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.

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).

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 (p_adj < 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 (p_adj < 0.05) lower power in SCZ (Figure 1, bottom left).

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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 p_adj < 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.

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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 p_adj < 0.05, max e.s. –0.82 SD with 18% reduction at CP3) and ISA (33 channels with p_adj < 0.05, max e.s. –0.96 SD at CPZ). Reductions for FS were topographically more limited for amplitude (23 central/parietal channels with p_adj < 0.05, max e.s. –0.62 SD with 15% reduction at Cz) and ISA (single channel, F1, p_adj = 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 p_adj < 0.05, max e.s. –0.98 SD at P5). In addition, FS chirp was more negative in SCZ (20 posterior channels with p_adj < 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).

SO abnormalities in SCZ

We detected SOs in N2 sleep using a previously described heuristic, based on relative/adaptive amplitude thresholds (see Materials and methods). Patients had an increased density of SOs across posterior channels (30 channels with p_adj < 0.05, max e.s. = 1.16 SD at O1, 18% increase), longer SO duration/wavelength (36 channels with p_adj < 0.05, max e.s. 1.92 SD at Cz, 18% increase) and flattened SO slope (44 channels with p_adj < 0.05, max e.s. –1.14 SD at Cz, 24% decrease) compared to controls (Figure 3, left panel). No differences between groups were observed in negative peak or peak-to-peak amplitude.

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SO parameters were not associated with total dose of antipsychotics but were affected by adjunctive medications: namely, increased SO duration and decreased slope were significantly associated with use of sedatives and tranquilizers; reduced SO slope was also linked to mood stabilizing and antiepileptic medication (uncorrected p < 0.01, Supplementary file 2). However, all SCZ-CTR group differences persisted in sensitivity analyses controlling for medication dosage (Supplementary file 3).

Of note, group differences in SO characteristics depended on the use of absolute versus relative amplitude thresholds for detecting SOs, as we have noted in other contexts (Djonlagic et al., 2021). We adopted relative amplitude thresholds for our primary analyses, as it maximized the magnitude of spindle/SO coupling observed across the whole sample. If using an absolute threshold, differences in SO density, duration, and slope were attenuated, whereas SO negative peak amplitude achieved significant group differences.

In addition, as SOs are most characteristic of N3 sleep, we performed secondary analysis that combined N2 and N3 sleep periods. While alterations in duration and slope of SOs remained significantly different between the groups, there were no differences in SO density applying either absolute or adaptive amplitude threshold for SO detection. A cycle specific analysis of N2 using adaptive threshold revealed that SO density alterations were more pronounced later in the night (Figure 2—figure supplement 1).

Intra-spindle frequency modulation: chirp/deceleration and SO phase

As noted, we observed a greater deceleration of FS (chirp) in SCZ. This effect was statistically independent of the reduction in FS density: for example, in a joint model, FS chirp and density at Pz were both independent predictors of SCZ (FS density t-score = –3.77, p = 0.0002 and chirp t-score = –3.17, p = 0.0015).

Given that spindles are temporally coupled with SO phase, as well as the fact that we observed SCZ/CTR differences for both FS chirp and preferential SO phase at spindle peak, we asked whether spindle deceleration was dependent on SO phase. We summarized spindle instantaneous frequency (estimated using a filter-Hilbert approach, see Materials and methods) as a function of spindle progression (quantiles of spindle duration) and also SO phase (in eighteen 20-degree bins).

Considering first only spindle progression, we observed the characteristic negative chirp (deceleration during the later portion of the spindle), for both FS and SS across all channels (Figure 4, top row). We further observed that spindle frequency was modulated by SO phase, consistently across channels but differently for FS and SS, likely reflecting the different temporal SO phase coupling of FS and SS (Figure 4, middle row): for SS, the highest instantaneous frequencies coincided with the SO positive peak, but earlier for FS (Figure 3, top right).

