubMed]
45. Hinrichs C, Singh V, Mukherjee L, Xu G, Chung MK, Johnson SC. the Alzheimer’s Disease Neuroimaging Initiative. Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage. 2009;48:138–149. [PMC free article] [PubMed]
46. Gunter JL, Bernstein MA, Borowski BJ, Ward CP, Britson PJ, Felmlee JP, Schuff N, Weiner M, Jack CR. Measurement of MRI scanner performance with the ADNI phantom. Med Phys. 2009;36:2193–2205. [PubMed]
47. King RD, George AT, Jeon T, Hynan LS, Youn TS, Kennedy DN, Dickerson B. the Alzheimer’s Disease Neuroimaging Initiative. Characterization of atrophic changes in the cerebral cortex using fractal dimensional analysis. Brain Imag Behav. 2009;3:154–166. [PMC free article] [PubMed]
48. Petersen RC, Trojanowski JQ. Use of Alzheimer’s disease biomarkers: potentially yes for clinical trials, but not yet for clinical practice. JAMA. 2009;302:436–437. [PMC free article] [PubMed]
49. Vemuri P, Wiste HJ, Weigand SD, Shaw LM, Trojanowski JQ, Weiner M, Knopman DS, Petersen RC, Jack CR, Jr, the Alzheimer’s Disease Neuroimaging Initiative MRI and CSF biomarkers in normal, MCI, AD: diagnostic discrimination and cognitive correlations. Neurol. 2009;73:287–293. [PMC free article] [PubMed]
50. Vemuri P, Wiste HJ, Weigand SD, Shaw LM, Trojanowski JQ, Weiner M, Knopman DS, Petersen RC, Jack CR, Jr, the Alzheimer’s Disease Neuroimaging Initiative MRI and CSF biomarkers in normal, MCI, AD: predicting future clinical change. Neurology. 2009;73:294–301. [PMC free article] [PubMed]
51. McDonald CR, McEvoy LK, Gharapetian L, Fennema-Notestine C, Hagler DJ, Jr, Holland D, Koyama A, Brewer JB, Dale AM, the Alzheimer’s Disease Neuroimaging Initiative Regional rates of neocortical atrophy from normal aging to early Alzheimer disease. Neurology. 2009;73:457–465. [PMC free article] [PubMed]
52. Querbes O, Aubry F, Pariente J, Lotterie JA, Demonet JF, Duret V, et al. the Alzheimer’s Disease Neuroimaging Initiative. Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain. 2009;132:2036–2047. [PMC free article] [PubMed]
53. Risacher SL, Saykin AJ, West JD, Shen L, Firpi HA, McDonald BC, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res. 2009;6:347–361. [PMC free article] [PubMed]
54. Jagust WJ, Landau SM, Shaw LM, Trojanowski JQ, Koeppe RA, Reiman EM, the Alzheimer’s Disease Neuroimaging Initiative et al. Relationships between biomarkers in aging and dementia. Neurology. 2009;73:1193–1199. [PMC free article] [PubMed]
55. Petersen RC. Commentary on “A roadmap for the prevention of dementia II: Leon Thal Symposium 2008. a national registry on aging. Alzheimers Dement. 2009;5:105–107. [PMC free article] [PubMed]
56. Petersen RC. Early diagnosis of Alzheimer’s disease: is MCI too late? Curr Alzheimer Res. 2009;6:324–330. [PMC free article] [PubMed]
57. Petersen RC, Jack CR., Jr Imaging and biomarkers in early Alzheimer’s disease and mild cognitive impairment. Clin Pharmacol Ther. 2009;86:438–441. [PMC free article] [PubMed]
58. Huang A, Abugharbieh R, Tam R, the Alzheimer’s Disease Neuroimaging Initiative A hybrid geometric-statistical deformable model for automated 3-D segmentation in brain MRI. IEEE Trans Biomed Eng. 2009;56:1838–1848. [PMC free article] [PubMed]
59. Langbaum JB, Chen K, Lee W, Reschke C, Bandy D, Fleisher AS, et al. the Alzheimer’s Disease Neuroimaging Initiative. Categorical and correlational analyses of baseline fluorodeox-yglucose positron emission tomography images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Neuroimage. 2009;45:11078–11116. [PMC free article] [PubMed]
60. Hirschman C. Connections: News from the Alzheimer’s Disease Education and Referral (ADEAR) Center. National Institute on Aging; Alzheimer’s Disease Neuroimaging Initiative (ADNI) Generates Promising Early Findings. Fall 2009. Available at: http://www.nia.nih.gov/Alzheimers/ResearchInformation/Newsletter/Fall2009/feature01.htm.
