P3b amplitude as a signature of cognitive decline in the older population: An EEG study enhanced by Functional Source Separation

Abstract

With the greying population, it is increasingly necessary to establish robust and individualized markers of cognitive decline. This requires the combination of well-established neural mechanisms, and the development of increasingly sensitive methodologies. The P300 event-related potential (ERP) has been one of the most heavily investigated neural markers of attention and cognition, and studies have reliably shown that changes in the amplitude and latency of the P300 ERP index the process of aging. However, it is still not clear whether either the P3a or P3b sub-components additionally index levels of cognitive impairment. Here, we used a traditional visual three-stimulus oddball paradigm to investigate both the P3a and P3b ERP components in sixteen young and thirty-four healthy elderly individuals with varying degrees of cognitive ability. EEG data extraction was enhanced through the use of a novel signal processing method called Functional Source Separation (FSS) that increases signal-to-noise ratio by using a weighted sum of all electrodes rather than relying on a single, or a small sub-set, of EEG channels. Whilst clear differences in both the P3a and P3b ERPs were seen between young and elderly groups, only P3b amplitude differentiated older people with low memory performance relative to IQ from those with consistent memory and IQ. A machine learning analysis showed that P3b amplitude (derived from FSS analysis) could accurately categorise high and low performing elderly individuals (78% accuracy). A comparison of Bayes Factors found that differences in cognitive decline within the elderly group were 87 times more likely to be detected using FSS compared to the best performing single electrode (Cz). In conclusion, we propose that P3b amplitude could be a sensitive marker of early, age-independent, episodic memory dysfunction within a healthy older population. In addition, we advocate for the use of more advanced signal processing methods, such as FSS, for detecting subtle neural changes in clinical populations.