Open Access
Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials
Michael W. Weiner,Dallas P. Veitch,Paul S. Aisen,Laurel A. Beckett,Nigel J. Cairns,Robert C. Green,Danielle J Harvey,Clifford R. Jack,William J. Jagust,John C. Morris,Ronald C. Petersen,Andrew J. Saykin,Leslie M. Shaw,Arthur W. Toga,John Q. Trojanowski,Alzheimer’s Disease Neuroimaging Initiative +15 more
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TLDR
The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers.About:
The article was published on 2017-04-01 and is currently open access. It has received 169 citations till now. The article focuses on the topics: Alzheimer's Disease Neuroimaging Initiative & Biomarker (medicine).read more
Citations
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Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks
Silvia Basaia,Federica Agosta,Luca Wagner,Elisa Canu,Giuseppe Magnani,Roberto Santangelo,Massimo Filippi +6 more
TL;DR: A deep learning algorithm is built and validated predicting the individual diagnosis of Alzheimer's disease and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan, demonstrating that it is exploitable by not-trained operators and likely to be generalizable to unseen patient data.
Journal ArticleDOI
Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative
Dallas P. Veitch,Michael W. Weiner,Paul S. Aisen,Laurel A. Beckett,Nigel J. Cairns,Robert C. Green,Danielle J Harvey,Clifford R. Jack,William J. Jagust,John C. Morris,Ronald C. Petersen,Andrew J. Saykin,Leslie M. Shaw,Arthur W. Toga,John Q. Trojanowski +14 more
TL;DR: Select topics that provide insights into AD progression are discussed and how this knowledge may improve clinical trials are outlined.
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Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images
TL;DR: This paper proposes a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network and delivers 82.4% accuracy and 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD.
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Altered bile acid profile in mild cognitive impairment and Alzheimer's disease: Relationship to neuroimaging and CSF biomarkers.
Kwangsik Nho,Alexandra Kueider-Paisley,Siamak MahmoudianDehkordi,Matthias Arnold,Shannon L. Risacher,Gregory Louie,Colette Blach,Rebecca Baillie,Xianlin Han,Gabi Kastenmüller,Wei Jia,Guoxiang Xie,Shahzad Ahmad,Thomas Hankemeier,Cornelia M. van Duijn,John Q. Trojanowski,Leslie M. Shaw,Michael W. Weiner,P. Murali Doraiswamy,Andrew J. Saykin,Rima Kaddurah-Daouk +20 more
TL;DR: In this paper, the association of BAs with the "A/T/N" (amyloid, tau, and neurodegeneration) biomarkers for AD: cerebrospinal fluid (CSF), atrophy (magnetic resonance imaging), and brain glucose metabolism (FDG PET).
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Early increase of CSF sTREM2 in Alzheimer’s disease is associated with tau related-neurodegeneration but not with amyloid-β pathology
Marc Suárez-Calvet,Marc Suárez-Calvet,Estrella Morenas-Rodríguez,Estrella Morenas-Rodríguez,Gernot Kleinberger,Kai Schlepckow,Miguel Ángel Araque Caballero,Nicolai Franzmeier,Anja Capell,Katrin Fellerer,Brigitte Nuscher,Erden Eren,Erden Eren,Johannes Levin,Johannes Levin,Yuetiva Deming,Laura Piccio,Celeste M. Karch,Carlos Cruchaga,Leslie M. Shaw,John Q. Trojanowski,Michael W. Weiner,Michael Ewers,Christian Haass,Christian Haass +24 more
TL;DR: CSF sTREM2 increased in early symptomatic stages of late-onset AD but, unexpectedly, was observed decreased at the earliest asymptomatic phase when only abnormal Aβ pathology but no tau pathology or neurodegeneration, is present.
References
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Genetic Analysis of Quantitative Phenotypes in AD and MCI: Imaging, Cognition and Biomarkers
Li Shen,Paul M. Thompson,Steven G. Potkin,Lars Bertram,Lindsay A. Farrer,Tatiana Foroud,Robert C. Green,Xiaolan Hu,Matthew J. Huentelman,Sungeun Kim,John S. K. Kauwe,Qingqin Li,Enchi Liu,Fabio Macciardi,Fabio Macciardi,Jason H. Moore,Leanne Munsie,Kwangsik Nho,Vijay K. Ramanan,Shannon L. Risacher,David J. Stone,Shanker Swaminathan,Arthur W. Toga,Michael W. Weiner,Andrew J. Saykin +24 more
TL;DR: A systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance.
Journal ArticleDOI
Longitudinal change in CSF Tau and Aβ biomarkers for up to 48 months in ADNI
TL;DR: The data demonstrate for the first time that there are distinct populations of ADNI subjects with abnormal longitudinal changes in CSF p-tau181 and Aβ1–42 levels, and the longitudinal results favor the hypothesis that Aβ 1–42 changes precede p- tau181 changes.
Journal ArticleDOI
Early detection of Alzheimer's disease using MRI hippocampal texture
Lauge Sørensen,Christian Igel,Naja Liv Hansen,Merete Osler,Martin Lauritzen,Egill Rostrup,Mads Nielsen +6 more
TL;DR: The presence of hippocampal texture abnormalities in MCI is highlighted, and the possibility that texture may serve as a prognostic neuroimaging biomarker of early cognitive impairment is highlighted.
Journal ArticleDOI
Association between anticholinergic medication use and cognition, brain metabolism, and brain atrophy in cognitively normal older adults
TL;DR: The use of anticholinergic medication was associated with increased brain atrophy and dysfunction and clinical decline, and use of AC medication among older adults should likely be discouraged if alternative therapies are available.
Journal ArticleDOI
Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study.
TL;DR: An ensemble system of feature selection and data sampling for the class imbalance problem and extensive experimental results show that a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach.
Related Papers (5)
2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception
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