Cognitive enhancement (CE) is the pursuit of enhancing and increasing the core mental capacity above the normal level. With the advancement of science and technology, many different approaches to carry out enhancement are available. The use of psychostimulants as the choice of cognitive enhancer is rapidly growing. Although anecdotal and subjective evidences claim that these drugs work however, empirical evidences from studies in healthy adults show inconclusive evidences. One reason could be that these studies did not consider sleep as an important factor mediating the effect of stimulants on brain activities. My study 1 investigates the role of sleep in stimulant mediated CE. Along with sleep, there are other factors which are important when investigating the stimulants’ effect of CE such as dosage, type of cognitive tasks, individual variability and bias of stimulant drugs toward certain cognitive domain. My study 2 investigates the evidences of bias by stimulants towards specific cognitive domain/s. Stimulants are addictive and comes with many side effects that may cause long term health issues. In my study 3, I investigated CE through targeted memory reactivation (TMR) which exploits the natural process of memory formation and strengthening during sleep with sensory stimulation to manipulate the memory strength. Specifically, in study 3 I developed a homebased- TMR protocol to selectively bias the weak and strong memories. This protocol was designed to carry out the study amidst the COVID pandemic lockdown. I developed a brand-new spatial memory cognitive task for remote online participation. The TMR intervention protocol is suitable for real world and naturalist setting without the participants having to come to the lab. This new homebased-TMR protocol shows some promising results. With future improvement and refinement, it could be turned into fully automated unsupervised TMR system.

Sleep is crucial for emotional and physiological health by allowing for restorative processes to occur. Disruption of sleep, such as that caused by stress, can result in adverse health outcomes. Given the importance of stress levels and sleep for health, disparities present are troubling and have not been the focus of studies until recently. In mainly middle-age and older adult samples, ethnic minorities and those with lower socioeconomic status experience poorer sleep and higher stress. However, fewer studies with young adult samples exist. One form of stress these groups report higher incidences of is that caused by perceived judgment. Given sleep’s benefit on other forms of stress, the stress stemming from the feeling of being judged by others may also be reduced. My first two studies explore possible factors influencing sleep and stress. First, I examine whether the impact of stress on sleep is influenced by factors such as Ethnicity. Next, I explore whether environment, in the form of feelings of neighborhood safety, influences sleep and stress levels. My final study now looks at the beneficial effects of sleep with the hypothesis that sleep, through emotional regulation, can buffer the negative effects of being judged by a peer in a lab-setting.

Sleep plays a critical role in the improvement of both episodic memory and working memory, with non-rapid eye movement neural oscillations, including slow oscillations (0.5-1Hz, SO) and sleep spindles (12-15Hz), causally implicated in these processes. However, studies elucidating the precise mechanisms underlying these processes are needed. Specifically, it remains unclear how neural oscillations during sleep coordinate with functional changes in the brain to facilitate the maintenance of information-specific episodic memories, or the increases in information-general working memory.The present dissertation aims to address these gaps in knowledge by investigating the neural mechanism underlying sleep-dependent episodic memory consolidation and working memory improvement. In study 1, I investigate how sleep protects episodic memory against waking interference. I ask whether sleep protects memory against future interference or if it rescues memory after interference has already occurred. In study 2, I probe the role of sleep spindles underlying sleep-dependent episodic memory consolidation. Specifically, I present a placebo-controlled, double-blind study that pharmacologically enhances sleep spindles to establish a causal role of sleep spindles and their temporal coupling with SOs in episodic memory consolidation. In study 3, I employ simultaneous EEG-fMRI to examine the neural activities associated with working memory as well as subsequent sleep, with a specific focus on SO as it’s a shared resource for both episodic and working memory. Together, these studies lead to a theoretical model of how the same sleep event may facilitate improvements across multiple cognitive domains. Specifically, SOs and it’s temporal coupling with sleep spindles coordinate with functional changes in specific memory networks to facilitate performance.

The last decade has seen significant progress in identifying sleep mechanisms that support cognition. Most of these studies focus on the link between electrophysiological events of the central nervous system during sleep and improvements in different cognitive domains, while the dynamic shifts of the autonomic nervous system across sleep have been largely overlooked. Recent studies, however, have identified significant contributions of autonomic inputs during sleep to cognition. Yet, there remain considerable gaps in understanding how central and autonomic systems work together during sleep to facilitate cognitive improvement. My dissertation work investigates the independent and interactive roles of central and autonomic activities during sleep and wake in cognitive processing. I specifically focus on the prefrontal-subcortical working memory (WM) processing and mechanisms underlying the formation of hippocampal-dependent episodic long-term memory (LTM). Here, I first present an introduction to heart-brain interaction and memory processing during sleep. Next, I show two experimental studies where I examine the role of autonomic activities and autonomic-central couplings during sleep on WM, which has reliably been shown to benefit from sleep. Lastly, I present a pharmacological within-subjects, double-blind, placebo-controlled study that identifies separate and competing underlying mechanisms between autonomic and central activities supporting WM and LTM. In light of these three studies’ novel contributions, I propose a theoretical model – the Sleep Oscillation Switch (SOS) Model that sleep is a competitive arena in which autonomic WM and LTM vie for limited resources.