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Spindle progression and SO phase were related, reflecting SO/spindle coupling. We therefore additionally summarized spindle instantaneous frequency as a joint function of both spindle progression and SO phase, that is, 5 spindle progression quantiles x 18 SO-phase bins = 90 combinations averaged over all channels (Figure 4, lower row, Figure 4—figure supplement 1). This analysis suggested that spindle progression and SO phase have independent, largely additive effects on spindle frequency, as each line (SO phase bin) shows broadly similar chirp. Likewise, conditional on spindle progression, the SO modulation effect is still evident by the vertical offsets of the different lines (SO phase bins).

More formally, modeling SS instantaneous frequency as a cubic function of spindle progression and/or SO phase (CTRs only) showed an associated adjusted R² = 0.48 and 0.44, respectively, and R² = 0.95 if combined. For FS, SO phase explained relatively less of the variance in spindle frequency (R² = 0.19 versus R² = 0.72 for spindle progression; combined, R² = 0.94). That is, SO phase has a modulating effect on spindle frequency, independent of the typical deceleration/chirp, and this modulation was stronger for SS than FS. To distinguish this from temporal spindle/SO coupling, we refer to it as ‘phase/frequency modulation’, whereby the frequency of spindle oscillations depends on SO phase in a temporally fine-grained manner, which could be mediated via differential hyperpolarization associated with global up- and down-states.

To quantify SO/spindle phase/frequency modulation for each individual/channel, we calculated linear-circular correlations between binned instantaneous spindle frequency and SO phase (see Materials and methods) and observed reductions in SO phase/SS frequency modulation in SCZ (Figure 4, right panel).

Functional hyperconnectivity in SCZ during N2 sleep

To index functional connectivity we computed the phase slope index (PSI) (Nolte et al., 2008) between all channel pairs for frequencies from 0.5 to 22.5 Hz in 5 Hz windows (resulting from center frequencies from 3 to 20 Hz shifted by 1 Hz increments). For each channel, we summed the normalized PSI of all pairs involving that channel: higher absolute values for this net PSI metric indicate that on average the channel tended to be functionally connected to other channels in a way that was either leading (positive values) or lagging (negative values) other channels with respect to coherent phase differences.

Comparing SCZ and CTR, directional net connectivity followed a qualitatively similar pattern in both groups, whereby frontocentral channels showed positive and posterior channels showed negative net PSIs at 6–9 and 16–17 Hz (center frequencies); in contrast, for 11–13 Hz, centroparietal channels had positive and frontal channels had negative net PSIs (Figure 5, two top rows). Such connectivity patterns were observed at the channel level in net PSI as well as in pairwise PSI (Figure 5, bottom row displays net PSI for two exemplary channels – AFz, POz – and pairwise PSI between them).

Figure 5

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Despite broadly similar patterns, significant group differences in net PSI were evident for 6–8 and 10–13 Hz (shaded areas at the channel level plots in Figure 5, bottom row, and topoplots Figure 5, middle row). Within the 6–8 Hz interval, five frontal channels had higher net PSI in SCZ, whereas six occipital channels had more negative values (p_adj < 0.05, Figure 5, middle left, largest e.s. = 0.9 SD for F8 at 7 Hz). In both cases, this corresponded to greater connectivity in SCZ (i.e. larger absolute PSIs). More extensive group differences were observed at 11 Hz (Figure 5, middle right) with 21 anterior channels showing more negative and 24 posterior channels with more positive net PSIs (largest e.s. = 1.31 SD at P5). A similar pattern of differences was observed if pairwise PSI values were tested; again, both effects were consistent with stronger connectivity (higher absolute net PSI values) in SCZ.

We also tested group differences in sigma-band magnitude squared coherence – the most studied aspect of connectivity in sleep in SCZ so far (Wamsley et al., 2012; Sun et al., 2021; Markovic et al., 2020) – but found no evidence of significant alterations. Out of 1596 channel pairs, only 53 expressed group differences with uncorrected p-value < 0.05, which is lower than expected by chance (1596×0.05~80 significant pairs). We also specifically tested connections between the channels previously reported to have altered coherence in SCZ. For example, decreased coherence between C3 and O2 was reported in both Wamsley et al., 2012; Sun et al., 2021, but it was not different in our sample (e.s. –0.06 SD, nominal p-value = 0.98). Likewise, we observed no reduction in coherence between C3 and O1 (e.s. –0.05 SD, nominal p-value = 0.95) that was shown in both Sun et al., 2021; Markovic et al., 2020.

Dimension reduction to summarize sleep EEG alterations

To summarize the many EEG metrics (across domains but also channels and frequencies), we applied singular value decomposition (SVD), as previously applied for the sleep EEG where it was called principal spectral component (PSC) analysis (Djonlagic et al., 2021). PSC analysis typically yields a small number of orthogonal components (linear combinations of the original features) which can be used as new summary score metrics (see Materials and methods). For example, applied to the 4503 spectral power metrics (57 channels by 79 frequency bins between 0.5 and 20 Hz), 11 orthogonal components explained over 90% of the total variance (Figure 6). When based on power spectra from N2 sleep, many components (especially the larger ones) have relatively straightforward interpretations in terms of patterns of spectral or spatial profiles, or in terms of related sleep EEG metrics such as spindle parameters. Here, the first component indexed variation in total power – as evident from inspection of the component loadings (Figure 6) and strong correlation between the component scores and total power (0.5–20 Hz) at Cz yielding correlation coefficient of 0.95 and p-value = 1×10^–62 (note that the sign of PSCs is not defined).

Figure 6

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The second component, PSC2, was independent of total power (p-value = 0.87 for Cz in CTR group), reflecting the orthogonality of components, but was instead strongly associated (r = –0.78 and p-value = 4×10^–27) with the ~1/f spectral slope, as estimated by a linear regression of log power on log frequency (0.5–20 Hz). PSC3 captured individual differences in spindle amplitude (Figure 6) correlating negatively with SS amplitude at Cz (r = –0.31, p-value = 0.0004) but positively with FS amplitude (r = 0.32, p-value = 0.0003) at Cz. Importantly, unlike sigma-band power, this component effectively controls for differences in total power and spectral slope.

All three components – that collectively accounted for 69% of the total variation in power – had similar distributions in SCZ and CTR. However, component 4, which accounted for 5.8% of the total variance, was significantly associated with SCZ (p_adj = 1×10^–4 [Bonferroni corrected for 11 components], e.s. = 1.1 SD). This component captured coordinated SCZ-associated reductions in power at 2–5 and 12–14 Hz (Figure 6, topoplots corresponding to component 4 loadings across channels and frequencies), echoing our previous univariate analyses pointing to decreased power in sigma and delta frequency ranges (Figure 1). Unlike component 3, component 4 was more strongly associated with spindle density than amplitude, and more so for FS (SS density r = 0.3, p-value = 0.0005 and FS density r = 0.6, p-value = 2×10^–13, SS amplitude p-value > 0.05, FS amplitude p-value = 0.04).

Applying the same approach to the net PSI-based functional connectivity metrics, we reduced 1026 variables (57 channels, 18 frequency bins) to 22 independent components that each accounted for at least 1% of the total variance (76% in total). The first component explained 17.6% of the variance and was significantly different between SCZ and CTR (p_adj = 0.0081 [Bonferroni corrected for 22 comparisons], e.s. = 0.76 SD). This component captured spatially higher connectivity at 6–9 and 11–15 Hz, with higher values in SCZ indicating hyperconnectivity, recapitulating our previous results (Figure 5).

Thus, PSCs were able to capture SCZ-related alterations in spectral power and net PSI while reducing the dimensionality of the data. As well as providing a framework for multiple testing correction, these analyses can indicate which changes are independent versus linked: for example, here the reductions in delta and sigma-band power map onto the same component. As described below, we used selected components to determine how different domains of sleep microarchitecture were related to each other and to the wake ERP; we also used components to build joint models to predict disease status.

