title: Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment.
abstract: Brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone.
authors.affiliation: Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Goryawala
authors.affiliation: Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhou
authors.affiliation: Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA.
authors.forename: Warren
authors.initials: W
authors.lastname: Barker
authors.affiliation: Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA ; Department of Psychiatry, Miller School of Medicine, University of Miami, Miami, FL, USA.
authors.forename: David A
authors.initials: DA
authors.lastname: Loewenstein
authors.affiliation: Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, USA ; Department of Psychiatry, Miller School of Medicine, University of Miami, Miami, FL, USA ; Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA ; Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA.
authors.forename: Ranjan
authors.initials: R
authors.lastname: Duara
authors.affiliation: Center for Advanced Technology and Education, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
authors.forename: Malek
authors.initials: M
authors.lastname: Adjouadi
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000145:classification* / Q000175:diagnosis*; D001284:Atrophy / Q000473:pathology; D001921:Brain / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000145:classification* / Q000175:diagnosis*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D009483:Neuropsychological Tests