Slow wave–spindle coupling during deep sleep is selectively linked to Plasma Amyloid-β levels in Older Adults

Slow wave–spindle coupling during deep sleep is selectively linked to Plasma Amyloid-β levels in Older Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Slow wave–spindle coupling during deep sleep is selectively linked to Plasma Amyloid-β levels in Older Adults Marina Wunderlin, Korian Wicki, Charlotte Elisabeth Teunissen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7085440/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Slow wave activity, the signature of deep/slow wave sleep, has consistently been linked to amyloid-beta (Aβ), a biomarker of Alzheimer’s disease. Less is known about how Aβ relates to specific microstructural processes within slow wave sleep, such as the coupling of slow waves (SW) and sleep spindles. Although better SW–spindle coupling has been associated with younger age, increased memory performance, and less brain atrophy, its relationship with Aβ remains poorly understood, particularly due to a lack of research in cognitively impaired older adults. Here, we investigate the association between SW–spindle coupling and Aβ in both cognitively normal and cognitively impaired older individuals. Additionally, we examine how an acoustic stimulation intervention known to boost slow wave sleep affects the link between SW–spindle coupling and Aβ. Methods Forty-seven older adults (age mean = 70.5 (0.68)), ranging from cognitively impaired to cognitively healthy, completed one adaptation and one baseline night. A subsample (n = 39, age mean = 70.5 (0.74)) additionally underwent a three-night acoustic stimulation intervention designed to boost slow wave activity. Blood samples post-baseline and post-intervention were analyzed for Aβ 1–42/1-40-ratio. Results Regardless of cognitive functioning, SW–spindle coupling was the best predictor for baseline Aβ, better than slow wave activity, age or cognitive functioning. Specifically, more favorable Aβ-levels were linked to a SW–spindle coupling physiology resembling a younger brain. While intervention-induced increases in slow wave activity were linked to a beneficial Aβ-response across all cognitive levels, intervention-induced increases in SW–spindle coupling benefited Aβ-response exclusively in cognitively impaired individuals. Conclusions Our results suggest a link between SW–spindle coupling and Aβ going beyond slow wave activity. This hints towards a potential specific function of SW–spindle coupling related to the early pathophysiology of Alzheimer’s disease. amyloid-beta plasma marker slow wave sleep slow wave–spindle coupling Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cognitive decline and dementia are becoming increasingly relevant issues due to our aging population. Amyloid-beta (Aβ)—a prominent pathophysiological marker of Alzheimer's disease (Hardy and Selkoe, 2002 )—starts accumulating up to 15 years before symptoms appear, with corresponding alterations in brain function becoming evident as early as 10 years prior to symptom onset (Beason-Held et al., 2013 ; Palmqvist et al., 2017 ). Hence, understanding the underlying trajectories and influences that contribute to the accumulation of Aβ is vital for targeted interventions and dementia prevention. Additionally, investigating whether such targeted interventions can yield favorable outcomes for Aβ is essential. A growing body of research has linked disturbed sleep to Aβ. For instance, in healthy older adults, shorter self-reported sleep duration and poorer sleep quality were associated with greater cortical Aβ-burden (Spira et al., 2013 ). Furthermore, total sleep deprivation studies have shown to negatively affect Aβ-levels in both CSF (Ooms et al., 2014 ) and the brain (Shokri-Kojori et al., 2018 ). Specifically, disturbed slow wave sleep (SWS), the deepest sleep stage, has been shown to interfere with Aβ-levels (Ju et al., 2017 ). Impairments in slow wave activity (SWA)—the underlying frequency during SWS—have been associated with adverse implications for cortical Aβ-burden (Mander et al., 2015 ) and CSF Aβ-levels (Varga et al., 2016 ). One study showed that disturbed SWA can forecast the rate of cortical Aβ accumulation across subsequent years, even when correcting for age, sex or sleep apnea (Winer et al., 2020 ). Sleep, and more specifically SWA, is thought to impact Aβ through its metabolic clearance function, a process that washes out metabolic waste products, such as Aβ (Fultz et al., 2019 ; Xie et al., 2013 ). Recently, blood-based assessments of Aβ have emerged as a cost-effective and minimally invasive method to monitor Aβ deposition in the brain (Thijssen et al., 2021 ; Verberk et al., 2020 ). Similarly to CSF- and cortical Aβ, plasma markers of Aβ have been shown to be unfavorably affected by total sleep deprivation (Eide et al., 2023 ; Liu et al., 2023 ), with SWA specifically identified as a critical factor predicting Aβ-levels (Rosenblum et al., 2024 ). The consistent evidence linking SWA to Aβ invites delving into the more specific phenomena within SWA and investigate how they may relate to Aβ. A distinctive electrophysiological process occurring during SWA is the coupling of slow waves (SWs) and sleep spindles (SW–spindle coupling). It is well established that SW–spindle coupling plays a crucial role in memory functioning (Rasch and Born, 2013 ). The hierarchical orchestration of neocortical SWs (< 1.25Hz), thalamocortical spindles (12–16Hz) and hippocampal ripples (80–250Hz) enables a temporally precise coordination between cortical and hippocampal networks (Buzsáki, 2015 ; Rasch and Born, 2013 ; Staresina et al., 2015 ). These electrophysiological events are organized in a phase-amplitude coupling hierarchy, where ripples are nested within spindle troughs, and spindles are further nested within SW peaks. The resulting synchronization allows memory traces to be selectively reactivated during sleep and gradually integrated into long-term cortical networks—a key component of system level memory consolidation (Rasch and Born, 2013 ). Research suggests that in the aging brain, spindles become uncoupled from SWs (Helfrich et al., 2018 ; Muehlroth et al., 2019 ), a process occurring gradually across the human life span (McConnell et al., 2021 ; Züst et al., 2023 ). Whereas there is a clear cross-frequency directionality of SWs driving spindles in younger individuals (i.e., spindles following the SW peak), in older individuals, a disruption of this hierarchical temporal structure occurs, characterized by the spindle shifting to precede the SW peak (Helfrich et al., 2018 ; Züst et al., 2023 ). Importantly, this shift is associated with both decreased memory function and atrophy in the medial frontal cortex (Helfrich et al., 2018 ; Muehlroth et al., 2019 ). Regarding biomarkers of neurodegeneration, only few studies have investigated its relationship with SW–spindle coupling in healthy older adults. Some reports showed that SW–spindle coupling was associated with tau burden (Winer et al., 2019 ) or plasma glial fibrillary acidic protein concentration (Züst et al., 2023 ). Only two studies indicated that detrimental SW–spindle coupling was associated with cortical Aβ-burden (Chylinski et al., 2022 ) or unfavorable plasma Aβ-levels (Wunderlin et al., 2023 ), respectively. An explanation for the sparsity of results may be that in healthy older adults, Aβ-levels are not elevated enough to be linked to microstructural features like SW–spindle coupling (Winer et al., 2020 ; Züst et al., 2023 ). With both SWA and its microstructural features (like SW–spindle coupling) having been linked to Aβ, the question arises whether SWA alone might merely represent a proxy for something more specific occurring during SWA, like SW–spindle coupling. Hence, our first aim was to explore whether SWA or SW–spindle coupling serves as predictor for Aβ. Using a sample of older adults ranging from cognitively healthy to -impaired allowed us to investigate whether predictive power of coupling on Aβ-dynamics depends on the level of cognitive functioning. Additionally, we examined whether SWA or SW–spindle coupling could—in terms of their predictive value—outperform other variables known to be linked to Aβ—such as cognitive functioning, age or sleep quality (Ju et al., 2013 ; Rodrigue et al., 2012 ). In the second part of this paper, we aim to re-explore whether phase-locked acoustic stimulation (PLAS)—a targeted intervention previously shown to enhance both SWA and SW–spindle coupling—might be associated with beneficial changes in Aβ. PLAS operates by presenting short acoustic stimuli during SWS, time-locked to the rising up-phase of a SW. A growing body of evidence shows that PLAS is associated with increased SWA and SW–spindle coupling—both in young (Leminen et al., 2017 ; Ngo et al., 2013 ; Ong et al., 2016 ; Wunderlin et al., 2021 ) and older adults (Papalambros et al., 2017 ; Wunderlin et al., 2024 ; Wunderlin et al., 2023 ; Zeller et al., 2024 ). Importantly, stronger PLAS-induced increases in SWA have been associated with more beneficial changes in Aβ across a three-night intervention, an effect that was more pronounced in cognitively impaired vs. healthy older adults (Wunderlin et al., 2023 ; Zeller et al., 2024 ). Here, we revisit these effects, specifically focusing on the previously unexplored question of whether improvements in SW–spindle coupling are also linked to a beneficial Aβ-response and whether this depends on the level of cognitive functioning level. Our results imply a unique predictive strength of SW–spindle coupling for baseline Aβ-levels, irrespective of cognitive functioning levels. Moreover, our results suggest that PLAS-induced increases in SWA benefit Aβ-response regardless of cognitive functioning levels, whereas PLAS-induced increases in SW–spindle coupling benefit Aβ-response exclusively in the cognitively impaired group. This hints towards a connection between SW–spindle coupling and Aβ-levels that goes beyond SWA. Methods Sample The baseline sample consisted of 47 older adults (mean age: 70.5 years, SE: 0.68, range: 59–80 years; 29 females, 18 males) with a range of cognitive functioning as measured via the Montreal Cognitive Assessment (MOCA, (Nasreddine et al., 2005 ); mean MOCA score: 26.2, SE: 0.40, range: 20–30). For the purpose of this study, MOCA scores below 26 indicate cognitive impairment (n = 17, mean MOCA score: 23.2, SE: 0.37), while scores from 26 to 30 (n = 30, mean MOCA score: 27.9, SE: 0.27) indicate cognitive health (Davis et al., 2021 ). A subset of the baseline sample of 39 older adults (mean age: 70.5 years, SE: 0.74, range: 59–79 years; 23 females, 16 males) completed a three-night PLAS protocol (see design & procedure section). The PLAS protocol sample ranged from cognitively impaired (n = 16, mean MOCA score: 23.2, SE: 0.39) to cognitively healthy (n = 23; mean MOCA score: 27.8, SE: 0.30; total sample mean MOCA score: 25.9, SE: 0.44, range: 20–30). The remaining eight participants from the baseline sample were not included in the PLAS protocol sample due to following a different protocol (n = 4) or due to unavailability of post-measurement blood samples (n = 4). See Table 1 for the characteristics of both samples. All participants underwent an extensive screening procedure to ensure that inclusion criteria were met. Exclusion criteria were sleep disorders and irregular sleep patterns as assessed via the Pittsburg Sleep Quality Index (PSQI, (Buysse et al., 1989 )) and via a screening night in the sleep laboratory to identify sleep apnea or restless leg syndrome, impaired hearing, non-fluency in German, current or previous neurological or psychiatric conditions, substance abuse and use of medication acting on the CNS. The study was conducted in accordance with the Declaration of Helsinki and approved by the local ethics committee of the canton of Bern, Switzerland. Written informed consent was obtained from all study participants. The current analyses represent an exploratory re-analysis of a preregistered study available under ClinicalTrials.gov (NCT04277104). Table 1 Characteristics of study participants. Means and standard errors are provided for both the baseline sample (= one night of natural sleep without intervention), as well as the PLAS protocol sample (= subset of the baseline sample with an additional three consecutive nights of phase-locked acoustic stimulation, see design and procedures). 1) categorical variable to determine the highest academic qualification, ranging from 1 (primary school) to 4 (University). 2) Montreal Cognitive Assessment, maximum score: 30, ≥ 26 cognitively healthy. 3) Pittsburgh Sleep Quality Index, < 5 normal sleep. 4) Apnea-Hypopnea Index < 5: normal sleep; 5 ≤ AHI < 15: mild sleep apnea. 5) Plasma amyloid-beta 42/40 ratio, lower scores are indicative of a higher risk for dementia. Baseline Sample (n = 47) Subset: PLAS protocol Sample (n = 39) mean se mean se Age 70.5 0.68 70.5 0.74 Sex 29 f, 18 m - 23 f, 16 m - Education 1 3.1 0.15 3.1 0.16 MOCA 2 26.2 0.40 25.9 0.44 PSQI 3 4.6 0.35 4.5 0.38 AHI 4 8.3 1.18 9.2 1.48 Baseline plasma Aβ42/Aβ40 5 0.067 0.001 0.066 0.002 Baseline plasma Aβ42 6.629 0.194 6.449 0.210 Baseline plasma Aβ40 98.677 1.984 97.516 2.323 Design & Procedure The full study design contained five nights in the sleep laboratory: an adaptation night, a baseline night (with sham-PLAS), and three experimental nights (with real-PLAS; see Fig. 1 ). The adaptation night served as an acclimatization to sleeping under laboratory conditions and as a screening night to screen for sleep-based exclusion criteria. After a recovery night at home, participants returned to the sleep laboratory for a baseline night. During the baseline night, sham-PLAS was administered, where a PLAS algorithm detected SW peaks in the online EEG signal and set time markers of these detections, but did not transmit any sound to the sleeping participant. After the baseline night, three consecutive experimental nights ensued, containing real-PLAS where upon detection of a SW peak, an acoustic stimulus (50ms of pink noise) was transmitted to the sleeping participant. Individual hearing thresholds were determined through hearing tests, and used as target stimulus intensity during PLAS. Acoustic stimuli were transmitted via sleepphones® (AcousticSheep LLC) at a mean volume of 69.2 dB(A). The PLAS algorithm is described elsewhere in more detail (Ruch et al., 2022 ; Wunderlin et al., 2022 ) and its application has previously been documented (Wunderlin et al., 2024 ; Wunderlin et al., 2023 ; Zeller et al., 2024 ). In brief, this algorithm analyzes the most recent 120 ms of data and computes correlations between the empirical topographical voltage distribution and a template topography of a typical slow wave peak. If the correlation is rising in > 75% of samples, a SW peak is predicted, and a stimulation is triggered. Hence, the algorithm is not dependent on the absolute amplitude of the signal—a metric that decreases with age (Colrain et al., 2010 ). Measures of cognitive functioning, including episodic memory performance, were assessed throughout the intervention. We previously demonstrated in these data that PLAS increases SWA, SW–spindle coupling and episodic memory performance, and is associated with beneficial Aβ-responses. For detailed descriptions of the tasks and results, see (Wunderlin et al., 2024 ; Wunderlin et al., 2023 ; Zeller et al., 2024 ). Here, we were selectively interested in a baseline measure of episodic memory performance as a potential explanatory variable in our statistical models (see statistical analysis section). Baseline episodic memory performance was assessed via a Face-Occupation Association (FOA) task, where participants encoded 40 faces that were each associated with one of 20 occupations (e.g. doctor or gardener). Baseline performance entailed the number of correctly recalled associations (0–40) after two encoding blocks. Sleep was recorded using a high-density EEG system (128-channel MicroCel Geodesic Sensor Net, Physio16 input box, 400 Series Geodesic EEG System, Magstim EGI, Eugene, OR, USA), at a sampling rate of 500 Hz, referenced to Cz. Polysomnographic scoring of sleep stages was performed according to the criteria of the American Academy of Sleep Medicine (Iber, 2007 ) by an experienced and certified somnologist. See Table 2 for participant’s baseline sleep architecture. Table 2 Sleep Architecture. Means and standard errors for polysomnographic variables are displayed. 1 Total Sleep Time in minutes. 2 Percentages of non-rapid eye movement (NREM) sleep stages N1-N3 and rapid eye movement (REM) sleep. 3 Minutes spent awake. 4 Wake time after sleep onset in minutes. 5 Sleep onset latency, i.e. the minutes in bed until sleep onset. 