Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis

Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effect...

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Vydané v:Human Brain Mapping Ročník 36; číslo 12; s. 4758 - 4770
Hlavní autori: Coupé, Pierrick, Fonov, Vladimir S., Bernard, Charlotte, Zandifar, Azar, Eskildsen, Simon F., Helmer, Catherine, Manjón, José V., Amieva, Hélène, Dartigues, Jean-François, Allard, Michèle, Catheline, Gwenaelle, Collins, D. Louis
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Blackwell Publishing Ltd 01.12.2015
Wiley
John Wiley & Sons, Inc
John Wiley and Sons Inc
Predmet:
[INFO.INFO-IM] Computer Science [cs]/Medical Imaging
Aged
Aged, 80 and over
Alzheimer
Alzheimer Disease
Alzheimer Disease - pathology
Area Under Curve
Cohort Studies
Computer Science
Databases, Factual
Databases, Factual - statistics & numerical data
Dementia
Dementia - pathology
Disease Progression
Electronic Data Processing
Female
Hippocampus
Hippocampus - pathology
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Magnetic Resonance Imaging - methods
Male
Medical Imaging
MRI biomarker
Psychiatric Status Rating Scales
Reproducibility of Results
ROC Curve
hippocampus
MRI biomarker
Alzheimer
ISSN:1065-9471, 1097-0193, 1097-0193
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Shrnutí:Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR‐based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three‐City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P < 0.0001) 7 years before conversion to AD, better than both hippocampal volume (58.1%, P = 0.04) and MMSE score (56.9%, P = 0.08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.4 percentage points for hippocampal grade (73.0%) over hippocampal volume (64.6%). Adaptation of the proposed framework to clinical score estimation is also presented. Compared with previous studies investigating new biomarkers for AD prediction over much shorter periods, the very long followup of the Three‐City cohort demonstrates the important clinical potential of the proposed imaging biomarker. The high accuracy obtained with this new imaging biomarker paves the way for computer‐based prognostic aides to help the clinician identify cognitively intact subjects that are at high risk to develop AD. Hum Brain Mapp 36:4758–4770, 2015. © 2015 Wiley Periodicals, Inc.
Bibliografia:"Fondation Plan Alzheimer", The Fondation pour la Recherche Médicale, Dana Foundation
NIH - No. P30AG010129; No. K01 AG030514
ANR - No. 2007LVIE 003
National Institutes of Health [Alzheimer's Disease Neuroimaging Initiative (ADNI)] - No. U01 AG024904
Northern California Institute for Research and Education
istex:64062160213D2A5238072BCC7E975E80A59F495E
ArticleID:HBM22926
ark:/67375/WNG-P9J0HK2C-0
Hence, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data, but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at
Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database
http://www.loni.usc.edu/ADNI/Data/ADNI_Authorship_List.pdf
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www.loni.ucla.edu/ADNI
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Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu/ADNI). Hence, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data, but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at http://www.loni.usc.edu/ADNI/Data/ADNI_Authorship_List.pdf).
ISSN:1065-9471
1097-0193
1097-0193
DOI:10.1002/hbm.22926