Differential expression of spatiotemporal sleep spindle clusters in aging

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Motivated by the potential of objective neurophysiological markers to index thalamocortical function in patients with severe psychiatric illnesses, we comprehensively characterized key non-rapid eye movement (NREM) sleep parameters across multiple domains, their interdependencies, and their relationship to waking event-related potentials and symptom severity. In 72 schizophrenia (SCZ) patients and 58 controls, we confirmed a marked reduction in sleep spindle density in SCZ and extended these findings to show that fast and slow spindle properties were largely uncorrelated. We also describe a novel measure of slow oscillation and spindle interaction that was attenuated in SCZ. The main sleep findings were replicated in a demographically distinct sample, and a joint model, based on multiple NREM components, statistically predicted disease status in the replication cohort. Although also altered in patients, auditory event-related potentials elicited during wake were unrelated to NREM metrics. Consistent with a growing literature implicating thalamocortical dysfunction in SCZ, our characterization identifies independent NREM and wake EEG biomarkers that may index distinct aspects of SCZ pathophysiology and point to multiple neural mechanisms underlying disease heterogeneity. This study lays the groundwork for evaluating these neurophysiological markers, individually or in combination, to guide efforts at treatment and prevention as well as identifying individuals most likely to benefit from specific interventions. Editor's evaluation This study, one of the largest of its kind, replicates previous findings regarding the impairment of sleep rhythms in patients with schizophrenia relative to healthy controls. Specifically, sleep spindles, which constitute a hallmark of non-Rapid Eye Movement sleep, are less frequent in people with schizophrenia and several other sleep features were also affected. Overall, this study provides evidence that brain dynamics during sleep are promising biomarkers for the diagnosis and the prevention of schizophrenia. https://doi.org/10.7554/eLife.76211.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Schizophrenia (SCZ) is a chronic disorder characterized by cognitive and behavioral dysfunction that significantly impacts the quality of life of affected individuals and their caregivers (Charlson et al., 2018; Stanley et al., 2017). It is highly heritable, exhibiting a heterogenous and polygenic architecture implicating many genetic risk factors (Ripke, 2014). Despite intensive research, current medications ameliorate positive symptoms only in a subset of individuals, often with side effects and with little impact on negative symptoms or cognitive deficits. Consequently, recovery outcomes have not improved over the past decades (Jääskeläinen et al., 2013). Given the clinical and genetic heterogeneity of SCZ, identifying reliable, objective biomarkers that index specific neurobiological deficits is crucial for developing the next-generation therapeutics. Emerging evidence points to thalamus as a critical node that supports cognitive function, and to abnormal thalamocortical connectivity as a key neurobiological deficit in SCZ (Woodward et al., 2012; Anticevic et al., 2015; Cao et al., 2018; Woodward and Heckers, 2016; Wu et al., 2022). NREM (non-rapid eye movement) sleep offers a lens through which we may index thalamocortical function without confounds from waking behaviors such as active symptoms or altered motivation. Two hallmarks of NREM sleep measured by the electroencephalogram (EEG) – slow oscillations (SO) and spindles – reflect distinct thalamic and thalamocortical circuits. The slow (~1 Hz) neuronal oscillations with large amplitude are generated by cortical neurons and propagated by cortico-thalamo-cortical circuits, while spindles are bursts of oscillatory neural activity (typically between 10 and 16 Hz and ~1 s in duration) arising from reverberant interaction between thalamic reticular nucleus (TRN) and thalamocortical relay neurons and modulated by thalamocortical connections. The coupling of SOs and spindles mediates information transfer and storage during sleep that underpins NREM’s role in overnight memory consolidation (Walker and Stickgold, 2004). NREM traits show strong heritability in healthy populations (Ambrosius et al., 2008), correlate with cognitive performances (Geiger et al., 2011; De Gennaro et al., 2008; Purcell et al., 2017), and afford objective, quantifiable markers of thalamic and thalamocortical functioning in large cohorts. Impaired sleep spindles and SOs have been reported in SCZ patients. Despite relatively consistent support for a general reduction in spindle density and/or amplitude in SCZ patients (Ferrarelli et al., 2007; Ferrarelli et al., 2010; Göder et al., 2015; Mylonas et al., 2020a; Schilling et al., 2017; Wamsley et al., 2012), it is not clear how specific topographical and temporal spindle properties, which may reflect distinct thalamocortical connections, are associated with SCZ. For example, distinguishing fast and slow spindles (FS and SS) and their temporal coupling with SO are less studied in SCZ patients. More generally, a comprehensive examination of how NREM features (including existing and novel metrics) are altered in SCZ and how they relate to each other is not established (Castelnovo et al., 2018; Zhang et al., 2020). This is in part due to the relatively small sample sizes (N < 30) previously utilized, that were unable to support comprehensive analyses across multiple domains of NREM sleep. More importantly, whether and how distinct NREM sleep deficits track with waking EEG, clinical symptoms, medication, and cognition within patients is not clear (Au and Harvey, 2020). For example, one recent report suggested reductions in spindle density were significantly more extensive in patients experiencing auditory hallucinations compared to hallucination-free patients (Sun et al., 2021). Such a link is intriguing, considering the substantial evidence of altered auditory processing in SCZ (Erickson et al., 2016; Freedman et al., 2020; Thuné et al., 2016), and that both spindles and auditory processing heavily rely on uninterrupted and precise function of thalamocortical