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Early prediction of Alzheimer's disease using null longitudinal model-based classifiers

Author
Gavidia, G.; Kanaan-Izquierdo, S.; Mataro-Serrat, M.; Perera, A.
Type of activity
Journal article
Journal
PloS one
Date of publication
2017-01-03
Volume
12
Number
1
DOI
https://doi.org/10.1371/journal.pone.0168011 Open in new window
Repository
http://hdl.handle.net/2117/102451 Open in new window
URL
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0168011 Open in new window
Abstract
Incipient Alzheimer’s Disease (AD) is characterized by a slow onset of clinical symptoms, with pathological brain changes starting several years earlier. Consequently, it is necessary to first understand and differentiate age-related changes in brain regions in the absence of disease, and then to support early and accurate AD diagnosis. However, there is poor understanding of the initial stage of AD; seemingly healthy elderly brains lose matter in regions related to AD, but similar changes can...
Citation
Gavidia, G., Kanaan, S., Mataro-Serrat, M., Perera, A. Early prediction of Alzheimer's disease using null longitudinal model-based classifiers. "PLoS one", 3 Gener 2017, vol. 12, núm. 1.
Keywords
Alzheimer disease, Atrophy, Biomarkers, Cerebrospinal fluid, Cognitive impairment, Linear Mixed Effect Modelling, Magnetic resonance imaging, Neuropsychological testing, Support vector machines
Group of research
B2SLab - Bioinformatics and Biomedical Signals Laboratory
CREB - Biomedical Engineering Research Centre
TALP - Centre for Language and Speech Technologies and Applications

Participants

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