Joint model analysis of sleep EEG alterations suggests their non-redundancy

Given that we observed SCZ-related alterations across multiple facets of the sleep EEG, we tested the extent to which they were independent of each other. Taking the most significant channel for each associated metric (e.g. SS density at P7, or FS density at C1) yielded 16 sleep EEG variables (Figure 7); for each of these we queried whether conditioning on the remaining 15 variables (sequentially) accounted for the original association with SCZ, defined as a nominal p > 0.05. With a few exceptions, most of the effects were independent of each other. Briefly, FS amplitude effects were accounted for by either FS density, SS amplitude, or SO slope; FS ISA and duration were accounted for by FS density or SO density; FS/SO phase angle effects were accounted for by SO duration. All other effects (12 out of 16 sleep EEG metrics) remained significant no matter which other metric was covaried for.

Figure 7

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Pairwise analyses such as these do not provide a complete description of the multivariate structure of the data: nonetheless, these analyses clearly indicate that most metrics captured non-redundant features of the sleep EEG. Two important results emerge: (1) reductions in SS and FS density were independent of each other, (2) alterations in power, net PSI, SO, and most of the coupling parameters persisted over and above the spindle metrics (Figure 7). These multiple and (at least partially) independent associations may reflect distinct neurophysiology abnormalities underlying SCZ.

Altered auditory processing in SCZ is mostly independent of sleep abnormalities

In the evening prior to the sleep study, participants performed three ERP paradigms, all of which have previously been reported to be altered in SCZ: sensory gating P50 (Freedman et al., 2020), mismatch negativity (MMN) (Erickson et al., 2016), and auditory steady-state response (ASSR) (Thuné et al., 2016).

Presented with identical consecutive auditory stimuli (S1 and S2 delivered 500 ms apart), typical sensory gating leads to suppression of the second P50 evoked response (S2) relative to the first (S1). SCZ patients showed reduced gating (higher S2/S1 ratios) in 10 frontocentral channels (p_adj < 0.05, max e.s. = 1.06 SD at C2) (Figure 8, left). Consistent with previous reports (Atagun et al., 2020; Bramon et al., 2004), this was primarily driven by elevated S2 amplitudes in SCZ.

Figure 8

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The ASSR evaluates neural synchrony of the entrainment response elicited by 40 Hz auditory stimulation and is measured by evoked power and phase locking. Both measures were decreased in patients, predominantly in central and parietal channels (Figure 8). ASSR power differences were significant in 15 channels (p_adj < 0.05, max e.s. = –0.69 SD at C2), while phase locking differences were present in 20 channels (p_adj < 0.05, max e.s. = –0.73 SD at Pz).

MMN measures the response to infrequent deviant stimuli (auditory tones differing in a perceptual feature such as duration) compared to repeated standard stimuli, generally occurring ~150–250 ms after stimulus onset. MMN amplitudes were reduced in frontocentral channels in SCZ group (Figure 8), but only differences at FT7 survived multiple comparison correction (p_adj = 0.012, e.s. = −0.7 SD).

These results broadly replicate previous reports (Erickson et al., 2016; Freedman et al., 2020; Thuné et al., 2016) with patients showing reductions in sensory gating, MMN, and ASSR. Despite similar effect sizes for MMN and ASSR, the former showed an attenuated responses in terms of the number of channels surviving correction. Nevertheless, congruent with literature indicating central channels being the most affected in SCZ during auditory processing, all ERP metrics displayed nominally significant group differences at Cz: P50 ratio – e.s. = 0.92 SD, uncorrected p-value = 0.0002, MMN amplitude – e.s. = −0.49 SD, p-value = 0.008, ASSR power – e.s. = –0.56 SD, p-value = 0.008, and ASSR phase synchrony – e.s. = –0.62 SD, p-value = 0.002.

Notably, SCZ-associated wake ERP and sleep EEG metrics were independent of one another. Among 100 comparisons (4 wake by 25 sleep metrics at Cz), only one showed moderate dependency (defined here as an absolute correlation coefficient >0.2 and p < 0.01): P50 S2/S1 ratio and SO peak-to-peak amplitude (r = −0.25, p = 0.01). This correlation between sensory gating and SO amplitude may be attributable to the shared effect of total power, as it was not significant (p > 0.05) after controlling for total power during N2 sleep.

