Alzheimer's Disease Neuroimaging Initiative (ADNI)

By Ronald C. Petersen, P. Aisen, L. Beckett, M. Donohue, A. Gamst, D. Harvey, Clifford R. Jack Jr., William J. Jagust, L. Shaw, Arthur W. Toga and 2 more. Neurology, 2010

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Unsupervised End-to-end Learning for Deformable Medical Image Registration (2017, methodology): “We randomly select 35 normal patients provided by ADNI [46] (Alzheimer’s Disease Neuroimaging Initiative) to enlarge our training set.”
Microglia in Alzheimer’s disease (2018, background): “A combined analysis of neuroimaging and genetic data found an association between a polymorphism in the IL1RAP gene and increased rate of Aβ accumulation in human brains of both AD and non-AD subjects (measured by Aβ-binding positron emission tomography tracer; Ramanan et al., 2015).”
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, positive): “Note that for the ADNI and AIBL datasets, three diagnosis labels (CN, MCI and AD) exist and are assigned by a physician after a series of clinical tests (Ellis et al., 2010, 2009; Petersen et al., 2010) while for OASIS only two diagnosis labels exist, CN and AD (the MCI subjects are labelled as AD)”
The changing prevalence and incidence of dementia over time — current evidence (2017, background): “3 Neuroscience and epidemiology Current neuroscience research has largely invested in mechanistic research for treatments alongside searches for potential biomarkers for diagnosis of dementia subtypes, now preceding any clinical signs, and monitoring of treatment efficacy in highly selected clinical”