circuits (Ferrarelli and Tononi, 2010). However, whether auditory abnormalities during wake and spindle deficits during sleep reflect similar or distinct thalamocortical dysfunctions in SCZ are yet to be investigated. Here, using whole-night high-density electroencephalography (hdEEG), we comprehensively characterized alterations across multiple domains of NREM neurophysiology, their interdependencies and relationship to wake and clinical features in a new SCZ cohort of 72 patients and 58 healthy controls. We first sought to replicate the primary spindle density deficit and characterize the precise facets of spindle activity associated with SCZ, including topography, morphology, and novel metrics of intra-spindle frequency modulation. Second, we extend our analyses to broader NREM sleep EEG, including spectral features, functional connectivity, SOs, and their coupling with spindles, to determine which features track with disease state, and whether their association with SCZ is independent of spindle parameters. Third, we asked whether spindle deficits likely index the same thalamocortical dysfunctions reflected in auditory processing abnormalities during wake, by determining how sleep and wake metrics were correlated within individuals. Fourth, we investigated the relationship between disease-associated metrics and symptom severity within patients. Fifth, we attempted to replicate our sleep EEG findings by compiling an independent and demographically distinct replication dataset of 57 cases and 59 controls and applying the same analytic procedures. Finally, based on multiple domains of EEG metric, we constructed joint models to classify diagnostic status and assessed their predictive performance in the original sample, as well as their transferability to the independent collection of patients with distinct demographics. Results Whole-night sleep EEG and wake auditory event-related potential (ERP) data were acquired from 130 individuals – 72 patients diagnosed with SCZ (25 females, 34.8 ± 7.0 years of age) and 58 healthy volunteers (CTR, 25 females, 31.7 ± 6.3 years of age) with no personal or family history of SCZ spectrum disorders. As SCZ patients were on average older than controls (effect size [e.s.] = 0.48 standard deviation (SD) units, p = 0.014), all statistical comparisons were performed using logistic regression with age and sex as covariates (see Materials and methods for details). Sleep stage macro-architecture is largely unaltered in SCZ Based on manual sleep staging in 30 s epochs (see Materials and methods), the duration and proportion of sleep stages did not differ between SCZ and CTR groups, with the exception of the proportion of N2 sleep relative to total sleep time (TST), which was smaller in SCZ (e.s. = –0.38 SD, p = 0.048). For the primary analytic sample, we removed four subjects (due to persistent line noise artifacts, see Materials and methods for details); in this final N = 126 sample, there were no significant case/control differences in stage duration (p = 0.14 for N2 percentage). Patients spent longer time in bed (TIB) before sleep onset (sleep latency, 50 versus 24 min, e.s. = 0.95 SD, p = 0.0003), with concomitant differences in TIB (e.s. = 1.47 SD, p = 2×10–7) and sleep efficiency (e.s. = −0.8 SD, p = 0.0013). Wake after sleep onset (WASO) time was not significantly different between groups (p = 0.13), suggesting that reduced sleep efficiency in SCZ was attributed to later sleep onset rather than more fragmented sleep. As a confirmation, group differences in sleep efficiency were not significant after controlling for sleep latency (p = 0.1). Congruent with the sleep EEG night, data from a sleep habit questionnaire, a 2-week sleep journal, and information on sleep and wake times the night before the EEG recording all indicated that SCZ patients had earlier bedtimes (all p < 10–10) and longer TIB (all p < 10–8), suggesting that the EEG night followed a typical sleep/wake schedule for SCZ patients. N2 sigma power is decreased in SCZ Sleep EEG analyses were based on 57 channels, resampled to 200 Hz and re-referenced to the linked mastoids. We extracted all N2 epochs and applied automated artifact detection/correction; all sleep EEG analyses used our open-source Luna package (see Materials and methods). With the exclusion of four patients due to persistent line noise artifacts, the final sleep EEG dataset comprised of 68 SCZ and 58 CTR, with an average of 353 ± 143 and 374 ± 89 N2 epochs, respectively (no group difference, p = 0.16). Replicating prior studies (Ferrarelli et al., 2007; Ferrarelli et al., 2010; Manoach et al., 2010; Manoach et al., 2014), sigma-band (11–15 Hz) spectral power, a common proxy for spindle activity, was significantly reduced in SCZ (for power differences in classical frequency bands, see Figure 1—figure supplement 1). Figure 1 (top row) illustrates spectral power from 0.5 to 20 Hz averaged over Fz, Cz, and Pz channels, with shaded regions indicating significant group differences (p < 0.01, unadjusted for multiple comparisons). Across all channels, the largest sigma-band deficit (–0.88 SD) was observed for FC1 at 13.25 Hz. Adjusted for multiple comparisons (see Materials and methods), 34 channels showed deficits (padj < 0.05) at 13.25 Hz (Figure 1, bottom right). Power in delta and lower theta frequencies (~2–6 Hz) was also reduced in SCZ (Figure 1, top row) with a maximum effect size at 3.25 Hz for Cz (e.s. = –1.21 SD) and 46 channels showing significantly (padj < 0.05) lower power in SCZ (Figure 1, bottom left). Figure 1 with 1 supplement see all Download asset Open asset Decreased power in 2–6 Hz and spindle frequencies in schizophrenia (SCZ) during N2 sleep. The top row illustrates spectral power for frequencies from 0.5 to 20 Hz at Fz, Cz, and Pz channels averaged for SCZ (purple) and CTR (green) groups. Dotted black line illustrates the difference (SCZ-CTR) in averaged spectral power (10×Δlog10(PSD)) and frequency ranges marked with vertical gray bars illustrate frequency bins that showed significant group differences (p < 0.01, unadjusted). The plots in the bottom row illustrate power spectral density (PSD) averaged within groups and spatial distribution of group differences across channels. Channels with p-values adjusted for multiple comparisons (padj) < 0.05 are encircled with black line. The color of each