Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer’s Disease
Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPA-GPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline.
KeywordsMachine learning Gaussian process classification Event-related potentials Alzheimer’s disease Single subject classification
The PRODEM study has been supported by the Austrian Research Promotion Agency FFG, project no. 827462, including financial contributions from Dr. Grossegger and Drbal GmbH, Vienna, Austria.
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