6 Sleep efficiency, i.e. the percentage of minutes spent asleep in relation to minutes lying in bed. Baseline Sample (n = 47) PLAS protocol Sample (n = 39) mean se mean se TST 1 334.6 9.46 342.3 9.12 % N1 2 30.3 1.71 30.6 1.90 % N2 2 47.9 1.23 47.2 1.13 % N3 2 8.7 1.12 9.0 1.32 % REM 2 13.1 0.73 13.2 0.82 Wake 3 136.3 7.89 140.0 7.43 WASO 4 121.2 7.37 124.1 6.83 SL 5 15.6 1.78 16.5 2.06 SE 6 71.7 1.52 71.1 1.61 Blood samples were drawn after the baseline night and after the last experimental night (E3, see Fig. 1 ). The samples were taken in the morning approximately an hour after waking and immediately centrifuged and stored in a -80°C freezer. Plasma samples were sent to the Neurochemistry Laboratory, Amsterdam University Medical Center (Amsterdam, The Netherlands), where Plasma Aβ 1–42 and 1–40 peptides were identified by means of single molecule array immunoassays (IA-N4PE; (Thijssen et al., 2021 )). Aβ 1–42/1–40 ratios were calculated to account for interindividual and preanalytical variability. Lower Aβ 42/40 ratios indicate a greater risk for amyloid deposition (Graff-Radford et al., 2007 ; Verberk et al., 2020 ). EEG processing EEG processing was performed in MATLAB R2022b (MathWorks) using the toolboxes EEGLAB (Delorme and Makeig, 2004 ), FieldTrip (Oostenveld et al., 2010 ) and CircStat (Berens, 2009 ), as well as the phase-amplitude coupling analysis framework by Jiang et al. ( 2015 ). The data was down-sampled to 200Hz and preprocessed via the PREP pipeline for EEGLAB (Bigdely-Shamlo et al., 2015 ) and FieldTrip’s automatic artifact rejection pipeline. All analyses were performed on sleep containing slow wave activity (N2/N3 sleep) only. Discrete SW, spindle and coupling event detection . The procedure for the detection of SWs, spindles and coupling events followed previously published work (Helfrich et al., 2018 ; Mölle et al., 2009 ; Staresina et al., 2015 ). All events were detected in channel Fz. For the detection of SWs, all zero-crossings within the SW-band (0.16–1.25 Hz) filtered data were marked. SW peaks and SW troughs were defined as the highest and lowest values between two successive positive-to-negative zero-crossings, provided they met a duration (0.8–2 s apart) and amplitude (75th percentile) criterion (Mölle et al., 2009 ). For the detection of spindles, the signal was first filtered between 12 and 16 Hz. Next, the instantaneous amplitude (envelope) of the filtered signal was calculated using a Hilbert transform and smoothed using a 200ms moving average. Data segments exceeding the 75th amplitude percentile for a duration of 0.5–3s were defined as spindles at their maximum value (Staresina et al., 2015 ). Finally, SW–spindle coupling events were defined as spindles within a range of ± 2.5 s time-locked to the SW trough, as previously described (Helfrich et al., 2018 ). For a graphical illustration of slow waves, spindles and their coupling, see Fig. 2 A. SW–spindle coupling. In addition to the quantity of SW–spindle coupling events, two measures capturing the quality, i.e. the exact synchronization of SWs and spindles were calculated: SW–spindle coupling strength : coupling strength was measured via the resultant vector length (RVL) (Berens, 2009 ). The RVL quantifies how consistently spindles are coupled with SWs (see Fig. 2 B). A longer RVL suggests that most spindles are closely clustered around their mean SW phase, indicating stronger coupling. A shorter RVL is indicative of greater variance of spindles within the SW phase, which suggests weaker coupling. To calculate RVL, the data from channel Fz was first filtered within the SW-band (0.16–1.25 Hz). Next, the instantaneous phase angle of the filtered signal was calculated using a Hilbert transform. For the above-defined SW–spindle coupling events, the individual phase angles were extracted and used to calculate the mean RVL using CircStat’s circ_r function. SW–spindle coupling directionality : coupling directionality was measured using the Phase Slope Index (PSI) (Jiang et al., 2015 ). Here, PSI was calculated between the phase of the SW frequency (0.5–2 Hz; in steps of 0.5 Hz) and the amplitude of the spindle frequency (12–16 Hz; in steps of 1 Hz). The PSI provides a measure of lag/lead between two frequencies, where 0 suggests no consistent directional influence of one over the other frequency. A positive value suggests a forward interaction, indicative of the SW leading the spindle, and a negative value suggests a reverse influence with the spindle leading the SW (see Fig. 2 C). The former (a positive value) is indicative of a coupling hierarchy toward a “younger” and less atrophic brain (Helfrich et al., 2018 ; Züst et al., 2023 ). To calculate PSI, the EEG data in Fz was epoched ± 2.5s around the previously detected SW troughs. Frequency power was estimated analyzing five cycles of each frequency within a sliding window of 2s, moving in steps of 1 second. Finally, for each participant, average PSI values were calculated over the frequency subbands. Statistical Analysis Baseline Analyses. To analyze which variables best explain Aβ 42/40 ratio, an optimized regression model was calculated. Although the main dependent variable of interest was Aβ 42/40, all analyses were additionally repeated for the single peptides Aβ 42 and 40. As a starting point, an a priori regression model was defined: Aβ variable ~ MOCA score + number of coupled spindles + SW amplitude + age + coupling strength + coupling directionality. Next, a stepwise regression (max. steps: 10) was performed using R-squared as the optimization criterion to iteratively refine the model. In this process, other potential predictors (spindle amplitude, percentage of sleep stages N1, N2, N3, REM, total sleep time, sleep efficiency, baseline memory performance and sex) were considered. At each step, the model examined the effect of adding a new predictor or removing a predictor already in the model on the overall R-squared. A predictor was added to the model if its inclusion increased R-squared by at least 0.1, and an existing predictor was removed if its exclusion reduced R-squared by no more than 0.05. To strengthen the robustness of our results, the optimized regression models were calculated both conservatively (excluding potential outliers as determined via Cook’s distances > 4/n (Belsley et al., 2005 )) and liberally (including all data points). Additional analyses to ensure the robustness of our results entailed recalculating all models without a specific a priori model. An advantage of not using an a priori model is to reduce potential bias stemming from the pre-selection of predictors. If all procedures (liberal, conservative, informed (with a priori model), and empty (without a priori model)) yielded similar results, we considered the results to be robust. PLAS protocol Analyses. In addition to the baseline analysis, we investigated relative changes during the three experimental nights (where PLAS was applied) compared to the baseline night without stimulation (see Fig. 1 ). We will refer to these relative changes as PLAS-induced changes. To analyze how PLAS-induced changes in sleep markers of interest (SW amplitude and SW–spindle coupling) interact with changes in Aβ 42/40 ratios from pre to post intervention, linear mixed effects models (LMM) were calculated. SW amplitude and SW–spindle coupling were used as sleep markers of interest because (A) they are specific markers that PLAS has been shown to increase (Ngo et al., 2013 ; Wunderlin et al., 2023 ), and (B) they are linked to Aβ, as demonstrated by previous research (Chylinski et al., 2022 ; Mander et al., 2015 ; Varga et al., 2016 ) as well as our baseline analyses. SW amplitude and SW–spindle coupling were predicted on the single trial level. Regarding SW amplitude, this meant that each detected SW event within a night was treated as a separate trial, with each event containing an amplitude value. Regarding SW–spindle coupling, each SW–spindle coupling event within a night served as a separate trial. Here, we first calculated the mean phase angle per night using CircStat’s circ_mean function. Next, the absolute distance of each event from the individual mean phase angle was calculated and divided by 180 (the maximum value), to get the relative distance of each event. The relative distance was then subtracted from 1 to provide a “closeness” estimate instead of a distance estimate per trial, where 1 means maximum “closeness” and 0 minimum “closeness”. Hence, higher values indicate that the spindle is closely aligned with the individual average angle for the night, while lower values indicate that the spindle is further from the individual average angle. This measure can be considered an approximation of the RVL, hence coupling strength, at the single trial level. We further explored an approximation to the PSI (as a measure for coupling directionality) on the single trial level, by calculating each spindle’s phase angle difference from zero. 0° can be seen as the reversal-point of cross-frequency directionality, with numbers below zero indicating that the spindle lies before the SW peak and numbers above 0 indicating that the spindle lies after the SW peak. The distances both below and above 0 in degrees were rescaled and mapped from their minimum to maximum values onto a range from − 1 to 1, with zero corresponding to 0°. Note that this approach does not measure precisely the same aspect as PSI. While a consistent phase angle difference indicates temporal precedence, the PSI additionally reflects a directional influence by quantifying the consistency of the phase difference as a function of frequency, which our surrogate metric cannot evaluate on single-trial level. Nonetheless, we consider the measure described to be a conceptually close approximation, albeit with some inherent limitations. Three separate LMMs were calculated for the prediction of SW amplitude, SW–spindle coupling strength, and coupling directionality. The model included the categorical variable nights (BL, E1, E2, E3, see Fig. 1 ), the change score in Aβ 42/40 from pre to post intervention, as well as their interaction as fixed effects. We controlled for age and MOCA score and included a random intercept for each participant. Maximum likelihood estimation was used to fit the model. Note that the factor night is coded with the first night (pre-intervention, baseline) as reference. Effects on SW amplitude and SW–spindle coupling can therefore be interpreted as contrasts against the baseline night, hence PLAS-induced changes. Results Slow wave–spindle coupling as best predictor for plasma amyloid-beta To examine what variables best explain differences in Aβ-values at baseline, optimized regression analyses were conducted. The conservative optimized regression analysis showed that the model best explaining differences in Aβ 42/40 was the model incorporating both coupling strength (β = 0.052, t(40) = 4.07, p < 0.001 ) and coupling directionality (β = 1.373, t(40) = 3.74, p < 0.001 ), but none of the other potential predictors (F(2, 40) = 13.7, adj. R 2 = 0.38, p < 0.001 , see Fig. 3 A). The same relationship was shown in the more liberal model, where potential outliers were not removed (F(2, 44) = 7.43, adj. R 2 = 0.22, p = 0.002 ; coupling strength: β = 0.036, t(44) = 3.03, p = 0.004 ; coupling directionality: β = 1.045, t(44) = 2.69, p = 0.01 ). For both the initial and conservative model, results did not change when using an empty (without a priori) model as baseline for the optimized regression instead of the informed (with a priori) model. For a visualization of the relationship between Aβ 42/40 and coupling parameters, see the left panels in Fig. 3 C and D. Predicting the Aβ 42 and 40 peptides individually revealed that effects were mainly carried by the Aβ 42 peptide (see Fig. 3 B). Coupling strength and coupling directionality were consistently the best predictors for Aβ 42, both in the conservative (F(2, 42) = 9.81, adj. R 2 = 0.29, p < 0.001 ; coupling strength: β = 6.078, t(42) = 3.13, p = 0.003 ; coupling directionality: β = 192.853, t(42) = 3.36, p = 0.002 ) and more liberal models (F(2, 44) = 8.58, adj. R 2 = 0.25, p < 0.001 ; coupling strength: β = 5.719, t(44) = 3.24, p = 0.002 ; coupling directionality: β = 166.09, t(44) = 2.91, p = 0.006 ) and results did not change with an empty model as opposed to the informed model. See the middle panels in Fig. 3 C and D for a visualization of the relationship between Aβ 42 and coupling parameters. For the Aβ 40 peptide, results showed no consistent effect. In the conservative model, coupling strength predicted Aβ 40 at trend levels (β = 36.484, t(43) = 1.844, p = 0.072; F(1, 43) = 3.4, adj. R 2 = 0.05, p = 0.072, see Fig. 3 B), but this was not seen in the more liberal model, where MOCA score and SW amplitude were the best predictors (MOCA: β=-1.495, t(44)=-2.046, p = 0.047 ; SW amplitude: β = 0.81, t(44) = 1.984, p = 0.054; F(2, 44) = 3.23, adj. R 2 = 0.09, p = 0.049). Furthermore, when using an empty instead of an informed model, both conservative and liberal procedures revealed no significant effect of any variable. The relationship between Aβ 40 and coupling is visualized in Fig. 3 C and D, right panels. To investigate whether effects differed depending on the level of cognitive functioning, the analyses were repeated, separately for cognitively healthy (n = 30) and cognitively impaired (n = 17) participants (see suppl. table S1 A for between-group characteristics). The only variable consistently appearing in all models (liberal, conservative, empty, informed) as a significant predictor for Aβ 42/40 and Aβ 42 was coupling strength for healthy older adults, and coupling directionality for cognitively impaired older adults (see supplementary material, table S2). For Aβ 40, no consistency across models was observed. These results suggest that, irrespective of cognitive functioning level, slow wave–spindle coupling was the best predictor for Aβ 42/40 and Aβ 42. The results further suggest that distinct mechanisms of slow wave–spindle coupling may be relevant at different levels of cognitive functioning. In sum, the results show that coupling strength and coupling directionality are better predictors for Aβ 42/40, and Aβ 42 than any other variable in the model, such as age, MOCA score, or SW amplitude—all variables expected to be linked to Aβ (e.g. Rodrigue et al., 2012 ; Rosenblum et al., 2024 ). Specifically, more favorable Aβ-levels were linked to a more consistent clustering of spindles within the SW phase as opposed to a more chaotic placement (coupling strength). This was particularly seen in healthy older adults. Additionally, more beneficial Aβ-levels were linked to the SW leading the spindle as opposed to the spindle leading the SW (coupling directionality), which was particularly pronounced in the cognitively impaired group. Both these measures were associated with plasma Aβ in a way that resembles the following statement: The more the brain resembled a younger state from a coupling-physiology perspective, the more beneficial the plasma Aβ profile. PLAS-induced gains in SW amplitude/ SW–spindle coupling strength link to beneficial Aβ-response As our baseline analyses indicated a strong link between SW–spindle coupling and Aβ, we analyzed in a next step whether Aβ-levels could profit from an intervention-induced increase in coupling quality. An additional variable of interest was SW amplitude, as previous reports showed that improvements in SW amplitude were linked to a beneficial Aβ-response (Wunderlin et al., 2023 ; Zeller et al., 2024 ). Different LMMs were conducted to examine PLAS effects on sleep parameters (SW amplitude and SW–spindle coupling) across three experimental nights (E1, E2 and E3), as well as how these effects interact with changes in Aβ 42/40 ratio from pre to post intervention. Table 3 A summarizes the main results for the full model incorporating all participants. There were significant main effects for SW amplitude in all three experimental nights (E1: β = 0.17, p = 0.008; E2: β = 0.81, p < 0.001; E3: β = 0.27, p < 0.001, Table 3 A, left panel) as well as SW–spindle coupling strength (E1: β = 0.012, p < 0.001; E2: β = 0.01, p = 0.001; E3: β = 0.007, p = 0.021, Table 3 A, right panel), but not SW–spindle coupling directionality (see suppl. material table S3). These findings suggest an overall increase in SW amplitude and SW–spindle coupling strength, but no enhancement in coupling directionality, during each of the three experimental nights in relation to the baseline night. In other words, PLAS increased SW amplitude and SW–spindle coupling strength, but not coupling directionality. For the increase in SW amplitude but not coupling strength nor directionality, a trend-level effect for MOCA score was observed (β = 0.61, p = 0.058), indicating that higher cognitive functioning levels were associated with higher SW-amplitude but not increased SW–spindle coupling quality. Notably, highly significant interaction effects were found between Aβ 42/40 ratio change from pre to post intervention and the increase in SW amplitude during all three nights (E1 x Aβ 42/40 change: β = 166.1, p < 0.001; E2 x Aβ 42/40 change: β = 87.6, p < 0.001; E3 x Aβ 42/40 change: β = 150.6, p < 0.001, see Table 3 A, left panel). These results indicate that PLAS-induced increases in SW amplitude were strongly and consistently associated with a beneficial Aβ-response across the intervention. Regarding SW–spindle coupling strength, only the third experimental night showed a significant interaction with Aβ 42/40 change (E3 x Aβ 42/40 change: β = 2.5, p = 0.006, see Table 3 A, right panel), indicating that increases in SW–spindle coupling strength in the last experimental night are linked to a beneficial Aβ-response. The left panels in Fig. 4 A and 4 B visualize these associations, showing the relationship between Aβ 42/40 change scores and increases in SW amplitude and SW–spindle coupling strength across the three nights, respectively. Removing one outlier subject identified by visual inspection (see Fig. 4 B, left panel) eliminated the previously significant interaction between Aβ change and increases in coupling strength in the third night. For coupling directionality, no significant interaction effects were observed (see suppl. material, table S3). To sum up, in the full sample, only increases in SW amplitude, but not SW–spindle coupling strength or directionality seem to be associated with beneficial Aβ-response. To investigate whether effects were dependent on the level of cognitive functioning, we conducted separate analyses for cognitively impaired and healthy participants (see suppl. table S1 B for between group characteristics). While interaction effects between increases in SW amplitude and Aβ 42/40 change were seen in both the cognitively impaired and the healthy subgroup (see Fig. 4 A, right