Alzheimer's Disease Neuroimaging Initiative

Primary goals

History and funding

The idea of a collaboration between public institutions and private pharmaceutical companies to fund a large biomarker project to study AD and to speed up progress toward effective treatments for the disease was conceived at the beginning of the millennium by Neil S. Buckholz at the National Institute on Aging (NIA) and Dr. William Potter, at Eli Lilly and Company. The Alzheimer's Disease Neuroimaging Initiative (ADNI) began in 2004 under the leadership of Dr. Michael W. Weiner, funded as a private – public partnership with $27 million contributed by 20 companies and two foundations through the Foundation for the National Institutes of Health and $40 million from the NIA. The initial five-year study (ADNI-1) was extended by two years in 2009 by a Grant Opportunities grant, and in 2011 and 2016 by further competitive renewals of the ADNI-1 grant (ADNI-2 and ADNI-3, respectively) (Table 1).

Enrollment of participants

ADNI enrolls participants between the ages of 55 and 90 who are recruited at 57 sites in the US and Canada. One group has dementia due to AD, another group has mild memory problems known as mild cognitive impairment (MCI), and the final control group consists of healthy elderly participants. ADNI-1 initially enrolled 200 healthy elderly, 400 participants with MCI, and 200 participants with AD. ADNI-GO, ADNI-2 and ADNI -3 added additional participants to augment the cohort, for final cohort size of over 1000 participants (Table 1).

Table 1:

Testing of participants

ADNI uses a variety of techniques to study its participants. After obtaining informed consent, participants undergo a series of initial tests that are repeated at intervals over subsequent years (Table 2):

Table 2

Data sharing

One defining characteristic of ADNI is the commitment by all participating research groups to share ownership of the data prior to the completion of the research and by collaborators to forgo any patent opportunities. This has been described by the head of the ADNI and data publications committee as "a radical experiment in open data access". All data generated by the ADNI study are entered into the data archive hosted at the Laboratory of NeuroImaging (LONI) at the University of Southern California. In 2013, whole genome sequencing data for the entire ADNI cohort were added to the LONI database. Qualified researchers worldwide can access image and clinical data sets that have undergone quality control procedures. To date nearly 1800 applications for data use have been received from investigators in multiple disciplines, and over 7 million brain scan images and clinical data sets have been downloaded.

ADNI contributes data to a number of consortia and big data projects which have the potential to unlock many of the mysteries of neurological diseases. It shares imaging and genetic data with the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium which uses imaging genetics to study 12 major brain diseases including schizophrenia, bipolar disease and depression. The ADNI dataset was also used as the "test" dataset in the Dialogue on Reverse Engineering Assessment and Methods (DREAM) Alzheimer's disease Big Data Challenge #1 for the discovery of novel predictive AD biomarkers. One measure of the success of this open data sharing approach is the number of scientific publications arising from ADNI data: currently over 1000 and a wide variety of fields including areas outside of Alzheimer's disease.

Development of standardized protocols

ADNI has developed standardized protocols that allow results from multiple centers both within the study and worldwide to be directly compared. These include methods for the acquisition and quality control of both MRI and PET scans on scanners differing in the vendor, software platform, and field strength, and also for the analysis of CSF biomarkers. The standardized methods are now used by pharmaceutical companies, and in clinical trials of preventive and disease modifying AD treatments.

Biomarker trajectories throughout disease progression

An initial goal of ADNI was to understand the development of AD pathology by tracking imaging and CSF biomarkers throughout disease progression according to the amyloid hypothesis.

A model of how different AD biomarkers change during the development of the AD proposed that biomarkers become abnormal in the following order:

1. β-amyloid (indicating deposition of amyloid in plaques outside the cell, measured in CSF and by amyloid PET)
2. Tau (indicating the formation of tau fibrils with the neurons)
3. Glucose metabolism (measured on PET, indicating damage to neurons)
4. Structural MRI (indicating damage to brain structure)
5. Cognitive impairment

This model has been largely validated using longitudinal ADNI data in patients who have abnormal levels of amyloid deposition, consistent with the amyloid hypothesis.

