AI Article Synopsis
- Researchers are focusing on creating biomarkers to differentiate Alzheimer's disease (AD) patients from those with normal cognition, which is essential for enhancing elderly health.
- They utilized a methodology that combines brain spatial and temporal event-related potentials (ERPs) in order to improve identification accuracy for AD.
- Findings show that integrating spatial data with temporospatial ERPs boosts classification accuracy, making ERPs a potential cost-effective, noninvasive tool for detecting AD and enhancing existing biomarker systems.
Article 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215980 | PMC |
| http://dx.doi.org/10.1016/j.dadm.2018.08.002 | DOI Listing |
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