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

Abstract

Multiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations, are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, aging, 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 group-level differences between patients and controls (original N = 130) during the N2 stage. Then in the combined sample (N = 301 including 175 patients), we characterized patient-to-patient variability. We replicated all group-level mean differences and confirmed the high accuracy of our predictive model (area under the receiver operating characteristic curve [AUC] = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics. Although multiple clinical and cognitive factors were associated with NREM metrics, collectively they did not account for much of the general increase in patient-to-patient variability. The medication regimen was a greater contributor to variability. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on the sleep EEG; further, among patients, certain medications exacerbated these effects, in particular olanzapine. Collectively, our results point to a spectrum of N2 sleep deficits among SCZ patients that can be measured objectively and at scale, with relevance to both the etiological heterogeneity of SCZ as well as potential iatrogenic effects of antipsychotic medication.

Keywords: EEG analysis; biomarkers; psychiatric disorders; sleep spindles.

Graphical Abstract

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 waves 1 and 2, respectively, ordered from highest to the lowest (note: the directions of effect were consistent between waves 1 and 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-value < .05) increased variability in SCZ vs CTR (magenta bar) and increased variability in CTR vs SCZ (green bar); 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 interindividual variances of spectral power at FZ across frequencies during the N2 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 linear 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. Significant channels after the FDR adjustment for multiple comparisons (N of tests = 1368—differences in variance and means for SS and FS across 6 metrics across 57 channels—2 × 2 × 6 × 57) highlighted with a black rim.

Figure 4.

Sleep EEG associations with clinical and cognitive factors. A—the matrix shows t-scores from linear regressions (controlling for age and sex) between sleep features and clinical factors in SCZ. For multi-channel variables, we used the first PC (or all PCs explaining > 5% of total variance across channels × frequencies in case of PSD and PSI) derived separately for each metric and aligned all PCs by the sign of the difference between SCZ and CTR means (i.e. positive value means that clinical factor was associated with metric exhibiting more SCZ-like pattern and negative—CTR-like pattern). Stars mark associations with p-value < .01. The horizontal bar below the matrix illustrates SCZ-CTR differences in the corresponding variable: blue = decrease, red = increase, white = no difference; (B) same as A but 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-value < .05 are highlighted in orange and with p-value < .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-value < .05).

Figure 5.

Sleep EEG associations with medication use in SCZ. A—the matrix shows t-scores from a multiple linear regression where all medication groups were included as well as age, sex and illness duration. between sleep features and binarized medication use in SCZ (where each medication is included separately). For multi-channel variables, we used the first PC (or all PCs explaining >5% of total variance across channels × frequencies in case of PSD and PSI) derived separately for each metric and aligned all PCs by the sign of the difference between SCZ and CTR means (i.e. positive value means that clinical factor was associated with metric exhibiting more SCZ-like pattern and negative—CTR-like pattern). Stars mark associations with p-value < .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, sex and illness duration. Nominal significance (p-value < .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 using LASSO regression for SCZ group, compared to the original estimates (horizontal dashed lines); 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-value < .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 [27]).