Independent replication of NREM sleep metric SCZ associations

Compiling whole-night sleep EEG recordings across three previously reported studies (Mylonas et al., 2020a; Wamsley et al., 2012; Manoach et al., 2010), we obtained a replication dataset of SCZ patients (N = 57) and controls (N = 59) (Supplementary file 5). All studies contained a common set of four channels (C3, C4, O1, and O2) although only N = 55 (26 SCZ, 29 CTR) had comparable hdEEG. Connectivity and SVD-based replication analyses were therefore restricted to that subset, whereas tests of spindles, SO, and coupling used the full four-channel replication sample. Associations between SCZ and several sleep parameters have been described previously for these studies individually, including spindle metrics, SO parameters, coupling, and spectral power (Mylonas et al., 2020a; Wamsley et al., 2012; Manoach et al., 2010; Demanuele et al., 2017). However, there has not been a comprehensive and joint analysis across datasets that is methodologically consistent with our sample (we refer to it as GRINS sample from this point and on). For example, the prior studies only examined FS and did not measure spindle chirp or global connectivity (Wamsley et al., 2012).

Out of 16 sleep EEG metrics (Figure 7, left, rows with different shades of blue background), group differences in 11 sleep metrics were replicated in the independent sample (Figure 7, left, EEG variables marked with a star). With respect to spindle metrics, most of primary findings from GRINS were replicated (nominal p < 0.05) in the independent dataset, the one exception being SS amplitude (GRINS C3: p < 0.001, e.s. = –0.78 SD, but replication C3: p = 0.37, e.s. = –0.19 SD) (Figure 9, two top rows, Supplementary file 6). We also replicated the association of increased SO density (GRINS O1 p < 0.001, e.s = 1.16 SD; replication O1, p = 0.003, e.s = 0.67 SD) and observed a consistent trend for SO duration (GRINS C3 p < 0.001, e.s = 1.08 SD; replication O1 p = 0.09, e.s = 0.52 SD). Differences in SO slope were not replicated, however. Alterations in coupling were replicated only for reduced SS/SO overlap (GRINS O1 p = 0.002, e.s. = −0.75 SD; replication C3 p = 0.002, e.s. = −0.83 SD (Figure 9, middle two rows, Supplementary file 6).

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With respect to spectral power, we replicated reductions in sigma power at C3 and C4, albeit with smaller effect sizes than seen in GRINS, whereas decreased delta power replicated only at C4 (Figure 9, bottom row left).

Replication analyses of net PSI connectivity yielded similar average patterns of directional connectivity as observed in GRINS (Figure 9—figure supplement 1). Of note, we also estimated mean net PSI topographies during REM sleep, which were also similar between GRINS and the replication cohort, but qualitatively different from NREM, suggesting that these connectivity profiles reflect functional connectivity patterns specific to NREM sleep, rather than arising obligatorily, for example, if reflecting only fixed aspects of brain morphology (Figure 9—figure supplement 1). We replicated our finding of elevated sigma-band (11 Hz center frequency) connectivity (uncorrected p < 0.01) at 11 Hz in SCZ (Figure 9, bottom row right), but the 7 Hz result in GRINS (Figure 5) was not replicated.

Building a joint predictive model in GRINS and testing transferability to an independent sample

