{"url":"/dataset/adni","name":"ADNI","full_name":"Alzheimer's Disease NeuroImaging Initiative","description_markdown":"Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD).[1] This cooperative study combines expertise and funding from the private and public sector to study subjects with AD, as well as those who may develop AD and controls with no signs of cognitive impairment.[2] Researchers at 63 sites in the US and Canada track the progression of AD in the human brain with neuroimaging, biochemical, and genetic biological markers.[2][3] This knowledge helps to find better clinical trials for the prevention and treatment of AD. ADNI has made a global impact,[4]\r\n firstly by developing a set of standardized protocols to allow the comparison of results from multiple centers,[4] and secondly by its data-sharing policy which makes available all at the data without embargo to qualified researchers worldwide.[5] To date, over 1000 scientific publications have used ADNI data.[6] A number of other initiatives related to AD and other diseases have been designed and implemented using ADNI as a model.[4] ADNI has been running since 2004 and is currently funded until 2021.[7]\r\n\r\nSource: Wikipedia, https://en.wikipedia.org/wiki/Alzheimer%27s_Disease_Neuroimaging_Initiative","description_withheld":null,"homepage":"http://adni.loni.usc.edu/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"MRI","url":"/datasets/modality/mri"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Graph Classification","url":"/task/graph-classification","datasets_with_task":"/datasets/task/graph-classification"},{"name":"Linear-Probe Classification","url":"/task/linear-probe-classification","datasets_with_task":"/datasets/task/linear-probe-classification"},{"name":"Alzheimer's Disease Detection","url":"/task/alzheimer-s-disease-detection","datasets_with_task":"/datasets/task/alzheimer-s-disease-detection"},{"name":"Cubic splines Image Registration","url":"/task/cubic-splines-image-registration","datasets_with_task":"/datasets/task/cubic-splines-image-registration"},{"name":"Explainable Artificial Intelligence (XAI)","url":"/task/xai","datasets_with_task":"/datasets/task/xai"},{"name":"Stable MCI vs Progressive MCI","url":"/task/stable-mci-vs-progressive-mci","datasets_with_task":"/datasets/task/stable-mci-vs-progressive-mci"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ADNI"],"data_loaders":[],"num_papers_in_archive":28,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/alzheimer-s-disease-detection-on-adni","task":"Alzheimer's Disease Detection","dataset_variant":"ADNI","rows":1,"metrics":["Accuracy (5-fold)","MCC (5-fold)"],"first_row_in_archive_order":{"model":"AXIAL","paper":"/paper/axial-attention-based-explainability-for","metrics":{"Accuracy (5-fold)":"85.6%","MCC (5-fold)":"0.712"},"code_links":[{"title":"GabrieleLozupone/AXIAL","url":"https://github.com/GabrieleLozupone/AXIAL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-detection-on-adni","task":"Anomaly Detection","dataset_variant":"ADNI","rows":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"Brainomaly","paper":"/paper/brainomaly-unsupervised-neurologic-disease","metrics":{"AUC":"0.6550"},"code_links":[{"title":"mahfuzmohammad/brainomaly","url":"https://github.com/mahfuzmohammad/brainomaly"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/explainable-artificial-intelligence-xai-on","task":"Explainable Artificial Intelligence (XAI)","dataset_variant":"ADNI","rows":1,"metrics":["AD-Related Brain Areas Identified"],"first_row_in_archive_order":{"model":"AXIAL","paper":"/paper/axial-attention-based-explainability-for","metrics":{"AD-Related Brain Areas Identified":"hippocampus, amygdala, parahippocampal, inferior lateral ventricles"},"code_links":[{"title":"GabrieleLozupone/AXIAL","url":"https://github.com/GabrieleLozupone/AXIAL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/graph-classification-on-adni","task":"Graph Classification","dataset_variant":"ADNI","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"NeuroPath","paper":"/paper/neuropath-a-neural-pathway-transformer-for","metrics":{"Accuracy":"85.56"},"code_links":[{"title":"Chrisa142857/neuro_detour","url":"https://github.com/Chrisa142857/neuro_detour"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/stable-mci-vs-progressive-mci-on-adni","task":"Stable MCI vs Progressive MCI","dataset_variant":"ADNI","rows":1,"metrics":["Accuracy (5-fold)","MCC (5-fold)"],"first_row_in_archive_order":{"model":"AXIAL","paper":"/paper/axial-attention-based-explainability-for","metrics":{"Accuracy (5-fold)":"72.5%","MCC (5-fold)":"0.443"},"code_links":[{"title":"GabrieleLozupone/AXIAL","url":"https://github.com/GabrieleLozupone/AXIAL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/neuropath-a-neural-pathway-transformer-for","title":"NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes","date":"2024-09-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/axial-attention-based-explainability-for","title":"AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans","date":"2024-07-02","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/brainomaly-unsupervised-neurologic-disease","title":"Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images","date":"2023-02-18","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}