channel corresponds to the signed −log10 adjusted p-value indicating the direction of group differences (blue corresponds to reduction, and red corresponds to increase in SCZ). Two scatterplots show individual spectral power for SCZ and CTR groups for channels with the largest effect sizes for the two indicated frequencies (marked with a white cross on the topoplots). Spindle density is reduced in SCZ, accompanied by altered spindle morphology We detected discrete spindle events using our previously reported wavelet-based algorithm (Purcell et al., 2017), targeting SS and FS separately by setting wavelet center frequencies to 11 and 15 Hz, respectively. Spindle densities showed the expected topographies, being maximal at either central/parietal (FS) or frontal (SS) channels (Figure 2). FS density was globally decreased in SCZ, significant at padj < 0.05 for 53 of 57 channels, with the largest deficit (32% reduction and e.s. of –1.27 SD) at FC2 (nominal p = 4×10–6). At this channel, average FS density in the CTR group was 2.7±0.7 spindles per minute, compared to 1.9±0.8 in the SCZ group. SS also exhibited reduced densities, albeit mostly restricted to posterior channels (max e.s. –0.79 SD at P7, reduction = 28%, nominal p = 5×10–5). Taken together, these analyses confirmed reductions in both sigma power and spindle density with high statistical confidence, providing a clear and independent replication of prior reports. Figure 2 with 3 supplements see all Download asset Open asset Reduced density of slow (SS) and fast spindles (FS) during N2 sleep in schizophrenia (SCZ) patients observed together with alterations in spindle morphology. The top two rows show topographical distribution in density, amplitude, integrated spindle activity (ISA), duration, chirp, and frequency of SS and FS averaged across SCZ (first row) and CTR (second row) groups. The third row of topographical plots illustrates group differences in these metrics. Each circle represents an EEG channel. EEG channels with adjusted p-values (multiple comparisons) < 0.05 are encircled in black and the color of each channel corresponds to the signed −log10 adjusted p-value indicating the direction of group differences (blue corresponds to reduction, and red corresponds to increase in SCZ). The bottom row illustrates distributions of spindle parameters in SCZ and CTR groups in the channel with the largest effect size of group differences (marked with a white cross on the topoplot). For each spindle, we further estimated the amplitude (maximal peak-to-peak voltage), duration, integrated spindle activity (ISA, a normalized measure that reflects both duration and amplitude), frequency, and chirp (intra-spindle change in frequency, indexing the typical deceleration of a spindle oscillation over its course) (Figure 2, Supplementary file 1). See Materials and methods for details. For SS, we observed an extensive SCZ-associated reduction in amplitude (50 channels with padj < 0.05, max e.s. –0.82 SD with 18% reduction at CP3) and ISA (33 channels with padj < 0.05, max e.s. –0.96 SD at CPZ). Reductions for FS were topographically more limited for amplitude (23 central/parietal channels with padj < 0.05, max e.s. –0.62 SD with 15% reduction at Cz) and ISA (single channel, F1, padj = 0.037, e.s. = −0.73 SD). FS (but not SS) were shorter in duration for SCZ in all but prefrontal and temporal channels (36 channels with padj < 0.05, max e.s. –0.98 SD at P5). In addition, FS chirp was more negative in SCZ (20 posterior channels with padj < 0.05, max e.s. –1.09 SD at O1), indicating more prominent deceleration in SCZ, that is, stronger chirp. Both SS and FS showed comparable SCZ and CTR distributions of observed average spindle frequencies, however. Densities of SS and FS were not significantly correlated with each other (abs(r) < 0.15 and p > 0.05 for SS and FS at Cz or Fz, as well as SS at Fz compared to FS at Cz). With respect to density reduction in SCZ, SS and FS effects were also statistically independent. For example, group differences in SS density at P7 (largest e.s. of the group differences, p = 5×10–5) were still significant (p = 6×10–4) when FS density (at P7 and FC2 – largest e.s. of differences in FS density) was added as a covariate in a joint model. The same was true for FS density at FC2 (original p = 4×10–6 and after controlling for SS density, p = 4×10–5). Below (see section Dimension reduction to summarize sleep EEG alterations), we explore in more detail the correlational structure of sleep EEG metrics, quantifying the underlying, independent components of variation across spectral, spindle, and other metrics, accounting for dependencies across scalp topography and neighboring frequencies. We confirmed that observed spindle density alterations extended to entire NREM sleep (N2 and N3 combined, data not shown) but expressed a sleep cycle-dependent effect for both SS and FS with more extensive group differences in the later sleep (cycle 3 compared to cycle 1, Figure 2—figure supplement 1). Sensitivity analyses to address potential medication effects Since 67 of 68 patients in the analytic sample were taking antipsychotic medication (Supplementary file 2), we converted antipsychotic doses to chlorpromazine equivalents (Langan et al., 2012), to determine whether group differences in spindle characteristics were likely to reflect medication effects (Supplementary file 2). There were no significant (all unadjusted p > 0.01) associations between total antipsychotic dose and any of the spindle parameters altered in SCZ, with the exception of FS chirp. However, this latter effect, seen only at two channels, had a direction of effect opposite to group differences observed in SCZ (FC6, t-value = −2.83, p = 0.006 and TP8, t-value = 2.8, p = 0.007), meaning that the original FS chirp SCZ association did not reflect antipsychotic multiple spindle parameters correlated with (N = Supplementary file 2), all original differences significant after patients using were removed from the analyses (Supplementary file With respect to medication such as and and and (Supplementary file top group differences in spindle metrics after controlling for each for FS Taken together, these that the observed group differences be by medication consistent with prior studies showing spindle deficits in patients and et al., 2014). SO abnormalities in SCZ We detected SOs in N2 sleep using a previously based on amplitude (see Materials and methods). Patients had an density of SOs across posterior channels channels with padj < 0.05, max e.s. = SD at 18% longer SO (36 channels with padj < 0.05, max e.s. SD at Cz, 18% and SO channels with padj < 0.05, max e.s. SD at Cz, compared to controls (Figure differences between groups were observed in negative or peak-to-peak Figure 3 with 1 supplement see all Download asset Open asset slow oscillations and their coupling with spindles in schizophrenia the topographical distribution of different SO parameters averaged across SCZ (first row) and CTR (second row) groups. between SO and spindles in SCZ and CTR groups. Each point represents coupling and average SO of a spindle at a channel in each SO of coupling for slow and fast spindles is as a vertical line of the The black line illustrates SO at in the third row illustrate and direction of group differences across electroencephalogram (EEG) channels in SO parameters and its coupling with EEG channels with significant group differences (padj < 0.05) are encircled in black and the color of each channel the signed −log10 adjusted p-value indicating of or increase of in the EEG in SCZ compared to data at channels with the largest effect sizes of group differences are in the bottom row of plots for the altered EEG parameters in SCZ channel is marked with a white cross on the SO parameters were not associated with total dose of but were affected by SO duration and decreased were significantly associated with of and reduced SO was also linked to and medication p < 0.01, Supplementary file 2). However, all group differences in analyses controlling for medication (Supplementary file group differences in SO characteristics on the of versus relative amplitude for SOs, as we have in other et al., 2021). We relative amplitude for our primary as it the of coupling observed across the using an differences in SO density, duration, and were SO negative amplitude significant group In addition, as SOs are most of N3 sleep, we performed that N2 and N3 sleep alterations in duration and of SOs significantly different between the groups, there were no differences in SO density applying either or amplitude for SO cycle specific of N2 using that SO density alterations were more later in the night (Figure 2—figure supplement 1). coupling but altered between SS and SO We coupling in using distributions to for coupling (see Materials and the of between spindles and SOs, the of coupling based on the of the distribution of SO at spindle and the from that As we observed a temporal coupling between both FS and SS and SO (Figure top right). Consistent with previous et al., FS of maximal peak-to-peak were on the of the SO and the SO positive = in SS to later = in (Figure top right). In of subjects had significant SO coupling for FS at Cz p-value < 0.05 by spindle with respect to SO to the and had significant SS coupling at The SCZ group comparable and of cases showing evidence of for FS at Cz and SS at Fz, We did not any significant group differences in the average of SO coupling based on the distribution normalized (Figure see Figure supplement 1 for the topographical distribution of coupling characteristics averaged across SCZ and CTR With respect to SO both SS and FS to earlier in SCZ, albeit this effect was only observed at a channels any strong and P7 for SS, max e.s. = SD and for max e.s. = SD). The most coupling differences were in of SS of spindles showing any with an controlling for spindle and SO which was decreased in SCZ channels with padj < 0.05, max e.s. SD at of the coupling metrics were associated with total dose of but there was in patients taking or an opposite effect was observed for and medication (Supplementary file 2). frequency and SO As we observed a deceleration of FS in SCZ. This effect was statistically independent of the reduction in FS for example, in a joint model, FS chirp and density at Pz were both independent of SCZ (FS density = p = and chirp = p = Given that spindles are with SO as well as the that we observed differences for both FS chirp and SO at spindle we asked whether spindle deceleration was on SO We spindle frequency using a see Materials and as a function of spindle of spindle duration) and also SO first only spindle we observed the negative chirp during the later of the for both FS and SS across all channels (Figure top We further observed that spindle frequency was modulated by SO across channels but for FS and SS, likely the different temporal SO coupling of FS and SS (Figure for SS, the frequencies with the SO positive but earlier for FS (Figure top right). Figure with 1 supplement see all Download asset Open asset frequency deceleration is associated with slow oscillation (SO) for slow and fast spindles and such is attenuated in schizophrenia frequency across slow and fast spindle as spindles in across all channels across all intra-spindle frequency on SO across all channels and total of spindles detected in all channels and individuals at a SO spindle frequency as a function of both spindle and SO of the each represents frequency of spindles averaged across all channels for each illustrate frequency of spindles to the negative and red illustrate spindles to the positive of The on the illustrate topographical distribution of averaged within SCZ and CTR groups separately for slow and fast was estimated as between frequency of individual spindle and of in bottom illustrate group differences in modulation. Channels with p-values < are encircled in and the color of each channel represents the signed −log10 p-value indicating decreased of or of coupling in the SCZ group. Spindle and SO were We spindle frequency as a joint function of both spindle and SO that is, spindle bins = averaged over all channels (Figure lower Figure supplement 1). This suggested that spindle and SO have largely effects on spindle frequency, as each line similar chirp. on spindle the SO effect is still by the vertical of the different More SS frequency as a function of spindle and/or SO showed an associated adjusted = 0.48 and and = 0.95 For SO relatively less of the in spindle frequency = versus = for spindle combined, = is, SO a effect on spindle frequency, independent of the typical and this was stronger for SS than this from temporal we to it as