Finally, as a form of omnibus replication analysis, we tested the overall transferability of the GRINS findings by building joint models based on multiple domains of sleep metrics to predict case versus control status, using logistic regression. We used the SVD/PSC approach to derive components based on the sleep metrics which were selected by the effect size of group differences and the number of channels with significant differences in the GRINS dataset (Figure 7, right): FS and SS density, amplitude, ISA, duration, and chirp; SO density, duration, and slope; spectral power and net PSI. We first extracted components from each domain (spindle, SO, spectral power, and connectivity) via four separate SVD/PSC analyses, retaining components which (1) explained more than 1% of total variance and (2) displayed significant group differences (uncorrected p-value < 0.01). We then fitted a model to the GRINS data based on 12 selected components (four spindle, three SO, three spectral power, and two connectivity components). These 12 predictors achieved high classification (SCZ versus CTR) performance with AUC of 0.94, R² = 0.69 (corrected for overfitting using bootstrapped validation, although predictors were, of course, selected based on their univariate association with disease) (Figure 10, left). If taken separately, spindle-based components were the most critical for correct classification (AUC = 0.92, R² = 0.63 for spindle components versus SO components AUC = 0.87, R² = 0.5; PSD components AUC = 0.84, R² = 0.43; PSI components AUC = 0.74, R² = 0.22). Prediction based on spindle density alone (SS density at P7 and FS density at FC2) achieved an AUC of 0.8, R² = 0.35.

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We evaluated the GRINS-derived model in the hdEEG replication cohort (26 cases, 29 controls, Supplementary file 5), to see whether it was broadly transferable to an independent and geographically/ethnically distinct population. We projected replication data into the GRINS-derived component space (see Materials and methods), that is, ensuring that ‘component 4’, for example, in the replication cohort indexed the same variability as in GRINS. Based on these projected components, we observed a full-model AUC of 0.73 in the replication sample (Figure 10, right). In contrast, prediction based on spindle density alone (SS density at P7 and FS density at FC2) achieved an AUC of only 0.64 in this independent sample (Figure 10—figure supplement 1).

We conducted a comprehensive study of NREM and wake abnormalities in SCZ using hdEEG recordings, basing our investigations on the previously reported spindle deficits (Ferrarelli et al., 2010; Mylonas et al., 2020a; Wamsley et al., 2012; Manoach et al., 2014) and extending to broader, multiple domains of NREM sleep macro- and microarchitecture, as well as wake ERPs implicated in thalamocortical functioning and SCZ (Ferrarelli and Tononi, 2010).

We provided statistically compelling evidence of a core set of spindle deficit in patients, consolidating prior reports based primarily on small sample sizes and varying methodologies. In addition, we demonstrated deficits in patients across multiple domains of NREM sleep including SO density and morphology, spectral power, SO/spindle coupling, and global connectivity, and replicated disrupted auditory processing during wake. Our findings in NREM were largely replicated in an independent and demographically distinct sample of patients and controls. A multivariate predictive model trained in GRINS using multiple NREM metrics was broadly transferable to the replication sample, and outperformed the model based on spindle density alone. Importantly, NREM sleep metrics were largely independent of one another, and did not correlate with wake ERP measures, suggesting multiple distinct thalamic and thalamocortical mechanisms underlying disease abnormalities. Taken together, these results support the utility of biomarkers that are based on a precise dissection of the sleep EEG, with the potential for risk and patient stratification that will ultimately map to distinct neurophysiological deficits.

