Detection of Alzheimer’s disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis.
Hum. Brain Mapp.. 2015-10-10; 36(12): 4758-4770
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1. Hum Brain Mapp. 2015 Dec;36(12):4758-70. doi: 10.1002/hbm.22926. Epub 2015 Oct
Detection of Alzheimer’s disease signature in MR images seven years before
conversion to dementia: Toward an early individual prognosis.
Coupé P(1), Fonov VS(2), Bernard C(3)(4)(5), Zandifar A(2), Eskildsen SF(6),
Helmer C(7)(8)(9), Manjón JV(10), Amieva H(7)(8)(9), Dartigues JF(7)(8)(11),
Allard M(3)(4)(5), Catheline G(3)(4)(5), Collins DL(2); Alzheimer’s Disease
(1)Laboratoire Bordelais De Recherche En Informatique, Unité Mixte De Recherche
CNRS (UMR 5800), PICTURA Research Group, Bordeaux, France.
(2)McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill
University, Montreal, Canada.
(3)University of Bordeaux, INCIA, UMR 5287, Talence, France.
(4)CNRS, INCIA, UMR 5287, Talence, France.
(5)École Pratique des Hautes Études, Bordeaux, France.
(6)Center of Functionally Integrative Neuroscience and MINDLab, Aarhus
University, Aarhus, Denmark.
(7)INSERM, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, Bordeaux,
(8)Département de Pharmacologie CHU de Bordeaux, University of Bordeaux,
(9)INSERM, CIC 14.01, Module EC, Bordeaux, France.
(10)Instituto De Aplicaciones De Las Tecnologías De La Información Y De Las
Comunicaciones Avanzadas (ITACA), Universitat Politècnica De València, Camino De
Vera S/N, Valencia, 46022, Spain.
(11)University Hospital, Memory Consultation, CMRR, Bordeaux, France.
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.
© 2015 Wiley Periodicals, Inc.
PMID: 26454259 [Indexed for MEDLINE]