Journal
ALZHEIMERS & DEMENTIA
Volume 13, Issue 4, Pages E1-E85Publisher
WILEY
DOI: 10.1016/j.jalz.2016.11.007
Keywords
Alzheimer's disease; Mild cognitive impairment; Amyloid; Tau; Biomarker; Disease progression
Categories
Funding
- NIH - National Institute on Aging [5U01AG024904-10]
- NIH/NCI [P30CA093373, RO1CA159447, R01CA115483, R01CA199668, R01CA199725, R01CA129769]
- NIH/NIA [P30AG010129, P30AG043097, U01AG024904, R01AG047827, P50 AG005681, P01 AG003991, R01AG048252, R01AG19771, P30AG10133, R44AG049540]
- California Department of Justice [14-6100]
- National Institute of Justice [2014-R2-CX-0012]
- NIH [R01-AG011378, RO1-AG041851, U01-AG06786, U01-AG024904, R01-AG37551, R01-AG043392, R01-NS092625, U01-HG006500, U19-HD077671, R01-HG005092, R01-AG047866, U01-HG008685, U41-HG006834, RO1-AG04 1851]
- Alexander Family Alzheimer's Disease Research Professorship of the Mayo Foundation
- NIH/NICHD [U54HD079125]
- DOD [W81XWH-12-2-0012, W81XWH-13-1-0259, W81XWH-14-1-0462]
- Broad Institute
- Department of Defense
- NIH/LM [R01LM011360]
- MJFox Foundation for PD Research (BioFIND)
- NIH
- GSK
- Janssen
- Biogen
- NeuroVigil, Inc.
- CHRU-Hopital Roger Salengro
- Siemens
- AstraZeneca
- Geneva University Hospitals
- Lilly
- University of California
- San Diego-ADNI
- Paris University
- Institut Catala de Neurociencies Aplicades
- University of New Mexico School of Medicine
- Ipsen
- CTAD (Clinical Trials on Alzheimer's Disease)
- Pfizer
- AD PD meeting
- Paul Sabatier University
- Novartis
- Tohoku University
- Merck
- Avid
- DOD
- VA
- NIA
- The NIH [U19-AG010483, UF1-AG032438, U01-AG042791, R01-NS089757, R01-AG049704, R01 MH 098260, R01 AG 046171, 1RFAG051550, P50 AG016574, U01 AG006786, R01 AG011378, R01 AG041581, U01 AG024904, 5U01AG024904, P41EB015922, 1U54EB020406]
- [AG-010124]
- [NS-053488]
Ask authors/readers for more resources
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 beta-amyloid deposition (A beta+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than A beta deposition; (4) Cerebrovascular risk factors may interact with A beta 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 A beta pathology along WM tracts predict known patterns of cortical A beta 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. (C) 2017 Published by Elsevier Inc. on behalf of the Alzheimer's Association.
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