61. Fjell AM, Walhovd KB, Fennema-Notestine C, McEvoy LK, Hagler DJ, Holland D, Brewer JB, Dale AM. One-year brain atrophy evident in healthy aging. J Neurosci. 2009;29:15223–15231. [PMC free article] [PubMed]
62. Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, Foster NL, Weiner MW, Jagust WJ. the Alzheimer’s Disease Neuroimaging Initiative. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging. (in press) [PMC free article] [PubMed]
63. Fennema-Notestine C, Hagler DJ, Jr, McEvoy LK, Fleisher AS, Wu EH, Karow DS, Dale AM, the Alzheimer’s Disease Neuroimaging Initiative Structural MRI biomarkers for preclinical and mild Alzheimer’s disease. Hum Brain Mapp. (in press) [PMC free article] [PubMed]
64. Ho AJ, Hua X, Lee S, Leow AD, Yanovsky I, Gutman B, et al. the Alzheimer’s Disease Neuroimaging Initiative. Comparing 3 T and 1.5 T MRI for tracking Alzheimer’s disease progression with tensor-based morphometry. Hum Brain Mapp. (in press) [PMC free article] [PubMed]
65. Chang YL, Jacobson MW, Fennema-Notestine C, Hagler DJ, Jr, Jennings RG, Dale AM, McEvoy LK. the Alzheimer’s Disease Neuroimaging Initiative. Level of executive function influences verbal memory in amnestic mild cognitive impairment and predicts prefrontal and posterior cingulate thickness. Cereb Cortex. (in press) [PMC free article] [PubMed]
66. Haense C, Herholz K, Jagust WJ, Heiss WD. Performance of FDG PET for detection of Alzheimer’s disease in two independent multi-centre samples (NEST-DD and ADNI) Dement Geriatr Cogn Disord. 2009;28:259–266. [PubMed]
67. Hampel H, Shen Y, Walsh DM, Aisen P, Shaw LM, Zetterberg H, Trojanowski JQ, Blennow K. Biological markers of β-amyloid related mechanisms in Alzheimer’s disease. Exper Neurol. (in press) [PMC free article] [PubMed]
68. Petersen RC, Knopman DS, Boeve BF, Geda YE, Ivnik RJ, Smith GE, Roberts RO, Jack CR., Jr Mild cognitive impairment ten years later. Arch Neurol. 2009;66:1447–1455. [PMC free article] [PubMed]
69. Fleisher AS, Donohue M, Chen K, Brewer JB, Aisen PS. the Alzheimer’s Disease Neuroimaging Initiative. Applications of neuroimaging to disease-modification trials in Alzheimer’s disease. Behav Neurol. 2009;21:129–136. [PubMed]
70. Holland D, Brewer JB, Hagler DJ, Fenema-Notestine C, Dale AM. the Alzheimer’s Disease Neuroimaging Initiative. Subregional neuroanatomical change as a biomarker for Alzheimer’s disease. Proc Natl Acad Sci U S A. 2009;106:20954–20959. [PubMed]
71. Cruchaga C, Kauwe JS, Bertelsen S, Nowotny P, Shah AR, et al. SNPs in the regulatory subunit of calcineurin are associated with CSF tau protein levels, brain mRNA levels. Alzheimers Dement. 2009;5:471–472.