Background and HypothesisCognitive impairment is a core feature of schizophrenia that worsens with aging and interferes with quality of life. Recent work identifies sleep as an actionable target to alleviate cognitive deficits. Cardinal non-rapid eye movement (NREM) sleep oscillations such as sleep spindles and slow oscillations are critical for cognition. People living with schizophrenia (PLWS) and their first-degree relatives have a specific reduction in sleep spindles and an abnormality in their temporal coordination with slow oscillations that predict impaired memory consolidation. While NREM oscillatory activity is reduced in typical aging, it is not known how further disruption in these oscillations contributes to cognitive decline in older PLWS. Another understudied risk factor for cognitive deficits among older PLWS is obstructive sleep apnea (OSA) which may contribute to cognitive decline.Study DesignWe conducted a narrative review to examine the published literature on aging, OSA, and NREM sleep oscillations in PLWS.Study ResultsSpindles are propagated via thalamocortical feedback loops, and this circuitry shows abnormal hyperconnectivity in schizophrenia as revealed by structural and functional MRI studies. While the risk and severity of OSA increase with age, older PLWS are particularly vulnerable to OSA-related cognitive deficits because OSA is often underdiagnosed and undertreated, and OSA adds further damage to the circuitry that generates NREM sleep oscillations.ConclusionsWe highlight the critical need to study NREM sleep in older PWLS and propose that identifying and treating OSA in older PLWS will provide an avenue to potentially mitigate and prevent cognitive decline.