panel and Table 3 B and C, left panels), this was not the case for the increase in SW–spindle coupling strength. Here, in the cognitively healthy subgroup, no significant interaction effects were observed, regardless of whether the outlier participant was included or excluded (see Table 3 B, right panel and Fig. 4 B, right panel). In the cognitively impaired subgroup however, there was a trend-level interaction effect for the first experimental night (E1 x Aβ 42/40 change: β = 3.3, p = 0.058), and significant interaction effects for the second and third night (E2 x Aβ 42/40 change: β = 7.0, p < 0.001; E3 x Aβ 42/40 change: β = 4.6, p = 0.008, see Table 3 C, right panel, Fig. 4 B, right panel). For SW–spindle coupling directionality, neither group showed significant interaction effects (see suppl. material, table S3). Our post hoc analyses investigated whether effects were primarily driven by either the 42 or the 40 peptide. Positive interactions between changes in Aβ and changes in sleep parameters were only observed for the 42 peptide, although inconsistently (SW amplitude: p E1 0.3; p E3 0.6; p E3 >0.4), and not for the 40 peptide. This suggests a potential tendency for the observed effects to be somewhat more carried by the 42 peptide. However, due to the lack of consistency, our observed effect is more clearly attributable to the Aβ 42/40 ratio rather than to the 42 peptide alone. Together these results suggest that PLAS-induced increases in SW amplitude are associated with a beneficial Aβ-response regardless of cognitive functioning level. In contrast, PLAS-induced increases in SW–spindle coupling strength appear to correlate with a beneficial Aβ-response only within the cognitively impaired group. Changes in coupling directionality were not associated with changes in Aβ, irrespective of cognitive functioning levels. Table 3 Results of linear mixed effect models (LMMs). The models tested whether PLAS-induced changes in SW amplitude and SW–spindle coupling strength (as measured via an approximation to the resultant vector length on the single trial level) interact with changes in Aβ 42/40 ratios from pre to post intervention. A. results for the full model containing all participants. B. results for the cognitively healthy (MOCA score ≥ 26) subgroup. C. results for the cognitively impaired (Moca Score < 26) subgroup. The models both investigated increases in SW amplitude (left panels) and SW–spindle coupling strength (right panels). Age and MOCA score were included as control variables, with each participant assigned a random intercept. In each model, interactions are highlighted in grey, with p-values for significant interaction and main effects (p < .05) shown in bold. A. Full sample models SW amplitude SW–spindle coupling strength Estimate Std. Error t p Estimate Std. Error t p (Intercept) -11.134 16.241 -0.686 0.497 0.336 0.191 1.762 0.086 Night E1 0.169 0.063 2.661 0.008 0.012 0.003 3.848 < 0.001 Night E2 0.81 0.063 12.914 < 0.001 0.01 0.003 3.201 0.001 Night E3 0.266 0.062 4.323 < 0.001 0.007 0.003 2.317 0.021 Aβ change -71.572 250.514 -0.286 0.777 1.561 2.992 0.522 0.605 MOCA score 0.613 0.304 2.02 0.05 0.005 0.004 1.322 0.194 age 0.157 0.183 0.859 0.396 0.001 0.002 0.473 0.639 Night E1 : Aβ change 166.104 18.861 8.807 < 0.001 1.323 0.923 1.434 0.152 Night E2 : Aβ change 87.633 18.935 4.628 < 0.001 1.528 0.917 1.666 0.096 Night E3 : Aβ change 150.576 18.712 8.047 < 0.001 2.539 0.919 2.762 0.006 B. Cognitively healthy sample models SW amplitude SW–spindle coupling strength (Intercept) -25.036 24.82 -1.009 0.324 0.331 0.296 1.121 0.274 Night E1 0.236 0.085 2.77 0.006 0.008 0.004 2.136 0.033 Night E2 1.091 0.084 12.979 < 0.001 0.01 0.004 2.733 0.006 Night E3 0.571 0.082 6.929 < 0.001 0.001 0.004 0.369 0.712 Aβ change -104.585 311.864 -0.335 0.74 5.94 3.767 1.577 0.128 MOCA score 1.372 0.745 1.842 0.078 0 0.009 0.043 0.966 age 0.054 0.219 0.247 0.807 0.003 0.003 1.115 0.277 Night E1 : Aβ change 161.65 23.71 6.818 < 0.001 0.338 1.08 0.313 0.754 Night E2 : Aβ change 76.374 24.136 3.164 0.002 -1.075 1.084 -0.991 0.322 Night E3 : Aβ change 62.656 23.541 2.662 0.008 1.47 1.074 1.369 0.171 C. Cognitively impaired sample models SW amplitude SW–spindle coupling strength (Intercept) 0.209 25.286 0.008 0.993 0.715 0.314 2.28 0.037 Night E1 0.043 0.093 0.459 0.647 0.018 0.005 3.419 0.001 Night E2 0.385 0.093 4.133 < 0.001 0.008 0.005 1.518 0.129 Night E3 -0.246 0.091 -2.694 0.007 0.016 0.005 3.11 0.002 Aβ change -151.533 381.783 -0.397 0.697 -6.41 4.841 -1.324 0.202 MOCA score -0.95 0.744 -1.278 0.22 -0.003 0.009 -0.316 0.756 age 0.504 0.301 1.675 0.113 -0.002 0.004 -0.506 0.62 Night E1 : Aβ change 163.823 31.546 5.193 < 0.001 3.264 1.721 1.897 0.058 Night E2 : Aβ change 113.477 30.76 3.689 < 0.001 7.015 1.676 4.184 < 0.001 Night E3 : Aβ change 355.484 31.254 11.374 < 0.001 4.565 1.721 2.653 0.008 Discussion Sleep, and in particular SWS has been consistently connected to Aβ-dynamics (Ju et al., 2017 ; Mander et al., 2015 ; Rosenblum et al., 2024 ; Varga et al., 2016 ; Winer et al., 2020 ). Less is known about the link between Aβ and specific microstructural aspects within SWS, such as the coupling of SWs and sleep spindles. Here we show that in older adults ranging in cognitive functioning levels, baseline SW–spindle coupling strength (i.e., how consistent the coupling is) and SW–spindle coupling directionality (i.e., whether the spindle or the SW leads) are the best predictors for plasma Aβ-dynamics. Specifically, more favorable Aβ-levels were best predicted by a more consistent clustering of spindles within the SW phase and a shift of the coupling hierarchy toward a “younger” status where the SW leads. We further explored how a three-night PLAS intervention—a non-invasive tool known to boost both SWA and SW–spindle coupling—interacts with Aβ-response. Results showed that PLAS-induced increases in SW amplitude were associated with a more beneficial Aβ-response from pre- to post-intervention, irrespective of cognitive functioning levels. Interestingly, PLAS-induced increases in SW–spindle coupling strength (but not -directionality) were only associated with a more favorable Aβ-response in the cognitively impaired, but not the healthy subgroup. This suggests that PLAS-induced increases in SW–spindle coupling might be selectively linked to beneficial Aβ-response in cognitively impaired older adults, where these dynamics are arguably deteriorating and may thus exhibit more room for improvement. SW–spindle coupling measures were the best predictors for plasma Aβ 42/40 ratio and Aβ 42 cross-sectionally, better than any other sleep quality measure, such as SW and spindle amplitudes, sleep duration, sleep efficiency, or the percentage of different sleep stages. SW–spindle coupling measures were also better predictors for Aβ 42/40 ratio and Aβ 42 than age, sex or cognitive functioning, as assessed via an episodic memory task and the MOCA score (Nasreddine et al., 2005 ). This unique predictive value, specifically related to the more pathogenic 42 peptide (Findeis, 2007 ), suggests that SW–spindle coupling is a critical physiological process related to the early pathophysiology of Alzheimer’s disease. One study conducted in healthy older adults showed that precise SW–spindle coupling was significantly associated with Aβ-burden over the medial prefrontal cortex (Chylinski et al., 2022 ). This result was specific for SW–spindle coupling: SWA was not associated with Aβ-burden, contrasting previous reports (Mander et al., 2015 ). Like our results, this suggests an important and potentially specific role of SW–spindle coupling beyond SWA in predicting Aβ-burden. SW–spindle coupling is known to involve an interplay of neocortical SWs, thalamocortical spindles and hippocampal ripples, facilitating efficient information transfer across widespread brain regions and playing a critical role in memory consolidation (Diekelmann and Born, 2010 ; Rasch and Born, 2013 ; Staresina et al., 2015 ). In aging, spindles become uncoupled from SWs, a change that is linked to both brain atrophy and decreased memory functions (Helfrich et al., 2018 ; Muehlroth et al., 2019 ). Arguably, early unfavorable Aβ-dynamics might specifically disturb the thalamocortical interplay driving the co-occurrence of spindles and SWs before SWA itself is affected. While SWA may remain intact, the finer, micro-oscillatory dynamics like SW–spindle coupling might already be disturbed. However, the causal direction of this relationship is not resolved. SW–spindle coupling might also have a specific function in driving Aβ-dynamics. SWA has been shown to be involved in glymphatic clearance, where neurotoxins such as Aβ are washed out of the brain (Fultz et al., 2019 ; Xie et al., 2013 ). Well-functioning coupling-dynamics might therefore be a sign of a highly functioning glymphatic clearance system. Arguably, SW–spindle coupling may serve a similar or at least supporting function in glymphatic clearance as has been shown for SWA (Fultz et al., 2019 ). To answer this question, however, more research is needed, specifically using animal models or human intracranial recordings allowing for more direct physiological measurements and manipulations. Our results showed that both coupling strength and coupling directionality were important predictors of Aβ. When separately analyzing participants categorized based on their cognitive functioning, we found that in cognitively impaired older adults, Aβ was specifically associated with coupling directionality whereas in cognitively healthy older adults, Aβ was specifically linked to coupling strength. These different SW–spindle coupling metrics may reflect different levels of functional priority. Coupling strength indicates the circular spread of spindles across the SW, i.e. how tightly spindles are clustered around their preferred phase. Coupling directionality, on the other hand, measures a more fine-grained interaction between the two oscillatory signals, reflecting the consistency of phase lag or lead, i.e., where on the SW the spindle occurs. Using an orchestra analogy, coupling strength could represent the alignment of musicians, such as how precisely they synchronize with each other in terms of timing and expression. Coupling directionality might represent how closely the musicians follow the conductor, ensuring the overall coordination of the piece. If the musicians fail to follow the conductor, the performance can fall apart completely—they might even play different sections or fall out of sync entirely. However, if the musicians’ alignment with each other is imperfect, the performance can still work—albeit with some declines in quality. In line with the orchestra analogy, we propose that coupling directionality may represent the necessary, foundational basis for optimized functionality (“following the conductor”), whereas coupling strength might rather serve a secondary, facilitating mechanism (“playing in synchronization”). Hence, our results suggest that in cognitively impaired older adults, more detrimental Aβ-levels are associated with the breakdown of the hierarchy of SWs and spindles, which may be so fundamental that it leaves no room for the clustering of SWs and spindles to be of predictive value. In healthy older adults, more detrimental Aβ-levels are associated with a deterioration in the clustering of SWs and spindles, but not with the underlying hierarchy. Hence, in healthy participants, irrespective of Aβ-levels, the fundamental mechanism is still intact, while the more supplementary mechanism could be optimized in those with more detrimental Aβ-levels. Although our results suggest that distinct mechanisms of phase-amplitude coupling may be relevant at different levels of cognitive functioning, the neurophysiological correlates of these specific SW–spindle coupling measures have yet to be elucidated. After identifying SW–spindle coupling as a strong predictor for plasma Aβ ratios, we aimed to investigate whether intervention-induced increases in SW–spindle coupling might be associated with more beneficial Aβ-responses. Our results showed that a three-night PLAS intervention led to both increases in SWA as well as increases in SW–spindle coupling, paralleling previous reports (Leminen et al., 2017 ; Ngo et al., 2013 ; Papalambros et al., 2017 ; Wunderlin et al., 2023 ). The pooled analysis revealed that only PLAS-induced increases in SW amplitude, but not PLAS-induced increases in SW–spindle coupling strength were associated with a beneficial Aβ-response from pre- to post-intervention. When separately analyzing healthy and cognitively impaired older adults, PLAS-induced increases in SW amplitude were consistently associated with a beneficial Aβ-response. Hence there seems to be a clear relationship between increases in SWA and more favorable changes in Aβ-levels, irrespective of cognitive functioning levels. This is in line with the hypothesis that SWA serves an important role in glymphatic clearance mechanisms potentially allowing for better washout of neurotoxins (Fultz et al., 2019 ; Xie et al., 2013 ). Strikingly, PLAS-induced increases in SW–spindle coupling strength were also associated with a beneficial Aβ-response, albeit only in the cognitively impaired, but not the healthy subgroup. Hence, in the cognitively impaired subgroup, our results suggest that increases in SW–spindle coupling may also play a part in Aβ-dynamics. Again, it could be argued that SW–spindle coupling might serve a similar or at least supporting clearance function as is the case for SWA. The PLAS-induced increase in SWA and coupling and the subsequently observed direct association between this increase and improved Aβ-response suggests that PLAS could be a valuable tool for enhancing Aβ-dynamics in individuals at risk for dementia. The reason for a selective effect of a PLAS-induced increase in SW–spindle coupling on more beneficial Aβ-response in the cognitively impaired, but not the healthy group, is a matter of speculation. Arguably, as PLAS targets SWs, PLAS-induced increases in SWs/SWA may represent the primary effect of PLAS, while increases in SW–spindle coupling likely emerge as a downstream, secondary effect: the increased SWA may create more windows of opportunity for coupling to occur. Because of a potentially disadvantaged starting position, PLAS-induced increases in SW amplitude may more effectively drive downstream improvements in SW–spindle coupling in the cognitively impaired group. Support for the notion of a more disadvantaged starting position in cognitively impaired older adults comes from previous research suggesting that atrophy and worse memory are associated with worse SW–spindle coupling (Helfrich et al., 2018 ; Muehlroth et al., 2019 ). However, although in our sample, baseline SW–spindle coupling was decreased in cognitively impaired compared to healthy individuals, these differences were not significant (see suppl. table S1 ). An alternative explanation for linking PLAS-induced increases to more beneficial Aβ-responses could be that instead of a causal effect of the stimulation, the network response to PLAS might simply serve as a predictor of how Aβ-levels progress, without the stimulation itself playing a causal role. However, if more responsive brains were to explain our results, we would expect to find effects either in both groups, or in the healthy group only, where brain responsiveness is arguably higher than in the cognitively impaired group. Limitations Although we used single-trial approaches to analyze both coupling strength and coupling directionality, we only found effects for the former. We acknowledge that our approach to coupling directionality on the single trial level does not measure precisely the same aspect as coupling directionality on the aggregated level, the phase slope index. Hence it is possible that PLAS effects were truly limited to coupling strength rather than coupling directionality; however, it is also conceivable that our single trial approach for coupling directionality was not sensitive enough to detect such effects. In line with our previous argument, the absence of an effect for coupling directionality is at least plausible. Our PLAS intervention—particularly given its short-term nature—can enhance SWA as intended, leading to increased clustering of spindles and SWs, but cannot affect the more fine-grained underlying process. It is possible that a longer intervention would be required to impact this finer process. Our analyses are based on a limited sample size. However, our baseline sample is comparable to a similar study that investigated plasma levels of Aβ in relation to sleep markers (Rosenblum et al., 2024 ). Studies examining the effects of sleep deprivation on plasma markers of Aβ have relied on smaller samples than those used in our intervention sample (Eide et al., 2023 ; Liu et al., 2023 ). While larger samples would strengthen the generalizability of our findings, we took methodological steps to bolster the robustness of our results. Specifically, we employed mixed-effects models on the single trial level in our intervention analyses. This maximizes statistical power by leveraging all available data points while accounting for individual variability through random intercepts to increase statistical power. In our baseline analyses, we validated key findings across multiple statistical approaches—including conservative, liberal, informed, and empty models. Only results consistent across all models were interpreted as robust. Conclusion In this paper, we have established a specific role for SW–spindle coupling and Aβ-dynamics—both in unmodulated and PLAS-modulated sleep. The unique predictive strength for Aβ, even surpassing SWA and cognitive functioning levels, suggests that SW–spindle coupling is a critical physiological process related to the early pathophysiology of dementia. Newly developed targeted interventions could therefore prioritize older adults with detrimental coupling- dynamics, focusing on those who would potentially benefit the most. Furthermore, our results suggest that PLAS-induced increases in sleep markers are closely linked to favorable Aβ-responses—particularly in cognitively impaired older adults. Overall, the results suggest that PLAS is a useful tool that could yield favorable outcomes for Aβ levels and therefore help in fighting increasing incidence rates of dementia. Abbreviations Aβ Amyloid-beta AHI Apnea-Hypopnea Index BL Baseline E1 – E3 Experimental nights 1 to 3 FOA Face-Occupation Associations LMM Linear mixed effects model MOCA Montreal Cognitive Assessment N1 – N3 Sleep stages 1 to 3 NREM Non-rapid eye movement PLAS Phase-locked acoustic stimulation PSI Phase Slope Index PSQI Pittsburgh Sleep Quality Index REM Rapid eye movement RVL Resultant vector lengt SWA Slow wave activity SWS Slow wave sleep SW(s) Slow wave(s) SE Sleep efficiency SL Sleep latency TST Total sleep time WASO Wake time after sleep onset Declarations Acknowledgement We thank all interns, students and assistants for their valuable work during data acquisition. Funding This work was supported by Dementia Research: Synapsis Foundation Switzerland, in collaboration with the Peter Bockhoff Foundation, the Heidi Seiler Foundation, and the Kurt Fries Foundation: grants 2018-PI02 & 2021-CDA03. This work was further supported by the Swiss National Science Foundation (SNSF): grant number 215333. Author Information Authors and Affiliations Marina Wunderlin: University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland Korian Wicki: University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland Graduate School for Health Sciences, University of Bern, 3012 Bern, Switzerland Charlotte Elisabeth Teunissen: Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands Marc Alain Züst: University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland Contributions Conceptualization: MAZ, MW Study Design: MAZ, MW Data Collection: MW Data Analysis: MW, MAZ, CET Visualization: MW, MAZ Writing—original draft: MW Writing—review: MAZ, CET, KW Writing—editing: MW, MAZ Corresponding authors Marc A. 