Resting state brain signal complexity of young healthy adults reflects genetic risk for developing Alzheimer’s Disease

Abstract

The e4 allele of the APOE gene is strongly associated with impaired brain functionality and cognitive decline in humans at older age. It is controversial whether and how the APOE e4 allele is affecting brain activity among young healthy individuals and how such effects may contribute to individual differences in cognitive performance. Signal complexity is a critical aspect of brain activity that has been shown to be associated with brain function. In this study, we analyzed multiscale entropy (MSE) of EEG signals among young healthy adults as an indicator of brain signal complexity and investigated how MSE is predicted by APOE genotype groups. Furthermore, by means of structural equation modeling, we investigated whether MSE predicts fluid intelligence. Results indicate larger MSE in young healthy e4 carriers across all time scales. Moreover, better fluid intelligence (gf) is associated with smaller MSE at low time scales and larger MSE at higher scales. However, MSE does not account for better cognitive performance among APOE e4 carriers by mediating the APOE genotype effect on fluid intelligence. The present results shed further light on the neural mechanisms underlying gene-behavior association relevant for Alzheimer’s Disease risk.

Exploring Neural Signal Complexity as a Potential Link between Creative Thinking, Intelligence, and Cognitive Control

“…Other studies on brain signal entropy in resting-state fMRI also showed substantial associations with gc (Saxe et al 2018). Moreover, information from cortical entropy profiles effectively predicted several cognitive abilities in a recent study by Li et al (2020).…”

“…This is the reason for only calculating MSE in this task up to time-scale 10. As systematically illustrated by Li et al (2020), the reliability of MSE decreases with the decreasing length of the time series. Thus, for these rather short time series, a higher-scale MSE would not satisfy these reliability expectations.…”

“…Hence, we integrated MSE values across several scales into a single score by using the area under the curve (AUC). This integration procedure has been previously proposed in the literature because low and high scales are interpreted differently but interpretations do not differ for neighboring scales (see Kaur et al 2019;Li et al 2020;Takahashi et al 2009). For a data-driven division of time scales in high and low results in the case of the verbal DT task, we visually inspected the line-plots of grand-mean MSE during the production of typical vs. original associations (see also Kaur et al 2020).…”