The slow oscillation (SO) observed during deep sleep is known to facilitate memory consolidation. However, the impact of age-related changes in sleep electroencephalography (EEG) oscillations and memory remains unknown. In this study, we aimed to investigate the contribution of age-related changes in sleep SO and its role in memory decline by combining EEG recordings and computational modeling. Based on the detected SO events, we found that older adults exhibit lower SO density, lower SO frequency, and longer Up and Down state durations during N3 sleep compared to young and middle-aged groups. Using a biophysically detailed thalamocortical network model, we simulated the "aged" brain as a partial loss of synaptic connections between neurons in the cortex. Our simulations showed that the changes in sleep SO properties in the "aged" brain, similar to those observed in older adults, resulting in impaired memory consolidation. Overall, this study provides mechanistic insights into how age-related changes modulate sleep SOs and memory decline.Clinical Relevance- This study contributes towards finding feasible biomarkers and target mechanism for designing therapy in older adults with memory deficits, such as Alzheimer's disease patients.

We investigated electroencephalographic (EEG) slow oscillations (SOs), sleep spindles (SSs), and their temporal coordination during nonrapid eye movement (NREM) sleep in patients with idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD). We analyzed 16 patients with video-polysomnography-confirmed iRBD (age, 65.4 ± 6.6 years; male, 87.5%) and 10 controls (age, 62.3 ± 7.5 years; male, 70%). SSs and SOs were automatically detected during stage N2 and N3. We analyzed their characteristics, including density, frequency, duration, and amplitude. We additionally identified SO-locked spindles and examined their phase distribution and phase locking with the corresponding SO. For inter-group comparisons, we used the independent samples t-test or Wilcoxon rank-sum test, as appropriate. The SOs of iRBD patients had significantly lower amplitude, longer duration (p = 0.005 for both), and shallower slope (p < 0.001) than those of controls. The SS power of iRBD patients was significantly lower than that of controls (p = 0.002), although spindle density did not differ significantly. Furthermore, SO-locked spindles of iRBD patients prematurely occurred during the down-to-up-state transition of SOs, whereas those of controls occurred at the up-state peak of SOs (p = 0.009). The phase of SO-locked spindles showed a positive correlation with delayed recall subscores (p = 0.005) but not with tonic or phasic electromyography activity during REM sleep. In this study, we found abnormal EEG oscillations during NREM sleep in patients with iRBD. The impaired temporal coupling between SOs and SSs may reflect early neurodegenerative changes in iRBD.

Certain neurophysiological characteristics of sleep, in particular slow oscillations (SOs), sleep spindles, and their temporal coupling, have been well characterised and associated with human memory abilities. Delta waves, which are somewhat higher in frequency and lower in amplitude compared to SOs, and their interaction with spindles have only recently been found to play a critical role in memory processing of rodents, through a competitive interaction between SO-spindle and delta-spindle coupling. However, human studies that comprehensively address delta wave interactions with spindles and SOs, as well as their functional role for memory are still lacking. Electroencephalographic data were acquired across three naps of 33 healthy older human participants (17 female) to investigate delta-spindle coupling and the interplay between delta- and SO-related activity. Additionally, we determined intra-individual stability of coupling measures and their potential link to the ability to form novel memories in a verbal memory task. Our results revealed weaker delta-spindle compared to SO-spindle coupling. Contrary to our initial hypothesis, we found no evidence for an opposing dependency between SO- and delta-related activities during non-rapid eye movement sleep. Moreover, the ratio between SO- and delta-nested spindles rather than SO-spindle and delta-spindle coupling measures by themselves predicted the ability to form novel memories best. In conclusion, our results do not confirm previous findings in rodents on competitive interactions between delta activity and SO-spindle coupling in older adults. However, they support the hypothesis that SO, delta wave, and spindle activity should be jointly considered when aiming to link sleep physiology and memory formation.

Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define ‘Process S’, a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5–4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space.

Age-related changes in striatal function are potentially important for predicting declining memory performance over the adult life span. Here, we used fMRI to measure functional connectivity of caudate subfields with large-scale association networks and positron emission tomography to measure striatal dopamine transporter (DAT) density in 51 older adults (age 65-86 years) who received annual cognitive testing for up to 7 years (mean = 5.59, range 2-7 years). Analyses showed that cortical-caudate functional connectivity was less differentiated in older compared with younger adults (n = 63, age 18-32 years). Unlike in younger adults, the central lateral caudate was less strongly coupled with the frontal parietal control network in older adults. Older adults also showed less "decoupling" of the caudate from other networks, including areas of the default network (DN) and the hippocampal complex. Contrary to expectations, less decoupling between caudate and the DN was not associated with an age-related reduction of striatal DAT, suggesting that neurobiological changes in the cortex may drive dedifferentiation of cortical-caudate connectivity. Reduction of specificity in functional coupling between caudate and regions of the DN predicted memory decline over subsequent years at older ages. The age-related reduction in striatal DAT density also predicted memory decline, suggesting that a relation between striatal functions and memory decline in aging is multifaceted. Collectively, the study provides evidence highlighting the association of age-related differences in striatal function to memory decline in normal aging.

