Alzheimer's Disease Neuroimaging Initiative: Two decades of pioneering Alzheimer's disease research and future directions
Bibliographic record
Abstract
This special issue of Alzheimer's & Dementia celebrates the accomplishments of the Alzheimer's Disease Neuroimaging Initiative (ADNI) project as it approaches its 20th anniversary in 2024, supported by funding from the National Institute on Aging.The earliest origins of ADNI lie in the groundbreaking discovery that antibodies targeting amyloid can remove amyloid beta (Aβ) plaques, sparking the advent of immunotherapy for Alzheimer's disease (AD). 1 At the same time, the importance of imaging and fluid biomarkers, particularly cerebrospinal fluid (CSF), in AD diagnosis gained recognition.ADNI was established in 2004 to validate and optimize biomarkers for AD clinical trials and freely share all the generated data with the scientific community without any restrictions.Since then, ADNI has evolved through five sequential phases incorporating advancements in the field and contributing to significant breakthroughs such as standardizing and validating Aβ and tau positron emission tomography (PET) imaging and CSF biomarkers.2-4 Moreover, ADNI data informed the design of clinical trials for aducanumab, 5 lecanemab, 6 donanemab, 7 solanezumab, 8 verubecestat, 9 crenezumab, 10 and gantenerumab, 11 facilitating the introduction of disease-modifying treatments into clinical practice.ADNI's open data sharing has led to over 6000 peer-reviewed publications, further highlighting the impact of the study.Furthermore, ADNI's approach to conducting longitudinal observational studies and openly sharing data served as a model for similar initiatives globally, leading to the creation of consortia like the Parkinson's Progression Markers Initiative (PPMI), 12 Japanese ADNI, 13 European ADNI, 14 Korean Brain Aging Study (KBASE), 15,16 China ADNI, 17 and South American initiatives.18 The following projects were also modeled on ADNI: the Dominantly Inherited Alzheimer Network (DIAN) studies, 19 Alzheimer's Disease Research Center Consortium for Clarity in Alzheimer's Disease and Related Dementias Research Through Imaging (CLARiTI), 20 Longitudinal Early-Onset Alzheimer's Disease Study (LEADS), 21 Australian Imaging Biomarker & Lifestyle Flagship Study of Ageing (AIBL), 22 Advancing Research and Treatment in Frontotemporal Lobar Degeneration and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects Longitudinal Frontotemporal Lobar Degeneration (ALLFTD), 23 Diverse Vascular Contributions to Cognitive Impairment and Dementia (VCID), 24 and Biomarkers for Vascular Contributions to Cognitive Impairment and Dementia (MarkVCID).25
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".