Alpha Oscillations Prior to Encoding Preferentially Modulate Memory Consolidation during Wake Relative to Sleep

Abstract

Sleep promotes memory consolidation through unique neuromodulatory activity. However, little is known about the impact of attention during pre-sleep memory encoding on later memory performance. The current study aimed to address the question of whether attentional state prior to encoding, as indexed by alpha oscillatory activity, modulates the consolidation of images across periods of sleep and wake. 22 participants aged 18 -41 years (mean age = 27.3) viewed 120 emotionally valenced images (positive, negative, neutral) before a 2hr afternoon sleep opportunity and an equivalent period of wake. Following the sleep and wake conditions, participants were required to distinguish between 120 previously seen (target) images and 120 new (distractor) images. Relative alpha power -adjusted according to participants' individual alpha frequency -was computed to index attentional state prior to the learning phase. Generalised linear mixed-effects modelling revealed memory performance was modulated by attention, such that greater pre-encoding alpha power preferentially promoted memory consolidation during wake compared to sleep. There was no difference in memory performance between positive, negative and neutral stimuli. Modulations in alpha oscillatory activity may help to coordinate the flow of information between task-relevant cortical regions and a thalamo-cortical loop that preferentially subserves the formation of memory during times of wake relative to sleep.

A Pilot Study of an In-Home Multicomponent Exergame Training for Older Adults: Feasibility, Usability and Pre-Post Evaluation

“…Afterwards, two IAF measures were estimated: peak alpha frequency (PAF) and center of gravity (CoG), by means of the resting IAF v1.0 open source package available from https://github.com/corcorana/restingIAF. This allowed a fully automatic and reliable strategy to determine IAF estimates during resting state EEG recordings, of which a more detailed and extensive description can be found elsewhere (Corcoran et al, 2018; Cross et al, 2018). Briefly, one-sided channel-wise power spectral density (PSD) was first calculated in the 1–40 Hz frequency range by the Welch’s modified periodogram method, using a 2048 sample (∼4 s) Hamming window (50% overlap) across segments (frequency resolution = 0.244 Hz) and normalized by dividing each PSD channel estimate (within the passband) by the mean spectral power.…”

Oscillatory and Aperiodic Neural Activity Jointly Predict Language Learning

“…IAF estimates used to derive fc were obtained from a set of parieto-occipital electrodes (P3/P4/O1/O2/P7/P8/Pz/Iz) using the restingIAF package (v1.0.3; Corcoran et al, 2019; see also Cross et al 2018b). This method applies a Savitzky-Golay filter (frame width = 11 bins, polynomial order = 5) to smooth and differentiate the power spectrum prior to estimating a weighted average of the spectral peak frequencies identified across channels within a specified frequency range (here, 7-14 Hz).…”

A usability study of a multicomponent video game-based training for older adults

“…Afterwards, two IAF measures were estimated: peak alpha frequency (PAF) and center of gravity (CoG), by means of the restingIAF v1.0 open source package available from https://github.com/corcorana/restingIAF. This allowed a fully automatic and reliable strategy to determine IAF estimates during resting state EEG recordings, of which a more detailed and extensive description can be found elsewhere [84,85]. Briefly, one-sided channelwise power spectral density (PSD) was first calculated in the 1-40 Hz frequency range by the Welch's modified periodogram method, using a 2048 sample (~4 s) Hamming window (50% overlap) across segments (frequency resolution = 0.244 Hz) and normalized by dividing each PSD channel estimate (within the passband) by the mean spectral power.…”