Brain ERP components predict which individuals progress to Alzheimer's disease and which do not.
Predicting which individuals will progress to Alzheimer's disease (AD) is important in both clinical and research settings. We used brain Event-Related Potentials (ERPs) obtained in a perceptual/cognitive paradigm with various processing demands to predict which individual Mild Cognitive Impairment (MCI) subjects will develop AD versus which will not. ERP components, including P3, memory "storage" component, and other earlier and later components, were identified and measured by Principal Components Analysis. When measured for particular task conditions, a weighted set of eight ERP component_conditions performed well in discriminant analysis at predicting later AD progression with good accuracy, sensitivity, and specificity. The predictions for most individuals (79%) had high posterior probabilities and were accurate (88%). This method, supported by a cross-validation where the prediction accuracy was 70-78%, features the posterior probability for each individual as a method of determining the likelihood of progression to AD. Empirically obtained prediction accuracies rose to 94% when the computed posterior probabilities for individuals were 0.90 or higher (which was found for 40% of our MCI sample).
Chapman
Robert M
RM
Department of Brain and Cognitive Sciences and Center for Visual Science at the University of Rochester, Rochester, NY 14627, USA. rmc@cvs.rochester.edu
McCrary
John W
JW
Gardner
Margaret N
MN
Sandoval
Tiffany C
TC
Guillily
Maria D
MD
Reilly
Lindsey A
LA
DeGrush
Elizabeth
E
D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000473:pathology* / Q000503:physiopathology*; D001921:Brain / Q000473:pathology*; D003072:Cognition Disorders / Q000503:physiopathology; D016002:Discriminant Analysis; D018450:Disease Progression; D004569:Electroencephalography / Q000379:methods; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D015203:Reproducibility of Results