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Thijssen, E.H., Verberk, I.M.W., Vanbrabant, J., et al. Highly specific and ultrasensitive plasma test detects Abeta(1-42) and Abeta(1-40) in Alzheimer’s disease. Sci Rep, 2021, 11: 9736. Varga, A.W., Wohlleber, M.E., Giménez, S., et al. Reduced Slow-Wave Sleep Is Associated with High Cerebrospinal Fluid Aβ42 Levels in Cognitively Normal Elderly. Sleep, 2016, 39: 2041–2048. Verberk, I.M.W., Thijssen, E., Koelewijn, J., et al. Combination of plasma amyloid beta(1-42/1-40) and glial fibrillary acidic protein strongly associates with cerebral amyloid pathology. Alzheimers Res Ther, 2020, 12: 118. Winer, J.R., Mander, B.A., Helfrich, R.F., et al. Sleep as a Potential Biomarker of Tau and β-Amyloid Burden in the Human Brain. J. Neurosci., 2019, 39: 6315–6324. Winer, J.R., Mander, B.A., Kumar, S., et al. Sleep Disturbance Forecasts β-Amyloid Accumulation across Subsequent Years. Current biology : CB, 2020, 30: 4291. Wunderlin, M., Koenig, T., Zeller, C., Nissen, C., Züst, M.A. Automatized online prediction of slow wave peaks during NREM sleep in young and old individuals: Why we should not always rely on amplitude thresholds. Journal of Sleep Research, 2022,. Wunderlin, M., Zeller, C.J., Senti, S.R., et al. Acoustic stimulation during sleep predicts long-lasting increases in memory performance and beneficial amyloid response in older adults. Age Ageing, 2023, 52: afad228. Wunderlin, M., Zeller, C.J., Wicki, K., Nissen, C., Züst, M.A. Acoustic stimulation during slow wave sleep shows delayed effects on memory performance in older adults. Frontiers in Sleep, 2024, 2:. Wunderlin, M., Züst, M.A., Hertenstein, E., et al. Modulating overnight memory consolidation by acoustic stimulation during slow-wave sleep: a systematic review and meta-analysis. Sleep, 2021, 44: zsaa296. Xie, L., Kang, H., Xu, Q., et al. Sleep Drives Metabolite Clearance from the Adult Brain. Science, 2013, 342: 373–377. Zeller, C.J., Wunderlin, M., Wicki, K., et al. Multi-night acoustic stimulation is associated with better sleep, amyloid dynamics, and memory in older adults with cognitive impairment. GeroScience, 2024, 46: 6157–6172. Züst, M.A., Mikutta, C., Omlin, X., et al. The hierarchy of coupled sleep oscillations reverses with aging in humans. J. Neurosci., 2023,. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7085440","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484652000,"identity":"3c5e0dd0-c8a9-489e-85e7-b070acfeb0d2","order_by":0,"name":"Marina Wunderlin","email":"","orcid":"","institution":"University of Bern","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Wunderlin","suffix":""},{"id":484652002,"identity":"169f28c4-964d-49f0-8cca-45cde1444553","order_by":1,"name":"Korian Wicki","email":"","orcid":"","institution":"University of Bern","correspondingAuthor":false,"prefix":"","firstName":"Korian","middleName":"","lastName":"Wicki","suffix":""},{"id":484652003,"identity":"5c76bf30-a2cb-42ac-b569-d22d097468c3","order_by":2,"name":"Charlotte Elisabeth Teunissen","email":"","orcid":"","institution":"Amsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Charlotte","middleName":"Elisabeth","lastName":"Teunissen","suffix":""},{"id":484652005,"identity":"4666abdb-6597-4914-982f-ae99bc60fc4d","order_by":3,"name":"Marc Alain Züst","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3PsQrCMBCA4QuBugTnSH0FIUVQCkpfpS66VHByclAC7VKc9W2Ugl2qrh0ELUJdA4I4idHJKdHNIf+QkOHjcgAm098nAAiTN/4WoNniZ8KJPLWkEW0LARPwWBpdzt3wUG9Xpug6UpBWNmhS2ABmWebwYVgSN15he6Eiq74FYIHF8gBJkhCW+xYmKrIvJXnIrY+XgrsvcjxpSC6noBAoy8Hh6D0FdKTEtDenrJYFzjLeJXKXHrfVH+sjIW4dr5qmJ3EfJ167kqyvKvLOB/r5RFMdMJlMJpOmJ5OKRhgOVrEZAAAAAElFTkSuQmCC","orcid":"","institution":"University of Bern","correspondingAuthor":true,"prefix":"","firstName":"Marc","middleName":"Alain","lastName":"Züst","suffix":""}],"badges":[],"createdAt":"2025-07-09 15:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7085440/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7085440/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86665717,"identity":"f7942fb6-92f9-4777-8d48-e7e678439bdf","added_by":"auto","created_at":"2025-07-14 11:06:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003estudy design. The full study design entailed a total of five nights in the sleep laboratory. All participants were subjected to an adaptation night to get acclimated to the sleep laboratory and to be screened for potential sleep disorders. All participants further completed a baseline night, where sleep was assessed without any intervention. During the baseline night, an algorithm detected SW peaks and markers were set time-locked to the peaks, however, no actual sounds were transmitted to the sleeping person (=sham PLAS). Following the baseline night, three consecutive nights of real-PLAS—with the goal to boost SWA—ensued. During the three experimental nights (E1-E3), acoustic stimuli were transmitted to the sleeping person, time-locked to the detection of a SW peak. Blood samples to assess plasma Aβ 42/40 ratios were drawn in the morning after the baseline night, as well as in the morning after the last experimental night (night E3). Baseline episodic memory performance was assessed before the baseline night. Baseline analyses were confined to the baseline night; PLAS-protocol analyses focused on relative changes during the three experimental nights compared to that baseline.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7085440/v1/64bf02516a1ad02b000e13fd.png"},{"id":86667105,"identity":"9408c6a7-3bf3-46c4-9b7e-6a7a03f50ad5","added_by":"auto","created_at":"2025-07-14 11:14:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIllustration of slow waves, spindles and their coupling. A. For the detection of slow waves and spindles, established duration and amplitude criteria were applied after the original data was filtered in the slow wave and spindle frequency range. Coupling relates to the phase of the slow wave at the spindle’s maximum (blue). B. Coupling strength is measured using the resultant vector length (red). For this the phase distribution of all spindles is plotted and the average preferred phase is calculated (dark blue). The resultant vector length indicates the amount of circular spread. A short resultant vector (low coupling strength) results from widely spread spindle phases, while a long vector (high coupling strength) indicates tightly clustered phases. C. Coupling directionality is measured using the phase slope index, which measures the consistency of phase lag or lead between the spindle and slow wave signal. A phase slope index significantly different from 0 suggests the leading signal drives the lagging signal. A positive phase slope index indicates that slow waves drive spindles, while a negative phase slope index indicates that spindles drive slow waves\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7085440/v1/49d938c183c1156df43846e8.png"},{"id":86667106,"identity":"3f2d9ee1-1865-447c-93cf-ed8e0759e5f3","added_by":"auto","created_at":"2025-07-14 11:14:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":215475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAssociation between Aβ and SW–spindle coupling at baseline. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e A stepwise self-optimized regression approach showed that Aβ 42/40 ratio is best explained by coupling strength (measured via resultant vector length (RVL)) and coupling directionality (measured via phase slope index (PSI)), as represented by black solid elements and green arrows. Other factors that were offered for consideration, but not included in the model, are shown as gray rectangles. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e An additional model incorporating both the 42 and 40 peptides individually revealed that effects were mainly carried by the Aβ 42 peptide, which is best explained by coupling strength and coupling directionality. For the Aβ 40 peptide, there was only a trend-level effect for the association with coupling strength, and no association with coupling directionality. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC./D.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Scatter plots showing simple regression plots between Aβ 42/40 ratio (left panels), Aβ 42 (middle panels), Aβ 40 (right panels) and coupling strength, as measured via RVL (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) or coupling directionality, as measured via PSI (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eD.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e). For illustrative purposes, the plots in \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eD.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e are shown for all participants. The reported t and p values correspond to the values of the individual regressors within the full model under \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Note that the full models reflect conservative models, where outlier subjects were excluded from the analysis. For both Aβ 42/40 ratio and Aβ 42, effects remained stable even when all outliers were included. However, for Aβ 40, results were inconsistent with results depending on model choice. Legend:\u0026nbsp; \u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e p \u0026lt; 0.001, \u003c/em\u003e\u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e p \u0026lt; 0.01, \u003c/em\u003e\u003csup\u003e\u003cem\u003e(*)\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e p = 0.07, n.s. not significant.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7085440/v1/b1d9e36e6a0b9b89e9cf5927.png"},{"id":86665719,"identity":"a34928ac-3e95-43b7-bdf1-c193bfc8909d","added_by":"auto","created_at":"2025-07-14 11:06:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":345665,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRelationship between PLAS-induced improvements in sleep parameters and changes in Aβ 42/40 ratio. In each plot, the x-axis represents the change in Aβ 42/40 ratio from pre-intervention to post-intervention, where higher values are indicative of a more favorable change. The y-axis depicts the PLAS-induced increase in \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eSW-amplitude and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eSW–spindle coupling strength, as measured via a single trial level approximation to the resultant vector length, relative to the baseline night without stimulation. A higher value reflects a beneficial shift in sleep parameters. The left panels show mean values per night for all participants along with their regression fit, while the right panels separately depict means and fit for healthy (MOCA score ≥ 26) and cognitively impaired (MOCA score \u0026lt;26) participants. While the PLAS-induced increase in SW amplitude is associated with more beneficial changes in Aβ 42/40 ratio in all three nights and irrespective of cognitive health, effects for PLAS-induced increases in SW–spindle coupling strength are evident only in the cognitively impaired group. Note that means and regression fits are presented for illustrative purposes only; the statistical model (LMM, see table 3) is based on the single trial level of all detected SWs and spindle-matched SWs, respectively. Legend:\u0026nbsp; \u003c/em\u003e\u003csup\u003e\u003cem\u003e***\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e p \u0026lt; 0.001, \u003c/em\u003e\u003csup\u003e\u003cem\u003e**\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e p \u0026lt; 0.01, \u003c/em\u003e\u003csup\u003e\u003cem\u003e(*)\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e p = 0.058, n.s. not significant.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7085440/v1/2e0f95f364e9af610f592c99.png"},{"id":88358879,"identity":"ccea6283-4803-4793-ab39-cdf58b307655","added_by":"auto","created_at":"2025-08-05 15:47:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1905950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7085440/v1/d916a0b3-3130-4cd7-9ec2-8d919230c073.pdf"},{"id":86665715,"identity":"52064bfe-8629-4653-8f65-56ae7703c02d","added_by":"auto","created_at":"2025-07-14 11:06:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":31737,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7085440/v1/4dc71980cf32f7985a9c2439.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Slow wave–spindle coupling during deep sleep is selectively linked to Plasma Amyloid-β levels in Older Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCognitive decline and dementia are becoming increasingly relevant issues due to our aging population. Amyloid-beta (Aβ)—a prominent pathophysiological marker of Alzheimer's disease (Hardy and Selkoe, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)—starts accumulating up to 15 years before symptoms appear, with corresponding alterations in brain function becoming evident as early as 10 years prior to symptom onset (Beason-Held et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Palmqvist et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hence, understanding the underlying trajectories and influences that contribute to the accumulation of Aβ is vital for targeted interventions and dementia prevention. Additionally, investigating whether such targeted interventions can yield favorable outcomes for Aβ is essential.