A supervised method to assist the diagnosis and classification of the status of Alzheimer's disease using data from an fMRI experiment.
The aim of this work is the development of a method to assist the diagnosis and classification of the status of Alzheimer's Disease (AD) using information that can be extracted from fMRI. The method consists of five stages: a) preprocessing of fMRI data to remove non-task related variability, b) modeling BOLD response depending on stimulus, c) feature extraction from fMRI data, d) feature selection and e) classification using the Random Forests (RF) 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, Greece.
Fotiadis
Dimitrios I
DI
Argyropoulou
Maria
M
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000145:classification* / Q000175:diagnosis*; D003704:Dementia / Q000145:classification / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted