Search Results - for the Alzheimer’s Disease Neuroimaging Initiative

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  1. 1
    Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers.

    Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. by Daoqiang Zhang, Dinggang Shen, Alzheimer's Disease Neuroimaging Initiative

    Published 2012-01-01
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  2. 2
    How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database

    How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (... by Stavros I Dimitriadis, Dimitris Liparas, for the Alzheimer's Disease Neuroimaging Initiative

    Published 2018-01-01
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  3. 3
    Predicting time to dementia using a quantitative template of disease progression

    Predicting time to dementia using a quantitative template of disease progression by Murat Bilgel, Bruno M. Jedynak, Alzheimer's Disease Neuroimaging Initiative

    Published 2019-12-01
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  4. 4
    NeuropsychBrainAge: A biomarker for conversion from mild cognitive impairment to Alzheimer's disease

    NeuropsychBrainAge: A biomarker for conversion from mild cognitive impairment to Alzheimer's disease by Jorge Garcia Condado, Jesus M. Cortes, for the Alzheimer's Disease Neuroimaging Initiative

    Published 2023-10-01
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  5. 5
    Predicting long‐term clinical stability in amyloid‐positive subjects by FDG‐PET

    Predicting long‐term clinical stability in amyloid‐positive subjects by FDG‐PET by Leonardo Iaccarino, Arianna Sala, Daniela Perani, the Alzheimer's Disease Neuroimaging Initiative

    Published 2019-06-01
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  6. 6
    Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes.

    Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes. by Zhiyuan Xu, Xiaotong Shen, Wei Pan, Alzheimer's Disease Neuroimaging Initiative

    Published 2014-01-01
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  7. 7
    Multimodal neuroimaging study of cerebrovascular disease, amyloid deposition, and neurodegeneration in Alzheimer's disease progression

    Multimodal neuroimaging study of cerebrovascular disease, amyloid deposition, and neurodegeneration in Alzheimer's disease progression by Patrick J. Lao, Adam M. Brickman, Alzheimer's Disease Neuroimaging Initiative

    Published 2018-01-01
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  8. 8
    Basal forebrain degeneration precedes and predicts the cortical spread of Alzheimer’s pathology

    Basal forebrain degeneration precedes and predicts the cortical spread of Alzheimer’s pathology by Taylor W. Schmitz, R. Nathan Spreng, The Alzheimer's Disease Neuroimaging Initiative

    Published 2016-11-01
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  9. 9
    Quantification of cognitive impairment to characterize heterogeneity of patients at risk of developing Alzheimer's disease dementia

    Quantification of cognitive impairment to characterize heterogeneity of patients at risk of developing Alzheimer's disease dementia by Diana L. Giraldo, Jan Sijbers, Eduardo Romero, for the Alzheimer's Disease Neuroimaging Initiative

    Published 2021-01-01
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  10. 10
    Age correction in dementia--matching to a healthy brain.

    Age correction in dementia--matching to a healthy brain. by Juergen Dukart, Matthias L Schroeter, Karsten Mueller, Alzheimer's Disease Neuroimaging Initiative

    Published 2011-01-01
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  11. 11
    Evaluating the Alzheimer's disease data landscape

    Evaluating the Alzheimer's disease data landscape by Colin Birkenbihl, Yasamin Salimi, Daniel Domingo‐Fernándéz, Simon Lovestone, AddNeuroMed consortium, Holger Fröhlich, Martin Hofmann‐Apitius, the Japanese Alzheimer's Disease Neuroimaging Initiative, and the Alzheimer's Disease Neuroimaging Initiative

    Published 2020-01-01
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  12. 12
    Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) Found by Hierarchical Clustering

    Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) Found by Hierarchical Clustering by Mikko Kärkkäinen, Mithilesh Prakash, Marzieh Zare, Jussi Tohka, for the Alzheimer's Disease Neuroimaging Initiative

    Published 2020-01-01
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  13. 13
    Classification of structural MRI images in Alzheimer's disease from the perspective of ill-posed problems.

    Classification of structural MRI images in Alzheimer's disease from the perspective of ill-posed problems. by Ramon Casanova, Fang-Chi Hsu, Mark A Espeland, Alzheimer's Disease Neuroimaging Initiative

    Published 2012-01-01
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  14. 14
    ADCOMS sensitivity versus baseline diagnosis and progression phenotypes

    ADCOMS sensitivity versus baseline diagnosis and progression phenotypes by Dave Evenden, Angus Prosser, Sofia Michopoulou, Christopher Kipps, and for the Alzheimer's Disease Neuroimaging Initiative

    Published 2024-01-01
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  15. 15
    Choroid plexus volumes and auditory verbal learning scores are associated with conversion from mild cognitive impairment to Alzheimer's disease

    Choroid plexus volumes and auditory verbal learning scores are associated with conversion from mild cognitive impairment to Alzheimer's disease by Michael J. Pearson, Ruth Wagstaff, Rebecca J. Williams, for the Alzheimer's Disease Neuroimaging Initiative

    Published 2024-07-01
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  16. 16
    Effect of apolipoprotein E ε4 allele on the progression of cognitive decline in the early stage of Alzheimer's disease

    Effect of apolipoprotein E ε4 allele on the progression of cognitive decline in the early stage of Alzheimer's disease by Kazushi Suzuki, Akihiro Hirakawa, Ryoko Ihara, Atsushi Iwata, Kenji Ishii, Takeshi Ikeuchi, Chung‐Kai Sun, Michael Donohue, Takeshi Iwatsubo, Alzheimer's Disease Neuroimaging Initiative, Japanese Alzheimer's Disease Neuroimaging Initiative

    Published 2020-01-01
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  17. 17
    Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification.

    Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. by Igor O Korolev, Laura L Symonds, Andrea C Bozoki, Alzheimer's Disease Neuroimaging Initiative

    Published 2016-01-01
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  18. 18
    Bayesian Physics-Based Modeling of Tau Propagation in Alzheimer's Disease

    Bayesian Physics-Based Modeling of Tau Propagation in Alzheimer's Disease by Amelie Schäfer, Mathias Peirlinck, Kevin Linka, Ellen Kuhl, The Alzheimer's Disease Neuroimaging Initiative (ADNI)

    Published 2021-07-01
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  19. 19
    Exploratory correlation of the human structural connectome with non‐MRI variables in Alzheimer's disease

    Exploratory correlation of the human structural connectome with non‐MRI variables in Alzheimer's disease by Iman Aganj, Jocelyn Mora, Aina Frau‐Pascual, Bruce Fischl, for the Alzheimer's Disease Neuroimaging Initiative

    Published 2023-10-01
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  20. 20
    The temporal relationships between white matter hyperintensities, neurodegeneration, amyloid beta, and cognition

    The temporal relationships between white matter hyperintensities, neurodegeneration, amyloid beta, and cognition by Mahsa Dadar, Richard Camicioli, Simon Duchesne, D. Louis Collins, for the Alzheimer's Disease Neuroimaging Initiative

    Published 2020-01-01
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