\u003c/p\u003e\u003cp\u003eA growing body of research has linked disturbed sleep to Aβ. For instance, in healthy older adults, shorter self-reported sleep duration and poorer sleep quality were associated with greater cortical Aβ-burden (Spira et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, total sleep deprivation studies have shown to negatively affect Aβ-levels in both CSF (Ooms et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and the brain (Shokri-Kojori et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Specifically, disturbed slow wave sleep (SWS), the deepest sleep stage, has been shown to interfere with Aβ-levels (Ju et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Impairments in slow wave activity (SWA)—the underlying frequency during SWS—have been associated with adverse implications for cortical Aβ-burden (Mander et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and CSF Aβ-levels (Varga et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). One study showed that disturbed SWA can forecast the rate of cortical Aβ accumulation across subsequent years, even when correcting for age, sex or sleep apnea (Winer et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sleep, and more specifically SWA, is thought to impact Aβ through its metabolic clearance function, a process that washes out metabolic waste products, such as Aβ (Fultz et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecently, blood-based assessments of Aβ have emerged as a cost-effective and minimally invasive method to monitor Aβ deposition in the brain (Thijssen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Verberk et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly to CSF- and cortical Aβ, plasma markers of Aβ have been shown to be unfavorably affected by total sleep deprivation (Eide et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with SWA specifically identified as a critical factor predicting Aβ-levels (Rosenblum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe consistent evidence linking SWA to Aβ invites delving into the more specific phenomena within SWA and investigate how they may relate to Aβ. A distinctive electrophysiological process occurring during SWA is the coupling of slow waves (SWs) and sleep spindles (SW–spindle coupling). It is well established that SW–spindle coupling plays a crucial role in memory functioning (Rasch and Born, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The hierarchical orchestration of neocortical SWs (\u0026lt; 1.25Hz), thalamocortical spindles (12–16Hz) and hippocampal ripples (80–250Hz) enables a temporally precise coordination between cortical and hippocampal networks (Buzsáki, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rasch and Born, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Staresina et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These electrophysiological events are organized in a phase-amplitude coupling hierarchy, where ripples are nested within spindle troughs, and spindles are further nested within SW peaks. The resulting synchronization allows memory traces to be selectively reactivated during sleep and gradually integrated into long-term cortical networks—a key component of system level memory consolidation (Rasch and Born, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResearch suggests that in the aging brain, spindles become uncoupled from SWs (Helfrich et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muehlroth et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a process occurring gradually across the human life span (McConnell et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Züst et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Whereas there is a clear cross-frequency directionality of SWs driving spindles in younger individuals (i.e., spindles following the SW peak), in older individuals, a disruption of this hierarchical temporal structure occurs, characterized by the spindle shifting to precede the SW peak (Helfrich et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Züst et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Importantly, this shift is associated with both decreased memory function and atrophy in the medial frontal cortex (Helfrich et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muehlroth et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Regarding biomarkers of neurodegeneration, only few studies have investigated its relationship with SW–spindle coupling in healthy older adults. Some reports showed that SW–spindle coupling was associated with tau burden (Winer et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or plasma glial fibrillary acidic protein concentration (Züst et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Only two studies indicated that detrimental SW–spindle coupling was associated with cortical Aβ-burden (Chylinski et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or unfavorable plasma Aβ-levels (Wunderlin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), respectively. An explanation for the sparsity of results may be that in healthy older adults, Aβ-levels are not elevated enough to be linked to microstructural features like SW–spindle coupling (Winer et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Züst et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith both SWA and its microstructural features (like SW–spindle coupling) having been linked to Aβ, the question arises whether SWA alone might merely represent a proxy for something more specific occurring during SWA, like SW–spindle coupling. Hence, our first aim was to explore whether SWA or SW–spindle coupling serves as predictor for Aβ. Using a sample of older adults ranging from cognitively healthy to -impaired allowed us to investigate whether predictive power of coupling on Aβ-dynamics depends on the level of cognitive functioning. Additionally, we examined whether SWA or SW–spindle coupling could—in terms of their predictive value—outperform other variables known to be linked to Aβ—such as cognitive functioning, age or sleep quality (Ju et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rodrigue et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the second part of this paper, we aim to re-explore whether phase-locked acoustic stimulation (PLAS)—a targeted intervention previously shown to enhance both SWA and SW–spindle coupling—might be associated with beneficial changes in Aβ. PLAS operates by presenting short acoustic stimuli during SWS, time-locked to the rising up-phase of a SW. A growing body of evidence shows that PLAS is associated with increased SWA and SW–spindle coupling—both in young (Leminen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ngo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ong et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wunderlin et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and older adults (Papalambros et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wunderlin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wunderlin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zeller et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Importantly, stronger PLAS-induced increases in SWA have been associated with more beneficial changes in Aβ across a three-night intervention, an effect that was more pronounced in cognitively impaired vs. healthy older adults (Wunderlin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zeller et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Here, we revisit these effects, specifically focusing on the previously unexplored question of whether improvements in SW–spindle coupling are also linked to a beneficial Aβ-response and whether this depends on the level of cognitive functioning level.\u003c/p\u003e\u003cp\u003eOur results imply a unique predictive strength of SW–spindle coupling for baseline Aβ-levels, irrespective of cognitive functioning levels. Moreover, our results suggest that PLAS-induced increases in SWA benefit Aβ-response regardless of cognitive functioning levels, whereas PLAS-induced increases in SW–spindle coupling benefit Aβ-response exclusively in the cognitively impaired group. This hints towards a connection between SW–spindle coupling and Aβ-levels that goes beyond SWA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSample\u003c/p\u003e\u003cp\u003eThe baseline sample consisted of 47 older adults (mean age: 70.5 years, SE: 0.68, range: 59–80 years; 29 females, 18 males) with a range of cognitive functioning as measured via the Montreal Cognitive Assessment (MOCA, (Nasreddine et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); mean MOCA score: 26.2, SE: 0.40, range: 20–30). For the purpose of this study, MOCA scores below 26 indicate cognitive impairment (n = 17, mean MOCA score: 23.2, SE: 0.37), while scores from 26 to 30 (n = 30, mean MOCA score: 27.9, SE: 0.27) indicate cognitive health (Davis et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A subset of the baseline sample of 39 older adults (mean age: 70.5 years, SE: 0.74, range: 59–79 years; 23 females, 16 males) completed a three-night PLAS protocol (see design \u0026amp; procedure section). The PLAS protocol sample ranged from cognitively impaired (n = 16, mean MOCA score: 23.2, SE: 0.39) to cognitively healthy (n = 23; mean MOCA score: 27.8, SE: 0.30; total sample mean MOCA score: 25.9, SE: 0.44, range: 20–30). The remaining eight participants from the baseline sample were not included in the PLAS protocol sample due to following a different protocol (n = 4) or due to unavailability of post-measurement blood samples (n = 4). See Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the characteristics of both samples.\u003c/p\u003e\u003cp\u003eAll participants underwent an extensive screening procedure to ensure that inclusion criteria were met. Exclusion criteria were sleep disorders and irregular sleep patterns as assessed via the Pittsburg Sleep Quality Index (PSQI, (Buysse et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e)) and via a screening night in the sleep laboratory to identify sleep apnea or restless leg syndrome, impaired hearing, non-fluency in German, current or previous neurological or psychiatric conditions, substance abuse and use of medication acting on the CNS. The study was conducted in accordance with the Declaration of Helsinki and approved by the local ethics committee of the canton of Bern, Switzerland. Written informed consent was obtained from all study participants. The current analyses represent an exploratory re-analysis of a preregistered study available under ClinicalTrials.gov (NCT04277104).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of study participants. Means and standard errors are provided for both the baseline sample (= one night of natural sleep without intervention), as well as the PLAS protocol sample (= subset of the baseline sample with an additional three consecutive nights of phase-locked acoustic stimulation, see design and procedures). \u003csup\u003e1)\u003c/sup\u003ecategorical variable to determine the highest academic qualification, ranging from 1 (primary school) to 4 (University). \u003csup\u003e2)\u003c/sup\u003eMontreal Cognitive Assessment, maximum score: 30, ≥ 26 cognitively healthy. \u003csup\u003e3)\u003c/sup\u003ePittsburgh Sleep Quality Index, \u0026lt; 5 normal sleep. \u003csup\u003e4)\u003c/sup\u003eApnea-Hypopnea Index \u0026lt; 5: normal sleep; 5 ≤ AHI \u0026lt; 15: mild sleep apnea. \u003csup\u003e5)\u003c/sup\u003ePlasma amyloid-beta 42/40 ratio, lower scores are indicative of a higher risk for dementia.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBaseline Sample (n = 47)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eSubset: PLAS protocol Sample (n = 39)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ese\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e29 f, 18 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e23 f, 16 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOCA\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSQI\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAHI\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e8.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline plasma Aβ42/Aβ40\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline plasma Aβ42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e6.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline plasma Aβ40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e98.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e97.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eDesign \u0026amp; Procedure\u003c/p\u003e\u003cp\u003eThe full study design contained five nights in the sleep laboratory: an adaptation night, a baseline night (with sham-PLAS), and three experimental nights (with real-PLAS; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The adaptation night served as an acclimatization to sleeping under laboratory conditions and as a screening night to screen for sleep-based exclusion criteria. After a recovery night at home, participants returned to the sleep laboratory for a baseline night. During the baseline night, sham-PLAS was administered, where a PLAS algorithm detected SW peaks in the online EEG signal and set time markers of these detections, but did not transmit any sound to the sleeping participant. After the baseline night, three consecutive experimental nights ensued, containing real-PLAS where upon detection of a SW peak, an acoustic stimulus (50ms of pink noise) was transmitted to the sleeping participant. Individual hearing thresholds were determined through hearing tests, and used as target stimulus intensity during PLAS. Acoustic stimuli were transmitted via sleepphones® (AcousticSheep LLC) at a mean volume of 69.2 dB(A). The PLAS algorithm is described elsewhere in more detail (Ruch et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wunderlin et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and its application has previously been documented (Wunderlin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wunderlin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zeller et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In brief, this algorithm analyzes the most recent 120 ms of data and computes correlations between the empirical topographical voltage distribution and a template topography of a typical slow wave peak. If the correlation is rising in \u0026gt; 75% of samples, a SW peak is predicted, and a stimulation is triggered. Hence, the algorithm is not dependent on the absolute amplitude of the signal—a metric that decreases with age (Colrain et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMeasures of cognitive functioning, including episodic memory performance, were assessed throughout the intervention. We previously demonstrated in these data that PLAS increases SWA, SW–spindle coupling and episodic memory performance, and is associated with beneficial Aβ-responses. For detailed descriptions of the tasks and results, see (Wunderlin et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wunderlin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zeller et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Here, we were selectively interested in a baseline measure of episodic memory performance as a potential explanatory variable in our statistical models (see statistical analysis section). Baseline episodic memory performance was assessed via a Face-Occupation Association (FOA) task, where participants encoded 40 faces that were each associated with one of 20 occupations (e.g. doctor or gardener). Baseline performance entailed the number of correctly recalled associations (0–40) after two encoding blocks.\u003c/p\u003e\u003cp\u003eSleep was recorded using a high-density EEG system (128-channel MicroCel Geodesic Sensor Net, Physio16 input box, 400 Series Geodesic EEG System, Magstim EGI, Eugene, OR, USA), at a sampling rate of 500 Hz, referenced to Cz. Polysomnographic scoring of sleep stages was performed according to the criteria of the American Academy of Sleep Medicine (Iber, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) by an experienced and certified somnologist. See Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for participant’s baseline sleep architecture.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSleep Architecture. Means and standard errors for polysomnographic variables are displayed. \u003csup\u003e1\u003c/sup\u003eTotal Sleep Time in minutes. \u003csup\u003e2\u003c/sup\u003ePercentages of non-rapid eye movement (NREM) sleep stages N1-N3 and rapid eye movement (REM) sleep. \u003csup\u003e3\u003c/sup\u003eMinutes spent awake. \u003csup\u003e4\u003c/sup\u003eWake time after sleep onset in minutes. \u003csup\u003e5\u003c/sup\u003eSleep onset latency, i.e. the minutes in bed until sleep onset. \u003csup\u003e6\u003c/sup\u003eSleep efficiency, i.e. the percentage of minutes spent asleep in relation to minutes lying in bed.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBaseline Sample (n = 47)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003ePLAS protocol Sample (n = 39)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003emean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ese\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTST\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e334.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e9.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e342.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e9.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% N1\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e30.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% N2\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e47.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e47.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% N3\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% REM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWake\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e136.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e140.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWASO\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e121.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e124.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSL\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e16.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSE\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e71.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e71.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eBlood samples were drawn after the baseline night and after the last experimental night (E3, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The samples were taken in the morning approximately an hour after waking and immediately centrifuged and stored in a -80°C freezer. Plasma samples were sent to the Neurochemistry Laboratory, Amsterdam University Medical Center (Amsterdam, The Netherlands), where Plasma Aβ 1–42 and 1–40 peptides were identified by means of single molecule array immunoassays (IA-N4PE; (Thijssen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)). Aβ 1–42/1–40 ratios were calculated to account for interindividual and preanalytical variability. Lower Aβ 42/40 ratios indicate a greater risk for amyloid deposition (Graff-Radford et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Verberk et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEEG processing\u003c/p\u003e\u003cp\u003eEEG processing was performed in MATLAB R2022b (MathWorks) using the toolboxes EEGLAB (Delorme and Makeig, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), FieldTrip (Oostenveld et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and CircStat (Berens, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), as well as the phase-amplitude coupling analysis framework by Jiang et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The data was down-sampled to 200Hz and preprocessed via the PREP pipeline for EEGLAB (Bigdely-Shamlo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and FieldTrip’s automatic artifact rejection pipeline. All analyses were performed on sleep containing slow wave activity (N2/N3 sleep) only.