72. Joyner AH, Roddey CJ, Bloss CS, Bakken TE, Rimol LM, Melle I, et al. A common MECP2 haplotype associates with reduced cortical surface area in two independent populations. Proc Natl Acad Sci USA. 2009;106:15483–15488. [PubMed]
73. Ito K, Ahadleh S, Corrigan B, French J, Fullerton T, Tensfeldt T. the Alzheimer’s Disease Neuroimaging Initiative. Disease progression meta-analysis model in Alzheimer’s disease. Alzheimers Dement. 2009;6:39–53. [PubMed]
74. Petersen RC, Aisen PS, Beckett LA, Donahue MJ, Gamst AC, Harvey DJ, et al. the Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurol. 2010;74:201–209. [PMC free article] [PubMed]
75. Fjell AM, Amlien IK, Westlye LT, Stenset V, Fladby T, Skinningsrud A, Eilsertsen DE, Bjørnerud A, Walhovd KB. CSF biomarker pathology correlates with a medial temporo-parietal network affected by very mild to moderate Alzheimer’s disease but not a fronto-striatal network affected by healthy aging. Neuroimage. 2010;49:1820–1830. [PubMed]
76. Rimol LM, Agartz I, Djurovic S, Brown AA, Roddey JC, Kähler AK, et al. the Alzheimer’s Disease Neuroimaging Initiative. Sex-dependent association of common variants of microcephaly genes with brain structure. Proc Natl Acad Sci U S A. 2010;107:384–388. [PubMed]
77. Jack CR, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Petersen RC, Weiner MW, Trojanowski JQ. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9:119–128. [PMC free article] [PubMed]
78. Wolz R, Aljabar P, Hajnal JV, Hammers A, Rueckert D. the Alzheimer’s Disease Neuroimaging Initiative. LEAP: learning embeddings for atlas propagation. Neuroimage. 2010;49:1316–1325. [PMC free article] [PubMed]
79. Kruggel F, Turner J, Muftuler LT, the Alzheimer’s Disease Neuroimaging Initiative Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort. Neuroimage. 2010;49:2123–2133. [PMC free article] [PubMed]
80. Lötjönen JM, Wolz R, Koikkalainen JR, Thurfjell L, Waldemar G, Soininen H, Rueckert D, the Alzheimer’s Disease Neuroimaging Initiative Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage. 2010;49:2352–2365. [PubMed]
81. Leung KK, Clarkson MJ, Bartlett JW, Clegg S, Jack CR, Jr, Weiner MW, Fox NC, Ourselin S, the Alzheimer’s Disease Neuroimaging Initiative Robust atrophy rate measurement in Alzheimer’s disease using multi-site serial MRI: tissue-specific intensity normalization and parameter selection. Neuroimage. 2010;50:516–523. [PMC free article] [PubMed]
82. Yushkevich PA, Avants BB, Das SR, Pluta J, Altinay M, Craige C. the Alzheimer’s Disease Neuroimaging Initiative. Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: an illustration in ADNI 3 T MRI data. Neuroimage. 2010;50:434–445. [PMC free article] [PubMed]
83. Walhovd KB, Fjell AM, Brewer J, McEvoy LK, Fennema-Notestine C, Hagler DJ, Jr, Jennings RG, Karow D, Dale AM. the Alzheimer’s Disease Neuroimaging Initiative. Combining MR imaging, Positron-Emission Tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer’sdisease. AJNR Am J Neuroradiol. 2010;31:347–354. [PMC free article] [PubMed]
84. Cronk BB, Johnson DK, Burns JM. the Alzheimer’s Disease Neuroimaging Initiative. Body mass index and cognitive decline in mild cognitive impairment. Alzheimer Dis Assoc Disord. (in press) [PMC free article] [PubMed]
85. Kauwe JS, Bertelsen S, Mayo K, Cruchaga C, Abraham R, Holling-worth P, et al. the Alzheimer’s Disease Neuroimaging Initiative. Suggestivesynergy between genetic variants in TF and HFE as risk factors for Alzheimer’s disease. Am J Med Genet B Neuropsychiatr Genet. (in press) [PMC free article] [PubMed]
