A spectrum of altered non-rapid eye movement sleep in schizophrenia

Abstract

Background: Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations (SO), are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses which treat all patients as a unitary set, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, illness duration and ageing, cognitive profiles and medication regimens. Using newly collected high density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported (Kozhemiako et. al, 2022) group-level mean differences between patients and controls (original N=130). Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability in NREM neurophysiology.

Results: We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (Area Under the ROC Curve, AUC = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics, with patterns only partially recapitulating those for group-level mean differences. Although multiple clinical and cognitive factors were associated with NREM metrics including spindle density, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater (albeit still partial) contributor to variability, although original group mean differences persisted after controlling for medications. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on an independent model of ageing and the sleep EEG.

Conclusion: We demonstrated robust and replicable alterations in sleep neurophysiology in individuals with SCZ and highlighted distinct patterns of effects contrasting between-group means versus within-group variances. We further documented and controlled for a major effect of medication use, and pointed to greater age-related change in NREM sleep in patients. That increased NREM heterogeneity was not explained by standard clinical or cognitive patient assessments suggests the sleep EEG provides novel, nonredundant information to support the goals of personalized medicine. Collectively, our results point to a spectrum of NREM sleep deficits among SCZ patients that can be measured objectively and at scale, and that may offer a unique window on the etiological and genetic diversity that underlies SCZ risk, treatment response and prognosis.

Figure 1.. Replication of wave 1 sleep neurophysiology alterations in wave 2 of the GRINS cohort.

A – absolute effect sizes of group-level mean differences in wave 1 and wave 2 respectively, ordered from highest to the lowest (note: the directions of effect were consistent between wave 1 and wave 2). Metrics with significant combined sample SCZ-CTR clusters are displayed as topoplots; the EEG channel with the highest absolute t-score was selected for top bar plot. Metrics are ordered from left to right based on decreasing effect size in wave 1. B – SCZ-CTR classification in wave 2 (Right) using a predictive model derived from wave 1 data (Left).

Figure 2.. Spindle density deficits in SCZ depend on SO-coupling status.

The topoplots represent the group differences between SCZ and CTR in spindle density computed based on all, coupled with SOs and uncoupled with SOs spindles. The first row displays the results for slow spindles (SS), while the second row shows the results for fast spindles (FS).

Figure 3.. Increased variability in the SCZ group across multiple sleep estimates.

A – Left : the percentage of sleep variables with significantly (p < 0.05) increased variability in SCZ vs CTR; Right: the percentage of sleep variables with increased variability (light shade) and those with both increased variability and altered means (dark shade) across seven domains. B – visualization of differences in means and inter-individual variances of spectral power at FZ across frequencies during the NREM2 stage; vertical lines represent 12 and 14 Hz. C – examples of sleep variables with the difference in variance (Bartlett’s test p-value in the title with effects of age and sex regressed out prior statistical comparison), but not in means (effect size and p-value from logistic regression controlling for age and sex inside the graph); in mean, but not in variance; and in both mean and variance. D – topoplots illustrate the distinct profiles of between-group differences in variance (top row) versus mean (bottom row) all channels for FS and SS metrics

Figure 4.. Sleep EEG associations with clinical and cognitive factors.

A – the left bar plot shows the percentage of all sleep variables still exhibiting higher variance in either the SCZ or CTR group after the effects of all clinical factors were simultaneously regressed out (for the SCZ group only), compared to the original estimates (horizontal dashed line); the right bar plot is similar but is stratified by domain (the denominator for percentages is the total number of variables in the domain); original estimates are marked by triangles. B – same as A but controlling for the cognitive (instead of clinical) variables. C – the graph illustrates the association between the total MCCB score and spectral power across all channels (lines) in the frequency range of 0.5–20 Hz. Channels and frequencies displaying association above the nominal significance threshold of p<0.05 are highlighted in orange and with p <0.01 in red. D – topoplots represent channels where the association between spindle density and total MCCB score (top two rows) or PANSS scores (bottom two rows) were nominally significant (p <0.05).

Figure 5.. Sleep EEG associations with medication use in SCZ.

A – the matrix shows t-scores from linear regressions (controlling for age and sex) between sleep features and binarized medication use in SCZ (where each medication is included separately). For multi-channel variables, the largest t-score among the channels is presented and stars mark associations with p < 0.01. The horizontal bar below the matrix illustrates SCZ-CTR differences in the corresponding variable: blue = decrease, red =increase, white =no difference; B – examples of medication effects on FS density, SS density, SO density, FS coupling magnitude and N2 proportion. Each arrow indicates an effect (t-score) of a certain medication on a sleep variable from a multiple linear regression where all medication groups were included as well as age and sex. Nominal significance (p<0.05) is marked by a white star inside the arrows. For multi-channel metrics, the largest effect across all channels is presented. The horizontal dashed line indicates the effect size of the group difference between SCZ and CTR in the corresponding metric (at the channel with the largest effect size for multi-channel variables). C – sedatives and tranquilizers effect on spectral power in N2 and REM across frequencies (solid lines) in comparison to SCZ-CTR differences (dashed lines) at Cz channel D – left bar plot shows the percentage of all sleep variables still with higher variance in SCZ or CTR group after effects of all common medications have been simultaneously regressed out for SCZ group, compared to the original estimates (horizontal dashed line); the right bar plot is similar but stratified by domain (denominator for percentages is the number of variables in the domain); original estimates are marked by triangles.

Figure 6.. Fast spindle density shows differential age-related decline in SCZ.

A – the topoplots show the associations between FS density and age, in either a case-only (left plot) or control-only (middle plot) analysis, and the interaction P-values from the joint models (right plot); associations P < 0.01 are marked by dark circles; B – scatter plots showing FS density at F5 as a function of age separately in cases and controls. C – scatter plots showing predicted and observed ages separately in cases and controls; age prediction was based on a modified version of the model described by Sun et al (2019).