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Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.
Alzheimers Dement. 2017 Apr; 13(4):e1-e85.AD

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.

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Authors+Show Affiliations

Weiner MW
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.
Veitch DP
Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA.
Aisen PS
Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA.
Beckett LA
Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA.
Cairns NJ
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.
Green RC
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Harvey D
Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA.
Jack CR
Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Jagust W
Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA.
Morris JC
Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA.
Petersen RC
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Saykin AJ
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.
Shaw LM
Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Toga AW
Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA.
Trojanowski JQ
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.
Alzheimer's Disease Neuroimaging Initiative
No affiliation info available

MeSH

Alzheimer DiseaseBrainClinical Trials as TopicDisease ProgressionHumansNeuroimaging

Pub Type(s)

Journal Article
Review

Language

eng

PubMed ID

28342697

Clinical Trial Links

A Biomarker Database to Investigate Blood-Based and Digital Biomarkers in Participants Screened for Alzheimer's Disease (Bio-Hermes)

Citation

Weiner, Michael W., et al. "Recent Publications From the Alzheimer's Disease Neuroimaging Initiative: Reviewing Progress Toward Improved AD Clinical Trials." Alzheimer's & Dementia : the Journal of the Alzheimer's Association, vol. 13, no. 4, 2017, pp. e1-e85.
Weiner MW, Veitch DP, Aisen PS, et al. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement. 2017;13(4):e1-e85.
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., Morris, J. C., Petersen, R. C., Saykin, A. J., Shaw, L. M., Toga, A. W., & Trojanowski, J. Q. (2017). Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimer's & Dementia : the Journal of the Alzheimer's Association, 13(4), e1-e85. https://doi.org/10.1016/j.jalz.2016.11.007
Weiner MW, et al. Recent Publications From the Alzheimer's Disease Neuroimaging Initiative: Reviewing Progress Toward Improved AD Clinical Trials. Alzheimers Dement. 2017;13(4):e1-e85. PubMed PMID: 28342697.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. AU - Weiner,Michael W, AU - Veitch,Dallas P, AU - Aisen,Paul S, AU - Beckett,Laurel A, AU - Cairns,Nigel J, AU - Green,Robert C, AU - Harvey,Danielle, AU - Jack,Clifford R,Jr AU - Jagust,William, AU - Morris,John C, AU - Petersen,Ronald C, AU - Saykin,Andrew J, AU - Shaw,Leslie M, AU - Toga,Arthur W, AU - Trojanowski,John Q, AU - ,, Y1 - 2017/03/22/ PY - 2016/09/07/received PY - 2016/11/21/revised PY - 2016/11/28/accepted PY - 2017/3/28/pubmed PY - 2017/10/7/medline PY - 2017/3/27/entrez KW - Alzheimer's disease KW - Amyloid KW - Biomarker KW - Disease progression KW - Mild cognitive impairment KW - Tau SP - e1 EP - e85 JF - Alzheimer's & dementia : the journal of the Alzheimer's Association JO - Alzheimers Dement VL - 13 IS - 4 N2 - 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. SN - 1552-5279 UR - https://www.unboundmedicine.com/medline/citation/28342697/Recent_publications_from_the_Alzheimer's_Disease_Neuroimaging_Initiative:_Reviewing_progress_toward_improved_AD_clinical_trials_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1552-5260(16)33124-7 DB - PRIME DP - Unbound Medicine ER -