Despite showing broadly comparable gross sleep macro-architecture in terms of stage duration and TST, we observed marked spindle impairments in SCZ, not associated with antipsychotic dose or adjunctive medication. Although we observed longer sleep latency in patients, it is not clear how generalizable this finding will be given the context of sleeping at the sleep center following ERP testing. Only a few studies have investigated SS and FS separately in SCZ (Ferrarelli et al., 2010; Schilling et al., 2017) despite multiple reports of their distinct topography, functional specificity, and SO coupling (Purcell et al., 2017; Cox et al., 2017; Mölle et al., 2011). In addition to finding that FS and SS density were largely uncorrelated and independently associated with SCZ, while reductions in the density and amplitude of both FS and SS were found in patients, the decrease in density was more prominent for FS, while the decrease in amplitude was more prominent for SS. Furthermore, despite observing similar average spindle deceleration (chirp) in both FS and SS, only FS showed more pronounced deceleration in patients. Although spindle chirp is a relatively novel metric, it has been shown that such intra-spindle frequency deceleration is a basic property of both SS and FS (Andrillon et al., 2011; Schönwald et al., 2011). While the mechanisms of how chirp is regulated are not established yet, our findings of altered chirp in SCZ should motivate future studies to investigate this question. In addition to differences in topography, one key distinction between SS and FS is in their coupling with SOs. One study demonstrated intact SO/FS temporal coupling in patients that was associated with overnight consolidation in motor task performance, but did not study SS (Demanuele et al., 2017). In GRINS, we observed reduced SO/spindle overlap in SCZ, but only for SS. Similarly, phase-frequency modulation was altered in SCZ only for SS. Spindles are generated in the TRN and modulated by thalamocortical connections. Interactions between different sub-regions of TRN and higher order or sensory thalamic nuclei have been described molecularly and functionally (Li et al., 2020; Vantomme et al., 2020), and may underlie frequency-dependent differences in spindle mechanisms. Alternatively, it is possible that unidentified spindle oscillators outside of TRN in the thalamocortical circuits contribute to SS and FS distinctions. Additional neurobiological studies are warranted to clarify the source of these frequency differences in spindles. Regardless of their origin, SS density and amplitude had more evident associations with SCZ symptomatology (negative symptoms and disorganized thoughts) compared to FS, although this result has yet to be replicated in other samples. Considering that SS and FS properties were largely independent of each other, these distinct patterns of association with SCZ point to differences in their thalamocortical generation and propagation mechanisms and functional affiliations (e.g. relations to sleep-dependent memory consolidation).

Spectral power was reduced in SCZ in sigma and delta/theta frequencies. The former finding was expected due to spindle deficits and is consistent with previous studies with smaller samples (Ferrarelli et al., 2007; Ferrarelli et al., 2010; Wamsley et al., 2012; Manoach et al., 2010; Manoach et al., 2014; Göder et al., 2006). Spectral power in slow (0.5–1 Hz) or delta (1–4 Hz) bands has been used as a proxy for SO activity. Both unaltered and reduced slow/delta power have been reported in SCZ (Ferrarelli et al., 2010; Göder et al., 2015; Wamsley et al., 2012; Manoach et al., 2010; Manoach et al., 2014; Göder et al., 2006; Keshavan et al., 1998). Few studies considered other frequencies; two studies that did test multiple frequency bands reported decreased theta power in SCZ (Manoach et al., 2010; Manoach et al., 2014), which is consistent with our findings.

Prior reports considering discrete SO events (0.5–2 Hz) during NREM have also been inconsistent, with some studies supporting reduced density/amplitude of SO (Keshavan et al., 1998; Hoffmann et al., 2000; Kaskie et al., 2019; Sekimoto et al., 2011) but others showing unaltered SOs (Ferrarelli et al., 2010; Mylonas et al., 2020a; Demanuele et al., 2017; Bartsch et al., 2019). Underpowered samples, methodological differences, and/or medication effects may account for these apparent discrepancies (Castelnovo et al., 2018). Using the current GRINS sample – which is at least twice as large as most of the previous studies – we observed SO alterations in patients, including increased SO density, duration, and flatter slope. Only SO density differences replicated here, although it is plausible that the limited four-channel montage was a limitation. Caveats include that we only considered SOs during N2 sleep, potential methodological effects of thresholding choices, and associations between SO metrics and adjunctive medication use. Overall, although our findings suggest that some form of SO abnormality is plausible in SCZ, these effects are not as large or robust as the spindle deficit, despite the putative role of SO in orchestrating spindles (Staresina et al., 2015).

Global patterns of functional connectivity during N2 sleep were also affected in SCZ. While there is substantial evidence of abnormal waking connectivity as a hallmark of SCZ from fMRI (Dong et al., 2018; van den Heuvel and Fornito, 2014) and M/EEG studies (Mackintosh et al., 2021; Maran et al., 2016), connectivity findings in sleep are more scarce and restricted to coherence in spindle (Wamsley et al., 2012; Sun et al., 2021; Markovic et al., 2020) and slow/delta frequency bands (Bartsch et al., 2019), mostly pointing to reduced connectivity in patients, although there is also evidence of increased coherence (Markovic et al., 2020). To the best of our knowledge, only two prior studies have evaluated coherence at other frequencies during sleep. One study reported increased coherence across classical frequency bands in childhood onset SCZ (Markovic et al., 2021); another study investigated NREM coherence in beta and gamma frequencies in a very limited sample yielding null results (Yeragani et al., 2006).