Neurobiologic Mechanisms of Sleep and Wakefulness

IN THIS ISSUE OF SLEEP GENZEL ET AL.1 PRESENT SURPRISING DATA ASKING US TO RECONSIDER WHAT WE THINK ABOUT SLEEP-DEPENDENT MEMORY consolidation. Classically, according to the dual-process hypothesis2 it is believed that REM sleep facilitates procedural skill memory, whereas slow wave sleep (SWS) facilitates declarative memories. However, surprisingly only rarely we see direct relationships of SWS or REM amounts after learning with overnight changes in declarative or procedural memory performance. In the current study by Genzel and colleagues subjects were deprived once each from SWS and REM sleep, and were once allowed to spend an undisturbed night. Memory performance was tested at 21:00 as well as 60 hours later, to bypass acute fatigue effects. Although deprivation of SWS and REM sleep (SWSD, REMD) led to significant reduction of the respective sleep stage (SWSD: 26.5 vs. 105.2 min, REMD: 12.1 vs. 81.6 min), procedural (finger tapping) as well as declarative memory consolidation (word pair association) remained unaffected. Yet stage 2 (S2) sleep spindles were found to be correlated with declarative memory performance after the undisturbed night. Unfortunately, the authors did not include a non-learning baseline night which makes it difficult to clearly dissociate trait-like (spindles are known to be generally elevated in good learners3) from immediate learning effects. Generally this is a caveat of many studies of this kind. That is, authors either do not relate their findings to overnight gains but simply to post-learning performances, or they relate absolute measures from the learning night (such as sleep spindles) to memory performance. However, to see if the change in sleep after learning is related to the change in memory performance overnight, it is important that these change scores be correlated. The experimental paradigm most often used to compare effects of REM with those of SWS was designed by Ekstrand and colleagues.4 This paradigm takes advantage of the fact that SWS predominates during early sleep and REM during late sleep. Memory retention is then compared across sleep periods of equal length but with either REM or SWS predominating. Plihal and Born2 extended that paradigm and used word-pair lists and mirror-tracing skills to assess whether the declarative and procedural memory systems can be related to SWS and REM, respectively. Although much data derived from that paradigm support the dual-process hypothesis, it is evident that plenty of studies to not fit this simple idea (Table 1). Table 1 Empirical Evidence Indicating that the Dual-Process Model of Sleep-Dependent Memory Consolidation is Incomplete Surprisingly, and as in the study by Genzel and colleagues, REMD does not always block procedural memory consolidation as would be expected.7,9,10 The lack of effect might be attributable to the residual amounts of the deprived sleep stage. It is therefore possible that the 12 min of REM in the REMD condition of the present study were sufficient for REM sleep to exert its positive effect on memory. In this context it is especially worth noting that even ultra short episodes of sleep (6 min) have been reported to exert beneficial effects on memory.18 Furthermore, in some sleep deprivation studies subjects are allowed to immediately return to sleep after being awakened questioning whether subjects were truly awake at all. Genzel and colleagues adequately controlled for that effect by asking subjects to perform simple arithmetic calculations for the duration of 2 minutes before going back to sleep. In general, the present study appears well conducted and results are not explainable by simple methodological flaws. Interestingly, S2 sleep filling almost 50% of the total sleep time at night has found little attention in models of sleep-dependent memory consolidation. Yet the distinct waxing and waning 12–15 Hz oscillatory patterns, termed sleep spindles have long been postulated to provide a physiological brain state supporting synaptic plasticity.19 Many of the effects seen when depriving subjects of early SWS might really originate from depriving the brain of early sleep spindling rather than SWS per se. Evidence accumulates indicating that sleep spindles serve declarative memory consolidation as well as procedural motor skills (Table 1). Of course there is also positive evidence for the dual-process model, as illustrated by the study from Rasch and colleagues20 demonstrating that odor cuing during SWS can improve the retention of hippocampus-dependent declarative memories but not of hippocampus-independent procedural memories. Likewise the study from Marshall and colleagues21 beautifully demonstrated that boosting slow oscillations by transcrainal direct current stimulation during SWS can improve declarative but not procedural memories overnight. But when going beyond the tasks mentioned in Table 1, there are also many more studies contradicting the dual-process model. In a study by Gais and colleagues22 using the visual discrimination task, discrimination skills for example improved over early SWS, and not late REM sleep as expected for a procedural skill. Stickgold and colleagues23 found that SWS in the first quarter as well as REM sleep in the last quarter of the night served the memory consolidation of this procedural task, pointing to a multi-step process where SWS and REM fulfill different functions in turns. Altogether the picture emerges that some ingredients are still missing in order to arrive at a satisfactory model for sleep-dependent memory consolidation. In 2004 Smith and colleagues24 proposed a model where the complexity of a motor task defines whether S2 or REM sleep will be needed for memory consolidation. In that model tasks which simply require motor skill refinement such as the pursuit rotor or finger tapping task can be subserved by S2 sleep mechanisms. REM on the other hand will be needed if complex motor tasks requiring the learning of new rules—such as the mirror tracing task—are to be learned. Indeed, this model appears to account for some of the discrepancies seen in the literature. According to Smith and colleagues it is therefore not surprising that Genzel's easy finger tapping task did not respond to REMD. However, further refinements of the dual-process model are needed. Most of all we need to turn away from the macroscale simply studying sleep architecture changes and focus more on fine-grained analyses of specific sleep mechanisms such as the distinguishable slow ( 13 Hz) sleep spindles,25 PGO waves, REM densities, or actual slow wave activity (SWA); and also respect their often local experience-dependent nature. Furthermore, it has been shown that the regular occurrence of NREM-REM cycles, might be more crucial for retention of verbal material across sleep than a specific sleep stage per se.26 We should not forget to also pay more attention to the exact nature of the studied tasks, the individual traits of our subjects (age, sex, a priori abilities), or the exact behavioral tests performed (free or cued recall, delayed tests of performance, susceptibility to interference). In the end the story appears simply much more complex than can be explained by a simple model. Therefore, the scientific search must continue to unravel the many facets of sleep-dependent memory consolidation and come up with a more comprehensive theory integrating up to now divergent results.

