Temporospatial components of brain ERPs as biomarkers for Alzheimer's disease. Issue 1 (29th August 2018)

• Record Type: Journal Article
• Authors: Chapman, Robert M.; Gardner, Margaret N.; Klorman, Rafael; Mapstone, Mark; Porsteinsson, Anton P.; Antonsdottir, Inga M.; Kamalyan, Lily
• Is Part Of: Alzheimer's & dementia. Volume 10:Issue 1(2018)
• Page Start: 604
• Page End: 614
• Publication Date: 2018-08-29
• Subjects: Discriminant analysis -- Brain event‐related potentials (ERP) -- Electrophysiology -- Alzheimer's disease (AD) -- Aging -- Principal components analysis (PCA) -- Receiver‐operating characteristic (ROC) -- Posterior probabilities -- Two‐step PCA -- Temporospatial ERPs -- Diagnosis -- Brain spatial information -- Temporal ERP components; Alzheimer's disease -- Periodicals; Alzheimer's disease -- Diagnosis -- Periodicals; Dementia -- Periodicals; Dementia -- Diagnosis -- Periodicals; 616.831
• DOI: 10.1016/j.dadm.2018.08.002
• Languages: English
• ISSNs: 2352-8729

Abstract

Abstract: Introduction: Developing biomarkers that distinguish individuals with Alzheimer's disease (AD) from those with normal cognition remains a crucial goal for improving the health of older adults. We investigated adding brain spatial information to temporal event‐related potentials (ERPs) to increase AD identification accuracy over temporal ERPs alone. Methods: With two‐step principal components analysis, we applied multivariate analyses that incorporated temporal and spatial ERP information from a cognitive task. Discriminant analysis used temporospatial ERP scores to classify participants as belonging to either the AD or healthy control group. Results: Temporospatial ERPs produced a cross‐validated area under the curve of 0.84. Adding spatial information through a formal procedure significantly improves classification accuracy. Discussion: A weighted combination of temporospatial ERP markers performs well in detecting AD. Because ERPs are noninvasive and inexpensive, they may be promising biomarkers for AD that can add functional information to other biomarker systems while providing the individual's probability of correct classification. Highlights: We investigated increasing Alzheimer's disease identification accuracy with brain temporospatial event‐related potentials (ERPs). Two‐step principal components analysis combined temporal and spatial ERP information from a cognitive task. Probabilities were included with each individual's classification as Alzheimer's disease or normal. Temporospatial ERPs produced a receiver‐operating characteristic area under the curve of 0.84 with good sensitivity and specificity. Our noninvasive ERP biomarkers offer high probability of correct classification.