Michael W Weiner1, Dallas P Veitch2, Paul S Aisen3, Laurel A Beckett4, Nigel J Cairns5, Robert C Green6, Danielle Harvey4, Clifford R Jack7, William Jagust8, John C Morris3, Ronald C Petersen9, Andrew J Saykin10, Leslie M Shaw11, Arthur W Toga12, John Q Trojanowski13. 1. Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA. Electronic address: michael.weiner@ucsf.edu. 2. Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA. 3. Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA. 4. Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA. 5. Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA. 6. Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. 7. Department of Radiology, Mayo Clinic, Rochester, MN, USA. 8. Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA. 9. Department of Neurology, Mayo Clinic, Rochester, MN, USA. 10. Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA. 11. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 12. Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA. 13. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Abstract
INTRODUCTION: 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. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
INTRODUCTION: 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. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
Authors: Gad A Marshall; Amy S Zoller; Kathleen E Kelly; Rebecca E Amariglio; Joseph J Locascio; Keith A Johnson; Reisa A Sperling; Dorene M Rentz Journal: Curr Alzheimer Res Date: 2014 Impact factor: 3.498
Authors: Michael W Weiner; Dallas P Veitch; Paul S Aisen; Laurel A Beckett; Nigel J Cairns; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; John C Morris; Ronald C Petersen; Jennifer Salazar; Andrew J Saykin; Leslie M Shaw; Arthur W Toga; John Q Trojanowski Journal: Alzheimers Dement Date: 2016-12-05 Impact factor: 21.566
Authors: Orla M Doyle; Eric Westman; Andre F Marquand; Patrizia Mecocci; Bruno Vellas; Magda Tsolaki; Iwona Kłoszewska; Hilkka Soininen; Simon Lovestone; Steve C R Williams; Andrew Simmons Journal: PLoS One Date: 2014-08-20 Impact factor: 3.240
Authors: Erika J Lampert; Kingshuk Roy Choudhury; Christopher A Hostage; Bharath Rathakrishnan; Michael Weiner; Jeffrey R Petrella; P Murali Doraiswamy Journal: Neuroimage Clin Date: 2014-09-04 Impact factor: 4.881
Authors: Jun-Young Lee; Soowon Park; Scott Mackin; Michael Ewers; Helena Chui; William Jagust; Philip S Insel; Michael W Weiner Journal: PLoS One Date: 2014-01-31 Impact factor: 3.240
Authors: Adam M Staffaroni; Duygu Tosun; Jue Lin; Fanny M Elahi; Kaitlin B Casaletto; Matthew J Wynn; Nihar Patel; John Neuhaus; Samantha M Walters; Elissa S Epel; Elizabeth H Blackburn; Joel H Kramer Journal: Neurobiol Aging Date: 2018-05-08 Impact factor: 4.673
Authors: Kevin Duff; Kayla R Suhrie; Bonnie C A Dalley; Jeffrey S Anderson; John M Hoffman Journal: Clin Neuropsychol Date: 2018-06-08 Impact factor: 3.535
Authors: Leslie M Shaw; Magdalena Korecka; Michal Figurski; Jon Toledo; David Irwin; Ju Hee Kang; John Q Trojanowski Journal: J Appl Lab Med Date: 2020-01-01