SummaryOscillatory activities of the brain and heart show a strong variation across wakefulness and sleep. Separate lines of research indicate that non‐rapid eye movement (NREM) sleep is characterised by electroencephalographic slow oscillations (SO), sleep spindles, and phase–amplitude coupling of these oscillations (SO–spindle coupling), as well as an increase in high‐frequency heart rate variability (HF‐HRV), reflecting enhanced parasympathetic activity. The present study aimed to investigate further the potential coordination between brain and heart oscillations during NREM sleep. Data were derived from one sleep laboratory night with polysomnographic monitoring in 45 healthy participants (22 male, 23 female; mean age 37 years). The associations between the strength (modulation index [MI]) and phase direction of SO–spindle coupling (circular measure) and HF‐HRV during NREM sleep were investigated using linear modelling. First, a significant SO–spindle coupling (MI) was observed for all participants during NREM sleep, with spindle peaks preferentially occurring during the SO upstate (phase direction). Second, linear model analyses of NREM sleep showed a significant relationship between the MI and HF‐HRV (F = 20.1, r2 = 0.30, p < 0.001) and a tentative circular‐linear correlation between phase direction and HF‐HRV (F = 3.07, r2 = 0.12, p = 0.056). We demonstrated a co‐ordination between SO–spindle phase–amplitude coupling and HF‐HRV during NREM sleep, presumably related to parallel central nervous and peripheral vegetative arousal systems regulation. Further investigating the fine‐graded co‐ordination of brain and heart oscillations might improve our understanding of the links between sleep and cardiovascular health.

FV1. Physiological sleep alterations in heathy aging and mild cognitive impairment

TRANSVENOUS PHRENIC NERVE STIMULATION EFFECTIVELY TREATS CENTRAL SLEEP APNEA DURING BOTH RAPID AND NONRAPID EYE MOVEMENT SLEEP

The therapeutic use of noradrenergic drugs makes the evaluation of their effects on cognition of high priority. Norepinephrine (NE) is an important neuromodulator for a variety of cognitive processes and may importantly contribute to sleep-mediated memory consolidation. The NE transmission fluctuates with the behavioral and/or brain state and influences associated neural activity. Here, we assessed the effects of altered NE transmission after learning of a hippocampal-dependent task on neural activity and spatial memory in adult male rats. We administered clonidine (0.05 mg/kg, i.p.; n = 12 rats) or propranolol (10 mg/kg, i.p.; n = 11) after each of seven daily learning sessions on an 8-arm radial maze. Compared to the saline group (n = 9), the drug-treated rats showed lower learning rates. To assess the effects of drugs on cortical and hippocampal activity, we recorded prefrontal EEG and local field potentials from the CA1 subfield of the dorsal hippocampus for 2 h after each learning session or drug administration. Both drugs significantly reduced the number of hippocampal ripples for at least 2 h. An EEG-based sleep scoring revealed that clonidine made the sleep onset faster while prolonging quiet wakefulness. Propranolol increased active wakefulness at the expense of non-rapid eye movement (NREM) sleep. Clonidine reduced the occurrence of slow oscillations (SO) and sleep spindles during NREM sleep and altered the temporal coupling between SO and sleep spindles. Thus, pharmacological alteration of NE transmission produced a suboptimal brain state for memory consolidation. Our results suggest that the post-learning NE contributes to the efficiency of hippocampal-cortical communication underlying memory consolidation.

Brain oscillations such as alpha waves in relaxed wakefulness, sleep spindles and slow oscillations in sleep, or beta and gamma oscillations are important markers for the brain state and may also play functional roles in some cognitive processes. The different names indicate that they are considered as distinct entities, with frequency and topography being the features that distinguish the two. Sleep spindles in the human EEG, for instance, are oscillations in a frequency range between 11 and 16 Hz. They were shown to be related to the memory consolidating effects of sleep [1]. One often distinguishes between slow and fast spindles, which supposedly serve different functional purposes. Both types of spindles have not only different frequencies, but also different topographical distributions in the scalp EEG. The distinction between slow and fast sleep spindles in measured data is, however, by no means simple and uncontroversial. This is primarily due to the large amount of variation in the frequency of spindles, which not only vary across individuals but are also state-dependent (sleep stage and time in the night) [2]. Additionally, the delineation to other types of oscillations can be unclear – for instance between slow sleep spindles and frontal alpha oscillations. Similar problems occur for other brain oscillations, for instance in the analysis of delta and slow oscillations.

Sleep spindles during a nap correlate with post sleep memory performance for highly rewarded word-pairs