86. Chang YL, Bondi MW, Fennema-Notestine C, McEvoy LK, Hagler DJ, Jr, Jacobson MW, Dale AM. the Alzheimer’s Disease Neuroimaging Initiative. Brain substrates of learning and retention in mild cognitive impairment diagnosis and progression to Alzheimer’s disease. Neuropsychologia. (in press) [PMC free article] [PubMed]
87. Fjell AM, Walhovd KB, Fennema-Notestine C, McEvoy LK, Hagler DJ, Holland D, Blennow K, Brewer JB, Dale AM. the Alzheimer’s Disease Neuroimaging Initiative. Brain atrophy in healthy aging is related to CSF levels of A{beta}1–42. Cereb Cortex. (in press) [PMC free article] [PubMed]
88. Franke K, Ziegler G, Klöppel S, Gaser C, the Alzheimer’s Disease Neuroimaging Initiative Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage. 2010;50:883–892. [PubMed]
89. Shen L, Kim S, Risacher SL, Nho K, Swaminathan S, West JD, et al. the Alzheimer’s Disease Neuroimaging Initiative. Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: a study of the ADNI cohort. Neuroimage. (in press) [PMC free article] [PubMed]
90. Okonkwo OC, Alosco ML, Griffith HR, Mielke MM, Shaw LM, Trojanowski JQ, Tremont G. the Alzheimer’s Disease Neuroimaging Initiative. CSF abnormalities and rate of decline in everyday function. Arch Neurol. (in press) [PMC free article] [PubMed]
91. De Meyer G, Shapiro F, Vanderstichele H, Vanmechelen E, Engleborghs B, De Deyn P-P, et al. the Alzheimer’s Disease Neuroimaging Initiative. Diagnosis-independent Alzheimer’s disease biomarker signature in cognitively normal elderly people. Arch Neurol. (in press) [PMC free article] [PubMed]
92. McEvoy LK, et al. Enrichment strategies for secondary prevention trials in Alzheimer’s disease. Alzheimer’s Dis Assoc Diss. (in press) [PMC free article] [PubMed]
93. Ott B, et al. Complex relationships between ventricular volume and cerebrospinal fluid biomarkers of Alzheimer’s disease. J Alzheimers Dis. (in press)
94. Thompson P, Hua X, Lee S, Hibar DP, Yanovsky I, Leow AD, et al. the Alzheimer’s Disease Neuroimaging Initiative. Mapping Alzheimer’s disease progression in 1309 MRI scans: power estimates for different inter scan intervals. Neuroimage. (in press) [PMC free article] [PubMed]
95. Vemuri P, Wiste HJ, Weigand SD, Knopman DS, Shaw LM, Trojanowski JQ, et al. the Alzheimer’s Disease Neuroimaging Initiative. Effect of APOE on biomarkes of amyloid load and neuronal pathology in Alzheimer’s disease. Ann Neurol. (in press) [PMC free article] [PubMed]
96. De Meyer G, Shapiro F, Vanderstichele H, Vanmechelen E, Engleborghs B, De Deyn P-P, et al. the Alzheimer’s Disease Neuroimaging Initiative. A mixture modeling approach to biomarker assessment reveals an Alzheimer’s disease signature in more than a third of cognitively normal elderly people. Arch Neurol. 2010 (in press)
97. Ewers M, Walsh C, Trojanowski JQ, Shaw LM, Petersen RC, Jack CR, Jr, et al. the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Multi-modal biological marker based signature and diagnosis of early Alzheimer’s disease. Neurobiol Aging. 2010 (in press)
98. Ewers M, Faluyi YO, Bennett D, Trojanowski JQ, Shaw LM, Petersen R, et al. the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Body mass index associated with biological CSF markers of core brain pathology in mild cognitive impairment and Alzheimer’s disease. Neurology. 2010 (in press)
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