A supervised method to assist the diagnosis of Alzheimer's disease based on functional magnetic resonance imaging.
In this work we present a supervised method to assist the diagnosis of Alzheimer's disease (AD) based on functional magnetic resonance images (fMRI). The method consists of five stages: a) preprocessing of fMRI data to remove non-task related variability, b) modeling the way in which the BOLD response depends on stimulus, c) feature extraction from fMRI data, d) feature selection and e) classification using the Random Forests algorithm. The proposed method is evaluated using data from 41 subjects (14 young adults, 14 non demented older adults and 13 demented older adults.
Tripoliti
Evanthia E
EE
Unit of Medical Technology and Intelligent Information Systems, Dept. of Computer Science, University of Ioannina and Biomedical Research Institute - FORTH, GR 451 10, Ioannina, Greece.
Fotiadis
Dimitrios I
DI
Argyropoulou
Maria
M
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis*; D001185:Artificial Intelligence; D001931:Brain Mapping; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity