Brain ERP components predict which individuals progress to Alzheimer's disease and which do not

• Published in: Neurobiology of aging Vol. 32; no. 10; pp. 1742 - 1755
• Main Authors: Chapman, Robert M, McCrary, John W, Gardner, Margaret N, Sandoval, Tiffany C, Guillily, Maria D, Reilly, Lindsey A, DeGrush, Elizabeth
• Format: Journal Article
• Language: English
• Published: London Elsevier Inc 01.10.2011; Elsevier
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - pathology; Alzheimer Disease - physiopathology; Alzheimer's disease (AD); Biological and medical sciences; Biomarker; Brain - pathology; Cognition Disorders - physiopathology; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Development. Senescence. Regeneration. Transplantation; Diagnosis; Discriminant Analysis; Disease Progression; Early detection; EEG; Electroencephalography - methods; Event-Related Potentials (ERP); Evoked Potentials - physiology; Female; Fundamental and applied biological sciences. Psychology; Humans; Internal Medicine; Male; Medical sciences; Mild Cognitive Impairment (MCI); Neurology; Neurophysiology; Neuropsychological Tests; Posterior probability; Prediction; Predictive Value of Tests; Principal Component Analysis; Principal Components Analysis (PCA); Reproducibility of Results; Vertebrates: nervous system and sense organs; Biomarker; Discriminant analysis; Posterior probability; EEG; Prediction; Principal Components Analysis (PCA); Mild Cognitive Impairment (MCI); Alzheimer's disease (AD); Event-Related Potentials (ERP); Diagnosis; Early detection; Neurophysiology; Senescence; Alzheimer disease; Central nervous system; Electrophysiology; Biological marker; Electroencephalography; Encephalon; Degenerative disease; Nervous system diseases; Cerebral disorder; Central nervous system disease; Early; mild cognitive impairment; Event evoked potential
• ISSN: 0197-4580; 1558-1497
• DOI: 10.1016/j.neurobiolaging.2009.11.010

Abstract 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).