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiscrete SW, spindle and coupling event detection\u003c/b\u003e. The procedure for the detection of SWs, spindles and coupling events followed previously published work (Helfrich et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mölle et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Staresina et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). All events were detected in channel Fz. For the detection of SWs, all zero-crossings within the SW-band (0.16–1.25 Hz) filtered data were marked. SW peaks and SW troughs were defined as the highest and lowest values between two successive positive-to-negative zero-crossings, provided they met a duration (0.8–2 s apart) and amplitude (75th percentile) criterion (Mölle et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For the detection of spindles, the signal was first filtered between 12 and 16 Hz. Next, the instantaneous amplitude (envelope) of the filtered signal was calculated using a Hilbert transform and smoothed using a 200ms moving average. Data segments exceeding the 75th amplitude percentile for a duration of 0.5–3s were defined as spindles at their maximum value (Staresina et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Finally, SW–spindle coupling events were defined as spindles within a range of ± 2.5 s time-locked to the SW trough, as previously described (Helfrich et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For a graphical illustration of slow waves, spindles and their coupling, see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSW–spindle coupling.\u003c/b\u003e In addition to the quantity of SW–spindle coupling events, two measures capturing the quality, i.e. the exact synchronization of SWs and spindles were calculated:\u003c/p\u003e\u003cp\u003e\u003cb\u003eSW–spindle coupling strength\u003c/b\u003e: coupling strength was measured via the resultant vector length (RVL) (Berens, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The RVL quantifies how consistently spindles are coupled with SWs (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A longer RVL suggests that most spindles are closely clustered around their mean SW phase, indicating stronger coupling. A shorter RVL is indicative of greater variance of spindles within the SW phase, which suggests weaker coupling. To calculate RVL, the data from channel Fz was first filtered within the SW-band (0.16–1.25 Hz). Next, the instantaneous phase angle of the filtered signal was calculated using a Hilbert transform. For the above-defined SW–spindle coupling events, the individual phase angles were extracted and used to calculate the mean RVL using CircStat’s circ_r function.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSW–spindle coupling directionality\u003c/b\u003e: coupling directionality was measured using the Phase Slope Index (PSI) (Jiang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Here, PSI was calculated between the phase of the SW frequency (0.5–2 Hz; in steps of 0.5 Hz) and the amplitude of the spindle frequency (12–16 Hz; in steps of 1 Hz). The PSI provides a measure of lag/lead between two frequencies, where 0 suggests no consistent directional influence of one over the other frequency. A positive value suggests a forward interaction, indicative of the SW leading the spindle, and a negative value suggests a reverse influence with the spindle leading the SW (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The former (a positive value) is indicative of a coupling hierarchy toward a “younger” and less atrophic brain (Helfrich et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Züst et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo calculate PSI, the EEG data in Fz was epoched ± 2.5s around the previously detected SW troughs. Frequency power was estimated analyzing five cycles of each frequency within a sliding window of 2s, moving in steps of 1 second. Finally, for each participant, average PSI values were calculated over the frequency subbands.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003e\u003cb\u003eBaseline Analyses.\u003c/b\u003e To analyze which variables best explain Aβ 42/40 ratio, an optimized regression model was calculated. Although the main dependent variable of interest was Aβ 42/40, all analyses were additionally repeated for the single peptides Aβ 42 and 40. As a starting point, an a priori regression model was defined: Aβ variable ~ MOCA score + number of coupled spindles + SW amplitude + age + coupling strength + coupling directionality. Next, a stepwise regression (max. steps: 10) was performed using R-squared as the optimization criterion to iteratively refine the model. In this process, other potential predictors (spindle amplitude, percentage of sleep stages N1, N2, N3, REM, total sleep time, sleep efficiency, baseline memory performance and sex) were considered. At each step, the model examined the effect of adding a new predictor or removing a predictor already in the model on the overall R-squared. A predictor was added to the model if its inclusion increased R-squared by at least 0.1, and an existing predictor was removed if its exclusion reduced R-squared by no more than 0.05.\u003c/p\u003e\u003cp\u003eTo strengthen the robustness of our results, the optimized regression models were calculated both conservatively (excluding potential outliers as determined via Cook’s distances \u0026gt; 4/n (Belsley et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)) and liberally (including all data points). Additional analyses to ensure the robustness of our results entailed recalculating all models without a specific a priori model. An advantage of not using an a priori model is to reduce potential bias stemming from the pre-selection of predictors. If all procedures (liberal, conservative, informed (with a priori model), and empty (without a priori model)) yielded similar results, we considered the results to be robust.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePLAS protocol Analyses.\u003c/b\u003e In addition to the baseline analysis, we investigated relative changes during the three experimental nights (where PLAS was applied) compared to the baseline night without stimulation (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We will refer to these relative changes as PLAS-induced changes. To analyze how PLAS-induced changes in sleep markers of interest (SW amplitude and SW–spindle coupling) interact with changes in Aβ 42/40 ratios from pre to post intervention, linear mixed effects models (LMM) were calculated. SW amplitude and SW–spindle coupling were used as sleep markers of interest because (A) they are specific markers that PLAS has been shown to increase (Ngo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wunderlin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and (B) they are linked to Aβ, as demonstrated by previous research (Chylinski et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mander et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Varga et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) as well as our baseline analyses.\u003c/p\u003e\u003cp\u003eSW amplitude and SW–spindle coupling were predicted on the single trial level. Regarding SW amplitude, this meant that each detected SW event within a night was treated as a separate trial, with each event containing an amplitude value. Regarding SW–spindle coupling, each SW–spindle coupling event within a night served as a separate trial. Here, we first calculated the mean phase angle per night using CircStat’s circ_mean function. Next, the absolute distance of each event from the individual mean phase angle was calculated and divided by 180 (the maximum value), to get the relative distance of each event. The relative distance was then subtracted from 1 to provide a “closeness” estimate instead of a distance estimate per trial, where 1 means maximum “closeness” and 0 minimum “closeness”. Hence, higher values indicate that the spindle is closely aligned with the individual average angle for the night, while lower values indicate that the spindle is further from the individual average angle. This measure can be considered an approximation of the RVL, hence coupling strength, at the single trial level.\u003c/p\u003e\u003cp\u003eWe further explored an approximation to the PSI (as a measure for coupling directionality) on the single trial level, by calculating each spindle’s phase angle difference from zero. 0° can be seen as the reversal-point of cross-frequency directionality, with numbers below zero indicating that the spindle lies before the SW peak and numbers above 0 indicating that the spindle lies after the SW peak. The distances both below and above 0 in degrees were rescaled and mapped from their minimum to maximum values onto a range from − 1 to 1, with zero corresponding to 0°. Note that this approach does not measure precisely the same aspect as PSI. While a consistent phase angle difference indicates temporal precedence, the PSI additionally reflects a directional influence by quantifying the consistency of the phase difference as a function of frequency, which our surrogate metric cannot evaluate on single-trial level. Nonetheless, we consider the measure described to be a conceptually close approximation, albeit with some inherent limitations.\u003c/p\u003e\u003cp\u003eThree separate LMMs were calculated for the prediction of SW amplitude, SW–spindle coupling strength, and coupling directionality. The model included the categorical variable \u003cem\u003enights\u003c/em\u003e (BL, E1, E2, E3, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the change score in Aβ 42/40 from pre to post intervention, as well as their interaction as fixed effects. We controlled for age and MOCA score and included a random intercept for each participant. Maximum likelihood estimation was used to fit the model. Note that the factor \u003cem\u003enight\u003c/em\u003e is coded with the first night (pre-intervention, baseline) as reference. Effects on SW amplitude and SW–spindle coupling can therefore be interpreted as contrasts against the baseline night, hence PLAS-induced changes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSlow wave\u0026ndash;spindle coupling as best predictor for plasma amyloid-beta\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine what variables best explain differences in Aβ-values at baseline, optimized regression analyses were conducted. The conservative optimized regression analysis showed that the model best explaining differences in Aβ 42/40 was the model incorporating both coupling strength (β\u0026thinsp;=\u0026thinsp;0.052, t(40)\u0026thinsp;=\u0026thinsp;4.07, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e) and coupling directionality (β\u0026thinsp;=\u0026thinsp;1.373, t(40)\u0026thinsp;=\u0026thinsp;3.74, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), but none of the other potential predictors (F(2, 40)\u0026thinsp;=\u0026thinsp;13.7, adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.38, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e, see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The same relationship was shown in the more liberal model, where potential outliers were not removed (F(2, 44)\u0026thinsp;=\u0026thinsp;7.43, adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.22, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e; coupling strength: β\u0026thinsp;=\u0026thinsp;0.036, t(44)\u0026thinsp;=\u0026thinsp;3.03, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.004\u003c/em\u003e; coupling directionality: β\u0026thinsp;=\u0026thinsp;1.045, t(44)\u0026thinsp;=\u0026thinsp;2.69, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.01\u003c/em\u003e). For both the initial and conservative model, results did not change when using an empty (without a priori) model as baseline for the optimized regression instead of the informed (with a priori) model. For a visualization of the relationship between Aβ 42/40 and coupling parameters, see the left panels in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and D.\u003c/p\u003e\u003cp\u003ePredicting the Aβ 42 and 40 peptides individually revealed that effects were mainly carried by the Aβ 42 peptide (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Coupling strength and coupling directionality were consistently the best predictors for Aβ 42, both in the conservative (F(2, 42)\u0026thinsp;=\u0026thinsp;9.81, adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e; coupling strength: β\u0026thinsp;=\u0026thinsp;6.078, t(42)\u0026thinsp;=\u0026thinsp;3.13, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.003\u003c/em\u003e; coupling directionality: β\u0026thinsp;=\u0026thinsp;192.853, t(42)\u0026thinsp;=\u0026thinsp;3.36, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e) and more liberal models (F(2, 44)\u0026thinsp;=\u0026thinsp;8.58, adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e; coupling strength: β\u0026thinsp;=\u0026thinsp;5.719, t(44)\u0026thinsp;=\u0026thinsp;3.24, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.002\u003c/em\u003e; coupling directionality: β\u0026thinsp;=\u0026thinsp;166.09, t(44)\u0026thinsp;=\u0026thinsp;2.91, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.006\u003c/em\u003e) and results did not change with an empty model as opposed to the informed model. See the middle panels in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and D for a visualization of the relationship between Aβ 42 and coupling parameters.\u003c/p\u003e\u003cp\u003eFor the Aβ 40 peptide, results showed no consistent effect. In the conservative model, coupling strength predicted Aβ 40 at trend levels (β\u0026thinsp;=\u0026thinsp;36.484, t(43)\u0026thinsp;=\u0026thinsp;1.844, p\u0026thinsp;=\u0026thinsp;0.072; F(1, 43)\u0026thinsp;=\u0026thinsp;3.4, adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.05, p\u0026thinsp;=\u0026thinsp;0.072, see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), but this was not seen in the more liberal model, where MOCA score and SW amplitude were the best predictors (MOCA: β=-1.495, t(44)=-2.046, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.047\u003c/em\u003e; SW amplitude: β\u0026thinsp;=\u0026thinsp;0.81, t(44)\u0026thinsp;=\u0026thinsp;1.984, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.054;\u003c/em\u003e F(2, 44)\u0026thinsp;=\u0026thinsp;3.23, adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.09, p\u0026thinsp;=\u0026thinsp;0.049). Furthermore, when using an empty instead of an informed model, both conservative and liberal procedures revealed no significant effect of any variable. The relationship between Aβ 40 and coupling is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and D, right panels.\u003c/p\u003e\u003cp\u003eTo investigate whether effects differed depending on the level of cognitive functioning, the analyses were repeated, separately for cognitively healthy (n\u0026thinsp;=\u0026thinsp;30) and cognitively impaired (n\u0026thinsp;=\u0026thinsp;17) participants (see suppl. table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA for between-group characteristics). The only variable consistently appearing in all models (liberal, conservative, empty, informed) as a significant predictor for Aβ 42/40 and Aβ 42 was coupling strength for healthy older adults, and coupling directionality for cognitively impaired older adults (see supplementary material, table S2). For Aβ 40, no consistency across models was observed. These results suggest that, irrespective of cognitive functioning level, slow wave\u0026ndash;spindle coupling was the best predictor for Aβ 42/40 and Aβ 42. The results further suggest that distinct mechanisms of slow wave\u0026ndash;spindle coupling may be relevant at different levels of cognitive functioning.\u003c/p\u003e\u003cp\u003eIn sum, the results show that coupling strength and coupling directionality are better predictors for Aβ 42/40, and Aβ 42 than any other variable in the model, such as age, MOCA score, or SW amplitude\u0026mdash;all variables expected to be linked to Aβ (e.g. Rodrigue et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rosenblum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Specifically, more favorable Aβ-levels were linked to a more consistent clustering of spindles within the SW phase as opposed to a more chaotic placement (coupling strength). This was particularly seen in healthy older adults. Additionally, more beneficial Aβ-levels were linked to the SW leading the spindle as opposed to the spindle leading the SW (coupling directionality), which was particularly pronounced in the cognitively impaired group. Both these measures were associated with plasma Aβ in a way that resembles the following statement: The more the brain resembled a younger state from a coupling-physiology perspective, the more beneficial the plasma Aβ profile.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePLAS-induced gains in SW amplitude/ SW\u0026ndash;spindle coupling strength link to beneficial Aβ-response\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs our baseline analyses indicated a strong link between SW\u0026ndash;spindle coupling and Aβ, we analyzed in a next step whether Aβ-levels could profit from an intervention-induced increase in coupling quality. An additional variable of interest was SW amplitude, as previous reports showed that improvements in SW amplitude were linked to a beneficial Aβ-response (Wunderlin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zeller et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDifferent LMMs were conducted to examine PLAS effects on sleep parameters (SW amplitude and SW\u0026ndash;spindle coupling) across three experimental nights (E1, E2 and E3), as well as how these effects interact with changes in Aβ 42/40 ratio from pre to post intervention. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA summarizes the main results for the full model incorporating all participants. There were significant main effects for SW amplitude in all three experimental nights (E1: β\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;0.008; E2: β\u0026thinsp;=\u0026thinsp;0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; E3: β\u0026thinsp;=\u0026thinsp;0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, left panel) as well as SW\u0026ndash;spindle coupling strength (E1: β\u0026thinsp;=\u0026thinsp;0.012, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; E2: β\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;=\u0026thinsp;0.001; E3: β\u0026thinsp;=\u0026thinsp;0.007, p\u0026thinsp;=\u0026thinsp;0.021, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, right panel), but not SW\u0026ndash;spindle coupling directionality (see suppl. material table S3). These findings suggest an overall increase in SW amplitude and SW\u0026ndash;spindle coupling strength, but no enhancement in coupling directionality, during each of the three experimental nights in relation to the baseline night. In other words, PLAS increased SW amplitude and SW\u0026ndash;spindle coupling strength, but not coupling directionality. For the increase in SW amplitude but not coupling strength nor directionality, a trend-level effect for MOCA score was observed (β\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;=\u0026thinsp;0.058), indicating that higher cognitive functioning levels were associated with higher SW-amplitude but not increased SW\u0026ndash;spindle coupling quality.\u003c/p\u003e\u003cp\u003eNotably, highly significant interaction effects were found between Aβ 42/40 ratio change from pre to post intervention and the increase in SW amplitude during all three nights (E1 x Aβ 42/40 change: β\u0026thinsp;=\u0026thinsp;166.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; E2 x Aβ 42/40 change: β\u0026thinsp;=\u0026thinsp;87.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; E3 x Aβ 42/40 change: β\u0026thinsp;=\u0026thinsp;150.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, see Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, left panel). These results indicate that PLAS-induced increases in SW amplitude were strongly and consistently associated with a beneficial Aβ-response across the intervention. Regarding SW\u0026ndash;spindle coupling strength, only the third experimental night showed a significant interaction with Aβ 42/40 change (E3 x Aβ 42/40 change: β\u0026thinsp;=\u0026thinsp;2.5, p\u0026thinsp;=\u0026thinsp;0.006, see Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, right panel), indicating that increases in SW\u0026ndash;spindle coupling strength in the last experimental night are linked to a beneficial Aβ-response. The left panels in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB visualize these associations, showing the relationship between Aβ 42/40 change scores and increases in SW amplitude and SW\u0026ndash;spindle coupling strength across the three nights, respectively. Removing one outlier subject identified by visual inspection (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, left panel) eliminated the previously significant interaction between Aβ change and increases in coupling strength in the third night. For coupling directionality, no significant interaction effects were observed (see suppl. material, table S3). To sum up, in the full sample, only increases in SW amplitude, but not SW\u0026ndash;spindle coupling strength or directionality seem to be associated with beneficial Aβ-response.\u003c/p\u003e\u003cp\u003eTo investigate whether effects were dependent on the level of cognitive functioning, we conducted separate analyses for cognitively impaired and healthy participants (see suppl. table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e B for between group characteristics). While interaction effects between increases in SW amplitude and Aβ 42/40 change were seen in both the cognitively impaired and the healthy subgroup (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, right panel and Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and C, left panels), this was not the case for the increase in SW\u0026ndash;spindle coupling strength. Here, in the cognitively healthy subgroup, no significant interaction effects were observed, regardless of whether the outlier participant was included or excluded (see Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, right panel and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, right panel). In the cognitively impaired subgroup however, there was a trend-level interaction effect for the first experimental night (E1 x Aβ 42/40 change: β\u0026thinsp;=\u0026thinsp;3.3, p\u0026thinsp;=\u0026thinsp;0.058), and significant interaction effects for the second and third night (E2 x Aβ 42/40 change: β\u0026thinsp;=\u0026thinsp;7.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; E3 x Aβ 42/40 change: β\u0026thinsp;=\u0026thinsp;4.6, p\u0026thinsp;=\u0026thinsp;0.008, see Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, right panel, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, right panel). For SW\u0026ndash;spindle coupling directionality, neither group showed significant interaction effects (see suppl. material, table S3).\u003c/p\u003e\u003cp\u003eOur post hoc analyses investigated whether effects were primarily driven by either the 42 or the 40 peptide. Positive interactions between changes in Aβ and changes in sleep parameters were only observed for the 42 peptide, although inconsistently (SW amplitude: p\u003csub\u003eE1\u003c/sub\u003e \u0026lt; 0.001; p\u003csub\u003eE2\u003c/sub\u003e \u0026gt;0.3; p\u003csub\u003eE3\u003c/sub\u003e \u0026lt; 0.001; coupling strength: p\u003csub\u003eE1\u003c/sub\u003e = 0.012; p\u003csub\u003eE2\u003c/sub\u003e \u0026gt;0.6; p\u003csub\u003eE3\u003c/sub\u003e \u0026gt;0.4), and not for the 40 peptide. This suggests a potential tendency for the observed effects to be somewhat more carried by the 42 peptide. However, due to the lack of consistency, our observed effect is more clearly attributable to the Aβ 42/40 ratio rather than to the 42 peptide alone.\u003c/p\u003e\u003cp\u003eTogether these results suggest that PLAS-induced increases in SW amplitude are associated with a beneficial Aβ-response regardless of cognitive functioning level. In contrast, PLAS-induced increases in SW\u0026ndash;spindle coupling strength appear to correlate with a beneficial Aβ-response only within the cognitively impaired group. Changes in coupling directionality were not associated with changes in Aβ, irrespective of cognitive functioning levels.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of linear mixed effect models (LMMs). The models tested whether PLAS-induced changes in SW amplitude and SW\u0026ndash;spindle coupling strength (as measured via an approximation to the resultant vector length on the single trial level) interact with changes in Aβ 42/40 ratios from pre to post intervention. \u003cb\u003eA.\u003c/b\u003e results for the full model containing all participants. \u003cb\u003eB.\u003c/b\u003e results for the cognitively healthy (MOCA score\u0026thinsp;\u0026ge;\u0026thinsp;26) subgroup. \u003cb\u003eC.\u003c/b\u003e results for the cognitively impaired (Moca Score\u0026thinsp;\u0026lt;\u0026thinsp;26) subgroup. The models both investigated increases in SW amplitude (left panels) and SW\u0026ndash;spindle coupling strength (right panels). Age and MOCA score were included as control variables, with each participant assigned a random intercept. In each model, interactions are highlighted in grey, with p-values for significant interaction and main effects (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) shown in bold.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eA. Full sample models\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eSW amplitude\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u003cp\u003eSW\u0026ndash;spindle coupling strength\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-11.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-71.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e250.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOCA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E1 : Aβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e166.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E2 : Aβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e87.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E3 : Aβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e150.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eB. Cognitively healthy sample models\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSW amplitude\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003eSW\u0026ndash;spindle coupling strength\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-25.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-104.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e311.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOCA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E1 : Aβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e161.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.754\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E2 : Aβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e76.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-1.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E3 : Aβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e62.656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC. Cognitively impaired sample models\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSW amplitude\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u003cp\u003e\u003cb\u003eSW\u0026ndash;spindle coupling strength\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-0.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-151.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e381.783\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-6.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-1.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMOCA score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E1 : Aβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e163.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E2 : Aβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e113.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNight E3 : Aβ change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e355.484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSleep, and in particular SWS has been consistently connected to Aβ-dynamics (Ju et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mander et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rosenblum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Varga et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Winer et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Less is known about the link between Aβ and specific microstructural aspects within SWS, such as the coupling of SWs and sleep spindles. Here we show that in older adults ranging in cognitive functioning levels, baseline SW\u0026ndash;spindle coupling strength (i.e., how consistent the coupling is) and SW\u0026ndash;spindle coupling directionality (i.e., whether the spindle or the SW leads) are the best predictors for plasma Aβ-dynamics. Specifically, more favorable Aβ-levels were best predicted by a more consistent clustering of spindles within the SW phase and a shift of the coupling hierarchy toward a \u0026ldquo;younger\u0026rdquo; status where the SW leads. We further explored how a three-night PLAS intervention\u0026mdash;a non-invasive tool known to boost both SWA and SW\u0026ndash;spindle coupling\u0026mdash;interacts with Aβ-response. Results showed that PLAS-induced increases in SW amplitude were associated with a more beneficial Aβ-response from pre- to post-intervention, irrespective of cognitive functioning levels. Interestingly, PLAS-induced increases in SW\u0026ndash;spindle coupling strength (but not -directionality) were only associated with a more favorable Aβ-response in the cognitively impaired, but not the healthy subgroup. This suggests that PLAS-induced increases in SW\u0026ndash;spindle coupling might be selectively linked to beneficial Aβ-response in cognitively impaired older adults, where these dynamics are arguably deteriorating and may thus exhibit more room for improvement.\u003c/p\u003e\u003cp\u003eSW\u0026ndash;spindle coupling measures were the best predictors for plasma Aβ 42/40 ratio and Aβ 42 cross-sectionally, better than any other sleep quality measure, such as SW and spindle amplitudes, sleep duration, sleep efficiency, or the percentage of different sleep stages. SW\u0026ndash;spindle coupling measures were also better predictors for Aβ 42/40 ratio and Aβ 42 than age, sex or cognitive functioning, as assessed via an episodic memory task and the MOCA score (Nasreddine et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This unique predictive value, specifically related to the more pathogenic 42 peptide (Findeis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), suggests that SW\u0026ndash;spindle coupling is a critical physiological process related to the early pathophysiology of Alzheimer\u0026rsquo;s disease.\u003c/p\u003e\u003cp\u003eOne study conducted in healthy older adults showed that precise SW\u0026ndash;spindle coupling was significantly associated with Aβ-burden over the medial prefrontal cortex (Chylinski et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This result was specific for SW\u0026ndash;spindle coupling: SWA was not associated with Aβ-burden, contrasting previous reports (Mander et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Like our results, this suggests an important and potentially specific role of SW\u0026ndash;spindle coupling beyond SWA in predicting Aβ-burden. SW\u0026ndash;spindle coupling is known to involve an interplay of neocortical SWs, thalamocortical spindles and hippocampal ripples, facilitating efficient information transfer across widespread brain regions and playing a critical role in memory consolidation (Diekelmann and Born, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rasch and Born, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Staresina et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In aging, spindles become uncoupled from SWs, a change that is linked to both brain atrophy and decreased memory functions (Helfrich et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muehlroth et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Arguably, early unfavorable Aβ-dynamics might specifically disturb the thalamocortical interplay driving the co-occurrence of spindles and SWs before SWA itself is affected. While SWA may remain intact, the finer, micro-oscillatory dynamics like SW\u0026ndash;spindle coupling might already be disturbed. However, the causal direction of this relationship is not resolved.\u003c/p\u003e\u003cp\u003eSW\u0026ndash;spindle coupling might also have a specific function in driving Aβ-dynamics. SWA has been shown to be involved in glymphatic clearance, where neurotoxins such as Aβ are washed out of the brain (Fultz et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Well-functioning coupling-dynamics might therefore be a sign of a highly functioning glymphatic clearance system. Arguably, SW\u0026ndash;spindle coupling may serve a similar or at least supporting function in glymphatic clearance as has been shown for SWA (Fultz et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To answer this question, however, more research is needed, specifically using animal models or human intracranial recordings allowing for more direct physiological measurements and manipulations.\u003c/p\u003e\u003cp\u003eOur results showed that both coupling strength and coupling directionality were important predictors of Aβ. When separately analyzing participants categorized based on their cognitive functioning, we found that in cognitively impaired older adults, Aβ was specifically associated with coupling directionality whereas in cognitively healthy older adults, Aβ was specifically linked to coupling strength. These different SW\u0026ndash;spindle coupling metrics may reflect different levels of functional priority. Coupling strength indicates the circular spread of spindles across the SW, i.e. how tightly spindles are clustered around their preferred phase. Coupling directionality, on the other hand, measures a more fine-grained interaction between the two oscillatory signals, reflecting the consistency of phase lag or lead, i.e., \u003cem\u003ewhere\u003c/em\u003e on the SW the spindle occurs. Using an orchestra analogy, coupling strength could represent the alignment of musicians, such as how precisely they synchronize with each other in terms of timing and expression. Coupling directionality might represent how closely the musicians follow the conductor, ensuring the overall coordination of the piece. If the musicians fail to follow the conductor, the performance can fall apart completely\u0026mdash;they might even play different sections or fall out of sync entirely. However, if the musicians\u0026rsquo; alignment with each other is imperfect, the performance can still work\u0026mdash;albeit with some declines in quality.