How does the human brain adapt to changes in the environment and store information to form memories? Decades of research has explored how information input from the environment triggers plastic changes in the brain, leading to new memory traces that have the potential to become long-term memories. My thesis asks what the optimal brain states (i.e., level of engagement with the external environment and the internal neural dynamics) are for these memory consolidation processes to occur. Sleep promotes memory consolidation (Rasch & Born, 2013), with the majority of prior studies focusing on the role of non-rapid eye movement (NREM) sleep for reducing forgetting in explicit memory contexts. Less is known about the role of rapid eye movement (REM) sleep, however, studies have shown REM may be critical for implicit or procedural learning (Cai, Mednick, Harrison, Kanady, & Mednick, 2009; Mednick, Nakayama, & Stickgold, 2003; Plihal & Born, 1997). The current thesis examines the effect of different brain states on memory consolidation, with a specific focus on visual perceptual learning. In the first two studies, I manipulated levels of sensory input from the external environment by using different wake conditions (active and quiet wake) compared with sleep, and manipulated internal neural dynamics by using different sleep conditions (naps with NREM sleep alone or NREM plus REM sleep). I tested perceptual learning of both motion direction (Study 1) and texture (Study 2) discrimination. My results show that REM sleep promotes training-induced improvements in performance (i.e., plasticity) on visual skills tasks. I hypothesize that REM sleep is the optimal brain state for this consolidation due to its unique combination of low external input coupled with neural dynamics that promote plasticity. As a secondary aim of this thesis, I explored the utility of napping beyond its use as an experimental tool by examining individual differences in nap-dependent learning. In other words, should everyone nap to boost daytime performance? I found that learning profiles after a nap are different in men and women (Study 1), and that people who regularly nap show greater magnitude of nap-dependent learning compared to people who nap infrequently (Study 3). These findings should be taken into consideration when recommending napping in operational settings.

Age and sex are two of the three major risk factors for Alzheimer’s disease. Sex differences have been shown across a wide range of cognitive skills in young and older adults. In addition, the aging brain results in decreases in both Long-Term Memory (LTM) and Working Memory (WM). A growing body of evidence shows that sleep is critical for the formation of memories, with several electrophysiological events, including slow oscillations (SO 0.5-1Hz) and sleep spindles (12-15Hz), linked with memory consolidation. Sleep has been shown to be influenced by sex, sex hormones’ fluctuation and aging. Therefore, it is possible that sleeping brain processes memory differently among men and women and among older and younger adults. This dissertation investigates the role of both menstrual phase and aging on sleep related memory formation. In Chapter 1, I explore the role of sex differences and menstrual phase on sleep dependent LTM consolidation among younger adults. I find superior pre-nap memory performance on a verbal memory task in women compared with men. Among women, post-nap memory retention was modulated by menstrual phase, with women showing more forgetting following a nap in their perimenses phase (−5 days to +6 days from the start of menses) compared to their non-perimenses (outside of the perimenses phase). In addition, three profiles emerged between brain activity and performance. In men, performance was associated with the temporal coupling of spindles and slow oscillations (i.e., spindle/SO coincidence) as well as the number of spindles. During non-perimenses, women’s performance was related with SOs only, whereas performance during perimenses was associated with sleep spindles only. In Chapter 2, I explore the role of sleep, wake and quiet rest on WM training among older adults. I show that WM improved within a day of practice only after offline periods containing nap and quiet rest, but not wake, with similar sleep-dependent mechanisms as LTM consolidation (e.g., SOs and the coupling of sigma and SOs). Finally in Chapter 3, I investigate the role of WM on emotional LTM performance among younger and older adults. I find a superior memory performance for younger adults, a positivity memory bias among older adults, and a positive relation between WM and LTM only among younger adults. Together, the studies from this dissertation show evidence for a role of sex, sex hormones’ fluctuation, and aging, on sleep and memory. With the reported declines in sleep and memory emerging in older adulthood and menopausal women experiencing a sudden drop in sex hormones, this dissertation highlights the importance of possible interactions among biological factors, such as sex and aging, while studying sleep and memory with the goal of illuminating new paths towards the prevention of Alzheimer’s disease.

Sleep, a critical necessity for health, cognition, and well-being, consumes 25 years of the average human’s life. Alterations in sleep’s macro and microstate have been observed in multiple illnesses, including depression and Alzheimer’s disease, and sleep may represent a biomarker of these difficult to diagnose pathologies. Despite this, adequate models of the dynamics of sleep able to capture the fine-grained minutia of sleep’s many states and processes are lacking. The current work leverages over 1000 nights of healthy sleep polysomnography recordings from multiple open-source datasets. Data were automatically analyzed through a machine learning and data engineering pipeline to extract important features of the electroencephalogram, such as sleep spindles, slow oscillations (SO), and Rapid Eye Movement (REM) events. The counts of these sleep features and transition probabilities of sleep stages were modeled across the night in 30-second epoch intervals, using a flexible yet principled Bayesian cognitive modeling approach. The model, christened P(Sleep) 2.0, defines “typical” patterns of healthy sleep, and how they are affected by time, circadian processes and individual differences (e.g., age, sex). Transition probability dynamics were found to best be predicted by the previous 2 stages, time spent in stage, time across the night, age and sex, but not an age*sex interaction. Spindles, SO and REM events were also affected by the same features, but an age*sex interaction was additionally present. N2 spindles and both N2 and N3 slow oscillations were found to increase with time spent in a stage, whereas N3 spindles and REM decreased. Spindles and SO were more present in the evening compared to the morning; in contrast, REM increased over the night. REM counts and N3 spindles increased with age, while N2 spindles and SO reduced. Females had more N2 spindles, and SO in both N2 and N3 than men. The current work replicates a body of previous literature based on small sample sizes, and the increased power in this study provides firm conclusions to arguments on the patterns of healthy sleep across the night, both at the macroscale (stages) and microscale (feature events). The proposed work has broader impacts on the detection of abnormal (and potentially pathologic) sleep patterns.