Other significant findings

Studies using ADNI cross-sectional and longitudinal MRI, PET, genetics, cognitive, biological fluid, and autopsy data have reported that:

Diagnosis of AD and the prediction of future AD

ADNI data has been used to test many diagnostic and prognostic machine learning algorithms. The most successful to date have used deep learning approaches that combine longitudinal data chronicling changes in biomarkers over time from more than one imaging, genetic, or biological modality.

Diagnosis

One example of a combination of biomarkers that can accurately diagnose AD is:

1. Changes in brain atrophy patterns over time (measured by MRI)
2. Levels of β-amyloid and tau (measured in CSF)

A second approach to diagnosis is to extract the most pertinent information from MRI scans alone. Deep learning algorithms can diagnose AD with greater than 95% accuracy, and can diagnose MCI due to AD with greater than 82% accuracy.

As imaging scans are expensive and sometimes unavailable, and the analysis of CSF requires an invasive lumbar puncture procedure, ADNI blood samples are being used to develop diagnostic blood tests for clinical use. These are currently not as accurate as other methods.

Prediction

Deep learning algorithms which extract the most pertinent information from MRI scans can also predict the progression of MCI patients to AD several years in advance with accuracies of greater than 90%.

Development of biomarkers for clinical trials

The major aim of ADNI is to develop biomarkers to enable successful clinical trials. AD clinical trials are now focusing on preventing the disease rather than curing it. Because AD pathology develops many years before outward signs of the disease such as memory loss, preventive therapies are targeted to cognitively normal people. ADNI studies have focused on two aspects of clinical trials in particular: 1) how best to select trial participants who don't yet show any signs of cognitive impairment but who are at a high risk of developing AD (subject selection); and 2) how to detect the effect of a therapy (outcome measures).

Subject selection

ADNI studies have shown that people who are β-amyloid positive or have a small hippocampal volume, or carry an APOE ε4 allele are at a higher risk for AD. Therefore clinical trial participants can be selected using these criteria. (39). Moreover, use of the selection strategy can reduce the number of participants required to detect a treatment effect over feasible trial (for example 3 years).

1. β-amyloid positivity. Currently, the phase 3 A4 trial testing the anti-amyloid antibody solanezumab, uses β-amyloid positivity to select elderly participants with no outward signs of AD.
2. Hippocampal volume. Hippocampal volume can differentiate between MCI patients who will go on to develop AD from those who are on different disease pathways. This reduces the number of participants required for effective clinical trials. Hippocampal volume is the first imaging biomarker to be qualified, with the help of ADNI data, by the European Medicines Agency to select patients for clinical trials. ADNI also contributed to the development of a standardized technique to manually measure hippocampal volume from MRI scans for use in clinical trials.
3. APOE ε4 allele. As this allele is the biggest risk factor for late onset AD, it is commonly used in subject selection.

Outcome measures

In the US, only cognitive tests have been approved as outcome measures for detecting clinical change in AD clinical trials. Studies using ADNI data have helped refine these tests to be more sensitive to very early changes in cognition. ADNI is working to develop imaging biomarkers such as various brain atrophy MRI measures as alternative outcome measures to these cognitive tests.

Future directions

ADNI-3 will follow current and additional patients with normal cognition, MCI, and AD for a further five years. The approach is unique to the study are:

1. Use of web-based methods for cognitive assessment of patients in conjunction with the Brain Health Registry.
2. Use of tau PET imaging to determine how tau tangles are related to amyloid levels and to cognition
3. Development of tau PET as outcome measure to replace cognitive outcome measures for AD clinical trials
4. Use of Human Connectome Project MRI techniques to map the effects of AD on brain connectivity
5. Use of Systems biology approaches to understand AD genetics and its relationship to AD biology
6. Use of high-powered MRI to detect very early structural brain changes associated with AD in patients with no symptoms.
7. Development of models to select participants for AD clinical trials using Precision medicine approaches

Other studies modeled on ADNI

ADNI's organization, funding structures, standardized methodologies, and open datasharing approaches have been used in a number of different studies.

Alzheimer's disease related

Other diseases