Memory consolidation during sleep relies on the precisely timed interaction of rhythmic neural events. Here, we investigate differences in slow oscillations (SO; 0.5–1 Hz), sleep spindles (SP), and their coupling across the adult human lifespan and ask whether observed alterations relate to the ability to retain associative memories across sleep. We demonstrate that older adults do not show the fine-tuned coupling of fast SPs (12.5–16 Hz) to the SO peak present in younger adults but, instead, are characterized most by a slow SP power increase (9–12.5 Hz) at the end of the SO up-state. This slow SP power increase, typical for older adults, coincides with worse memory consolidation in young age already, whereas the tight precision of SO–fast SP coupling promotes memory consolidation across younger and older adults. Crucially, brain integrity in source regions of SO and SP generation, including the medial prefrontal cortex, thalamus, hippocampus and entorhinal cortex, reinforces this beneficial SO–SP coupling in old age. Our results reveal that cognitive functioning is not only determined by maintaining structural brain integrity across the adult lifespan, but also by the preservation of precisely timed neural interactions during sleep that enable the consolidation of declarative memories.

The individual alpha frequency (IAF) of the human EEG reflects systemic properties of the brain, is highly heritable, and relates to cognitive functioning. Not much is known about the modifiability of IAF by cognitive interventions. We report analyses of resting EEG from a large-scale training study in which healthy younger (20-31 years, N = 30) and older (65-80 years, N = 28) adults practiced 12 cognitive tasks for~100 1-h sessions. EEG was recorded before and after the cognitive training intervention. In both age groups, IAF (and, in a control analysis, alpha amplitude) did not change, despite large gains in cognitive performance. As within-session reliability and test-retest stability were high for both age groups, imprecise measurements cannot account for the findings. In sum, IAF is highly stable in healthy adults up to 80 years, not easily modifiable by cognitive interventions alone, and thus qualifies as a stable neurophysiological trait marker. Descriptors: EEG, Alpha frequency, Reliability, Stability, TraitThe alpha frequency (AF) is the dominant frequency of the human electroencephalogram (EEG) during relaxed wakefulness and may tap into general central nervous system (CNS) functioning, as well as the status of mental health and cognitive functioning. Already Berger (1929Berger ( , 1933 took up a two-fold perspective on AF. He assessed AF in order to monitor changes within subjects (intraindividual change), such as the deceleration of AF caused by intoxication and its acceleration paralleling recovery from intoxication. He also measured the AF for individual patients and healthy individuals to delineate differences between persons (interindividual differences), for example, in relation to cognitive ability.Today, a vast amount of evidence supports and extends Berger's initial observations. Significant correlations between interindividual differences in AF and a large variety of cognitive measures have been observed

Some eighty years after the discovery of the human electroencephalogram (EEG) and its dominant rhythm, alpha (~10 Hz), the neurophysiological functions and behavioral correlates of alpha oscillations are still under debate. Similarly, the biological mechanisms contributing to the general factor of intelligence, or g, have been under scrutiny for decades. Individual alpha frequency (IAF), a trait-like parameter of the EEG, has been found to correlate with individual differences in cognitive performance and cognitive abilities. Informed by large-scale theories of neural organization emphasizing the general functional significance of oscillatory activity, the present study replicates and extends these findings by testing the hypothesis that IAF is related to intelligence at the level of g, rather than at the level of specific cognitive abilities. Structural equation modeling allowed us to statistically control for measurement error when estimating the association between IAF and intellectual functioning. In line with our hypothesis, we found a statistically reliable and substantial correlation between IAF and g (r = .40). The magnitude of this correlation did not differ significantly between younger and older adults, and captured all of the covariation between IAF and the cognitive abilities of reasoning, memory, and perceptual speed. The observed association between IAF and g provides a parsimonious explanation for the commonly observed diffuse pattern of correlations between IAF and cognitive performance. We conclude that IAF is a marker of global architectural and functional properties of the human brain.

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10 The average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a 11 prevalent index of human brain function. Increasing evidence questions the utility of trial-/group 12 averaged power estimates however, as seemingly sustained activity patterns may be brought about 13 by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe the 14 duration and power of rhythmic and arrhythmic neural responses on the single trial-level. However, 15 it is less clear how well this can be achieved in empirical MEG/EEG/LFP recordings. Here, we 16 extend an existing rhythm detection algorithm (extended Better OSCillation detection: "eBOSC"; 17 cf. Whitten et al., 2011) to systematically investigate boundary conditions for estimating neural 18rhythms at the single-trial level. Using simulations as well as resting and task-based EEG recordings 19 from a micro-longitudinal assessment, we show that alpha rhythms can be successfully captured in 20 single trials with high specificity, but that the quality of single-trial estimates varies greatly between 21 subjects. Despite those signal-to-noise-based limitations, we highlight the utility and potential of 22 rhythm detection with multiple proof-of-concept examples, and discuss implications for single-trial 23 analyses of neural rhythms in electrophysiological recordings. Using an applied example of 24 working memory retention, rhythm detection indicated load-related increases in the duration of 25 frontal theta and posterior alpha rhythms, in addition to a frequency decrease of frontal theta 26 rhythms that was observed exclusively through amplification of rhythmic amplitudes. 27 28Highlights: 29• Traditional narrow-band rhythm metrics conflate the power and duration of rhythmic and arrhythmic 30 periods. We extend a state-of-the-art rhythm detection method (eBOSC) to derive rhythmic episodes in 31 single trials that can disambiguate rhythmic and arrhythmic periods. 32• Simulations indicate that this can be done with high specificity given sufficient rhythmic power, but with 33 strongly impaired sensitivity when rhythmic SNR is low. Empirically, surface EEG recordings exhibit 34 stable inter-individual differences in α-rhythmicity in ranges where simulations suggest a gradual bias, 35 leading to high collinearity between narrow-band and rhythm-specific estimates. 36• Beyond these limitations, we highlight multiple empirical benefits of characterizing rhythmic episodes 37 in single trials, such as (a) a principled separation of rhythmic and arrhythmic content, (b) an 38 amplification of rhythmic amplitudes, and (c) a specific characterization of sustained and transient 39 events. 40• In an exemplary application, rhythm-specific estimates increase sensitivity to working memory load 41 effects, in addition to indicating a frequency modulation of frontal theta rhythms through the 42 amplification of rhythmic power. 43 44