\u003c/p\u003e\u003cp\u003eIn line with the orchestra analogy, we propose that coupling directionality may represent the necessary, foundational basis for optimized functionality (\u0026ldquo;following the conductor\u0026rdquo;), whereas coupling strength might rather serve a secondary, facilitating mechanism (\u0026ldquo;playing in synchronization\u0026rdquo;). Hence, our results suggest that in cognitively impaired older adults, more detrimental Aβ-levels are associated with the breakdown of the hierarchy of SWs and spindles, which may be so fundamental that it leaves no room for the clustering of SWs and spindles to be of predictive value. In healthy older adults, more detrimental Aβ-levels are associated with a deterioration in the clustering of SWs and spindles, but not with the underlying hierarchy. Hence, in healthy participants, irrespective of Aβ-levels, the fundamental mechanism is still intact, while the more supplementary mechanism could be optimized in those with more detrimental Aβ-levels. Although our results suggest that distinct mechanisms of phase-amplitude coupling may be relevant at different levels of cognitive functioning, the neurophysiological correlates of these specific SW\u0026ndash;spindle coupling measures have yet to be elucidated.\u003c/p\u003e\u003cp\u003eAfter identifying SW\u0026ndash;spindle coupling as a strong predictor for plasma Aβ ratios, we aimed to investigate whether intervention-induced increases in SW\u0026ndash;spindle coupling might be associated with more beneficial Aβ-responses. Our results showed that a three-night PLAS intervention led to both increases in SWA as well as increases in SW\u0026ndash;spindle coupling, paralleling previous reports (Leminen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ngo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Papalambros et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wunderlin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The pooled analysis revealed that only PLAS-induced increases in SW amplitude, but not PLAS-induced increases in SW\u0026ndash;spindle coupling strength were associated with a beneficial Aβ-response from pre- to post-intervention. When separately analyzing healthy and cognitively impaired older adults, PLAS-induced increases in SW amplitude were consistently associated with a beneficial Aβ-response. Hence there seems to be a clear relationship between increases in SWA and more favorable changes in Aβ-levels, irrespective of cognitive functioning levels. This is in line with the hypothesis that SWA serves an important role in glymphatic clearance mechanisms potentially allowing for better washout of neurotoxins (Fultz et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Strikingly, PLAS-induced increases in SW\u0026ndash;spindle coupling strength were also associated with a beneficial Aβ-response, albeit only in the cognitively impaired, but not the healthy subgroup. Hence, in the cognitively impaired subgroup, our results suggest that increases in SW\u0026ndash;spindle coupling may also play a part in Aβ-dynamics. Again, it could be argued that SW\u0026ndash;spindle coupling might serve a similar or at least supporting clearance function as is the case for SWA. The PLAS-induced increase in SWA and coupling and the subsequently observed direct association between this increase and improved Aβ-response suggests that PLAS could be a valuable tool for enhancing Aβ-dynamics in individuals at risk for dementia.\u003c/p\u003e\u003cp\u003eThe reason for a selective effect of a PLAS-induced increase in SW\u0026ndash;spindle coupling on more beneficial Aβ-response in the cognitively impaired, but not the healthy group, is a matter of speculation. Arguably, as PLAS targets SWs, PLAS-induced increases in SWs/SWA may represent the primary effect of PLAS, while increases in SW\u0026ndash;spindle coupling likely emerge as a downstream, secondary effect: the increased SWA may create more windows of opportunity for coupling to occur. Because of a potentially disadvantaged starting position, PLAS-induced increases in SW amplitude may more effectively drive downstream improvements in SW\u0026ndash;spindle coupling in the cognitively impaired group. Support for the notion of a more disadvantaged starting position in cognitively impaired older adults comes from previous research suggesting that atrophy and worse memory are associated with worse SW\u0026ndash;spindle coupling (Helfrich et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muehlroth et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, although in our sample, baseline SW\u0026ndash;spindle coupling was decreased in cognitively impaired compared to healthy individuals, these differences were not significant (see suppl. table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). An alternative explanation for linking PLAS-induced increases to more beneficial Aβ-responses could be that instead of a causal effect of the stimulation, the network response to PLAS might simply serve as a predictor of how Aβ-levels progress, without the stimulation itself playing a causal role. However, if more responsive brains were to explain our results, we would expect to find effects either in both groups, or in the healthy group only, where brain responsiveness is arguably higher than in the cognitively impaired group.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough we used single-trial approaches to analyze both coupling strength and coupling directionality, we only found effects for the former. We acknowledge that our approach to coupling directionality on the single trial level does not measure precisely the same aspect as coupling directionality on the aggregated level, the phase slope index. Hence it is possible that PLAS effects were truly limited to coupling strength rather than coupling directionality; however, it is also conceivable that our single trial approach for coupling directionality was not sensitive enough to detect such effects. In line with our previous argument, the absence of an effect for coupling directionality is at least plausible. Our PLAS intervention\u0026mdash;particularly given its short-term nature\u0026mdash;can enhance SWA as intended, leading to increased clustering of spindles and SWs, but cannot affect the more fine-grained underlying process. It is possible that a longer intervention would be required to impact this finer process.\u003c/p\u003e\u003cp\u003eOur analyses are based on a limited sample size. However, our baseline sample is comparable to a similar study that investigated plasma levels of Aβ in relation to sleep markers (Rosenblum et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Studies examining the effects of sleep deprivation on plasma markers of Aβ have relied on smaller samples than those used in our intervention sample (Eide et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While larger samples would strengthen the generalizability of our findings, we took methodological steps to bolster the robustness of our results. Specifically, we employed mixed-effects models on the single trial level in our intervention analyses. This maximizes statistical power by leveraging all available data points while accounting for individual variability through random intercepts to increase statistical power. In our baseline analyses, we validated key findings across multiple statistical approaches\u0026mdash;including conservative, liberal, informed, and empty models. Only results consistent across all models were interpreted as robust.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this paper, we have established a specific role for SW\u0026ndash;spindle coupling and Aβ-dynamics\u0026mdash;both in unmodulated and PLAS-modulated sleep. The unique predictive strength for Aβ, even surpassing SWA and cognitive functioning levels, suggests that SW\u0026ndash;spindle coupling is a critical physiological process related to the early pathophysiology of dementia. Newly developed targeted interventions could therefore prioritize older adults with detrimental coupling- dynamics, focusing on those who would potentially benefit the most. Furthermore, our results suggest that PLAS-induced increases in sleep markers are closely linked to favorable Aβ-responses\u0026mdash;particularly in cognitively impaired older adults. Overall, the results suggest that PLAS is a useful tool that could yield favorable outcomes for Aβ levels and therefore help in fighting increasing incidence rates of dementia.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eA\u0026beta;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Amyloid-beta\u003c/p\u003e\n\u003cp\u003eAHI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Apnea-Hypopnea Index\u003c/p\u003e\n\u003cp\u003eBL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Baseline\u003c/p\u003e\n\u003cp\u003eE1 \u0026ndash; E3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Experimental nights 1 to 3\u003c/p\u003e\n\u003cp\u003eFOA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Face-Occupation Associations\u003c/p\u003e\n\u003cp\u003eLMM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Linear mixed effects model\u003c/p\u003e\n\u003cp\u003eMOCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Montreal Cognitive Assessment\u003c/p\u003e\n\u003cp\u003eN1 \u0026ndash; N3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sleep stages 1 to 3\u003c/p\u003e\n\u003cp\u003eNREM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Non-rapid eye movement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLAS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Phase-locked acoustic stimulation\u003c/p\u003e\n\u003cp\u003ePSI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Phase Slope Index\u003c/p\u003e\n\u003cp\u003ePSQI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pittsburgh Sleep Quality Index\u003c/p\u003e\n\u003cp\u003eREM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Rapid eye movement\u003c/p\u003e\n\u003cp\u003eRVL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Resultant vector lengt\u003c/p\u003e\n\u003cp\u003eSWA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Slow wave activity\u003c/p\u003e\n\u003cp\u003eSWS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Slow wave sleep\u003c/p\u003e\n\u003cp\u003eSW(s) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Slow wave(s)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sleep efficiency\u003c/p\u003e\n\u003cp\u003eSL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sleep latency\u003c/p\u003e\n\u003cp\u003eTST\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Total sleep time\u003c/p\u003e\n\u003cp\u003eWASO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Wake time after sleep onset\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eWe thank all interns, students and assistants for their valuable work during data acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Dementia Research: Synapsis Foundation Switzerland, in collaboration with the Peter Bockhoff Foundation, the Heidi Seiler Foundation, and the Kurt Fries Foundation: grants 2018-PI02 \u0026amp; 2021-CDA03. This work was further supported by the Swiss National Science Foundation (SNSF): grant number 215333.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor Information\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors and Affiliations\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eMarina Wunderlin:\u0026nbsp;University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland\u003c/p\u003e\n\u003cp\u003eKorian Wicki:\u0026nbsp;University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland\u003c/p\u003e\n\u003cp\u003eGraduate School\u0026nbsp;for Health Sciences, University of Bern, 3012 Bern, Switzerland\u003c/p\u003e\n\u003cp\u003eCharlotte Elisabeth Teunissen:\u0026nbsp;Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Neurodegeneration, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands\u003c/p\u003e\n\u003cp\u003eMarc Alain Z\u0026uuml;st:\u0026nbsp;University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, 3000 Bern 60, Switzerland\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eContributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: MAZ, MW\u003c/p\u003e\n\u003cp\u003eStudy Design: MAZ, MW\u003c/p\u003e\n\u003cp\u003eData Collection: MW\u003c/p\u003e\n\u003cp\u003eData Analysis: MW, MAZ, CET\u003c/p\u003e\n\u003cp\u003eVisualization: MW, MAZ\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;original draft: MW\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;review: MAZ, CET, KW\u003c/p\u003e\n\u003cp\u003eWriting\u0026mdash;editing: MW, MAZ\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCorresponding authors\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eMarc A. Z\u0026uuml;st \u0026amp; Marina Wunderlin\u003c/p\u003e\n\u003cp\u003eEthics Declarations\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the local ethics committee of the canton of Bern, Switzerland. Written informed consent was obtained from all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due due to ethical restrictions but are available from the corresponding author on reasonable request. The analysis code used in this study will be made available via Git.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBeason-Held, L.L., Goh, J.O., An, Y., et al. Changes in Brain Function Occur Years before the Onset of Cognitive Impairment. Journal of Neuroscience, 2013, 33: 18008\u0026ndash;18014.\u003c/li\u003e\n\u003cli\u003eBelsley, D.A., Kuh, E., Welsch, R.E. Regression diagnostics: Identifying influential data and sources of collinearity. John Wiley \u0026amp; Sons , 2005.\u003c/li\u003e\n\u003cli\u003eBerens, P. CircStat: A MATLAB Toolbox for Circular Statistics. Journal of Statistical Software, 2009, 31: 1\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eBigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., Robbins, K.A. The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Front. Neuroinform., 2015, 9:.\u003c/li\u003e\n\u003cli\u003eBuysse, D.J., Reynolds, C.F., Monk, T.H., Berman, S.R., Kupfer, D.J. 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Multi-night acoustic stimulation is associated with better sleep, amyloid dynamics, and memory in older adults with cognitive impairment. GeroScience, 2024, 46: 6157\u0026ndash;6172.\u003c/li\u003e\n\u003cli\u003eZ\u0026uuml;st, M.A., Mikutta, C., Omlin, X., et al. The hierarchy of coupled sleep oscillations reverses with aging in humans. J. Neurosci., 2023,.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"amyloid-beta, plasma marker, slow wave sleep, slow wave–spindle coupling","lastPublishedDoi":"10.21203/rs.3.rs-7085440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7085440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSlow wave activity, the signature of deep/slow wave sleep, has consistently been linked to amyloid-beta (Aβ), a biomarker of Alzheimer\u0026rsquo;s disease. Less is known about how Aβ relates to specific microstructural processes within slow wave sleep, such as the coupling of slow waves (SW) and sleep spindles. Although better SW\u0026ndash;spindle coupling has been associated with younger age, increased memory performance, and less brain atrophy, its relationship with Aβ remains poorly understood, particularly due to a lack of research in cognitively impaired older adults. Here, we investigate the association between SW\u0026ndash;spindle coupling and Aβ in both cognitively normal and cognitively impaired older individuals. Additionally, we examine how an acoustic stimulation intervention known to boost slow wave sleep affects the link between SW\u0026ndash;spindle coupling and Aβ.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eForty-seven older adults (age\u003csub\u003emean\u003c/sub\u003e = 70.5 (0.68)), ranging from cognitively impaired to cognitively healthy, completed one adaptation and one baseline night. A subsample (n\u0026thinsp;=\u0026thinsp;39, age\u003csub\u003emean\u003c/sub\u003e = 70.5 (0.74)) additionally underwent a three-night acoustic stimulation intervention designed to boost slow wave activity. Blood samples post-baseline and post-intervention were analyzed for Aβ 1\u0026ndash;42/1-40-ratio.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eRegardless of cognitive functioning, SW\u0026ndash;spindle coupling was the best predictor for baseline Aβ, better than slow wave activity, age or cognitive functioning. Specifically, more favorable Aβ-levels were linked to a SW\u0026ndash;spindle coupling physiology resembling a younger brain. While intervention-induced increases in slow wave activity were linked to a beneficial Aβ-response across all cognitive levels, intervention-induced increases in SW\u0026ndash;spindle coupling benefited Aβ-response exclusively in cognitively impaired individuals.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur results suggest a link between SW\u0026ndash;spindle coupling and Aβ going beyond slow wave activity. This hints towards a potential specific function of SW\u0026ndash;spindle coupling related to the early pathophysiology of Alzheimer\u0026rsquo;s disease.\u003c/p\u003e","manuscriptTitle":"Slow wave–spindle coupling during deep sleep is selectively linked to Plasma Amyloid-β levels in Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 11:06:10","doi":"10.21203/rs.3.rs-7085440/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8043e420-64e1-454b-aa74-6e33017d037b","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-13T12:28:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 11:06:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7085440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7085440","identity":"rs-7085440","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}