@article {5519, title = {EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer{\textquoteright}s Disease}, journal = {Journal of Alzheimer{\textquoteright}s Disease}, volume = {91}, year = {2023}, month = {02/2023}, pages = {1557 - 1572}, abstract = {

Background: Alzheimer{\textquoteright}s disease (AD) is associated with EEG changes across the sleep-wake cycle. As the brain is a non-linear system, non-linear EEG features across behavioral states may provide an informative physiologic biomarker of AD. Multiscale fluctuation dispersion entropy (MFDE) provides a sensitive non-linear measure of EEG information content across a range of biologically relevant time-scales.

Objective: To evaluate MFDE in awake and sleep EEGs as a potential biomarker for AD.

Methods: We analyzed overnight scalp EEGs from 35 cognitively normal healthy controls, 23 participants with mild cognitive impairment (MCI), and 19 participants with mild dementia due to AD. We examined measures of entropy in wake and sleep states, including a slow-to-fast-activity ratio of entropy (SFAR-entropy). We compared SFAR-entropy to linear EEG measures including a slow-to-fast-activity ratio of power spectral density (SFAR-PSD) and relative alpha power, as well as to cognitive function.

Results: SFAR-entropy differentiated dementia from MCI and controls. This effect was greatest in REM sleep, a state associated with high cholinergic activity. Differentiation was evident in the whole brain EEG and was most prominent in temporal and occipital regions. Five minutes of REM sleep was sufficient to distinguish dementia from MCI and controls. Higher SFAR-entropy during REM sleep was associated with worse performance on the Montreal Cognitive Assessment. Classifiers based on REM sleep SFAR-entropy distinguished dementia from MCI and controls with high accuracy, and outperformed classifiers based on SFAR-PSD and relative alpha power.

Conclusion: SFAR-entropy measured in REM sleep robustly discriminates dementia in AD from MCI and healthy controls.

}, issn = {13872877}, doi = {10.3233/JAD-221152}, url = {https://www.medra.org/servlet/aliasResolver?alias=iospress\&doi=10.3233/JAD-221152}, author = {Azami, Hamed and Moguilner, Sebastian and Penagos, Hector and Sarkis, Rani A. and Arnold, Steven E. and Gomperts, Stephen N. and Lam, Alice D.} } @article {5531, title = {Hippocampal memory reactivation during sleep is correlated with specific cortical states of the retrosplenial and prefrontal cortices}, journal = {Learning \& Memory}, volume = {30}, year = {2023}, month = {09/2023}, pages = {221 - 236}, abstract = {

Episodic memories are thought to be stabilized through the coordination of cortico{\textendash}hippocampal activity during sleep. However, the timing and mechanism of this coordination remain unknown. To investigate this, we studied the relationship between hippocampal reactivation and slow-wave sleep up and down states of the retrosplenial cortex (RTC) and prefrontal cortex (PFC). We found that hippocampal reactivations are strongly correlated with specific cortical states. Reactivation oc- curred during sustained cortical Up states or during the transition from up to down state. Interestingly, the most prevalent interaction with memory reactivation in the hippocampus occurred during sustained up states of the PFC and RTC, while hippocampal reactivation and cortical up-to-down state transition in the RTC showed the strongest coordination. Reactivation usually occurred within 150{\textendash}200 msec of a cortical Up state onset, indicating that a buildup of excitation during cortical Up state activity influences the probability of memory reactivation in CA1. Conversely, CA1 reactivation occurred 30{\textendash}50 msec before the onset of a cortical down state, suggesting that memory reactivation affects down state ini- tiation in the RTC and PFC, but the effect in the RTC was more robust. Our findings provide evidence that supports and highlights the complexity of bidirectional communication between cortical regions and the hippocampus during sleep.

}, doi = {10.1101/lm.053834.123}, url = {http://learnmem.cshlp.org/lookup/doi/10.1101/lm.053834.123}, author = {Feliciano-Ramos, Pedro A. and Galazo, Maria and Penagos, Hector and Wilson, Matthew} }