The typically used magnitude squared coherence metric is very sensitive to volume conduction effects, precluding accurate estimation of true connectivity between channels (Sakkalis, 2011). Here, we used the PSI (Nolte et al., 2008), which mitigates volume conduction and shared reference issues. Summarized by channel, our results revealed a distinct pattern of directed connectivity across 3–20 Hz that was also seen in the ESZ replication dataset. We observed increased connectivity (higher absolute net PSIs) in SCZ for frequencies at 6–8 and 11–14 Hz. While increases in 6–8 Hz connectivity is partially congruent with a previous report (Markovic et al., 2021), hyperconnectivity for sigma appears to be the opposite of previous findings of decreased sigma coherence in SCZ. It is important to note, however, that these findings do not necessarily contradict one another, but rather indicate that such decrease in coherence is mostly driven by a zero-lagged connectivity component, which would be highly influenced by volume conduction and attenuated in PSI estimates. When pairwise sigma magnitude squared coherence was explicitly tested in GRINS, we found no evidence of decreased connectivity. Our results of sigma hyperconnectivity in SCZ as indexed by net PSI was replicated in the ESZ cohort, suggesting its robustness.

We clarified that alterations in SO, spectral power, and connectivity represented largely independent phenomena from the sleep spindle deficit: a model based on multiple domains better classified disease status in an independent sample, compared to spindle density alone. Although still distant from diagnostic application in clinical settings, such results suggest that alterations in these aspects of sleep electrophysiology convey additional information on the neurobiological underpinnings of SCZ and may be helpful in stratifying patients with SCZ or distinguishing them from other neuropsychiatric conditions for which spindle density alterations have been reported (Farmer et al., 2018; Ritter et al., 2018).

In addition to spindle deficits, we report novel EEG alterations in SCZ patients including intra-spindle frequency change (chirp), overlap between slow spindles and SO, and increase in PSI indicative of functional hyperconnectivity. While the neurobiology of these metrics is yet to be studied extensively, there are clues to the function of these EEG features. For example, TRN neurons gradually hyperpolarize and fire with fewer action potentials upon repeated bursting which may underlie the chirp during the ‘waning’ of spindles (Barthó et al., 2014). Therefore, chirp could reflect intrinsic biophysical properties of TRN and thalamocortical neurons during this phase. Similarly, a recent study suggested that spindles nested within SO play a causal role in learning and are important for offline processing (Kim et al., 2019; Latchoumane et al., 2017), suggesting SCZ patients may be impaired during such reactivations of waking experience. The full relevance and functional implications of these findings in disease pathophysiology require further investigation, however.

With respect to the wake metrics, we broadly confirmed the previously reported ERP deficits in SCZ (Erickson et al., 2016; Freedman et al., 2020; Thuné et al., 2016; Ferrarelli and Tononi, 2010; Atagun et al., 2020; Bramon et al., 2004). Specifically, we observed impaired sensory gating (indexing deficits in filtering repetitive sensory stimuli), reduced MMN (reflecting abnormal automatic change detection process in auditory cortex), and decreased ASSR to 40 Hz auditory stimulation (associating with neural circuit abnormalities in this disorder) (Erickson et al., 2016; Freedman et al., 2020; Thuné et al., 2016). Leveraging the fact that our samples had both sleep and wake EEG recordings, we tested whether sleep spindle deficits and wake abnormalities were associated, as might be expected if they were to both depend on intact function of the same thalamocortical circuits (Ferrarelli and Tononi, 2010; Pratt and Morris, 2015). In sleep, the TRN is crucial for spindle generation, whereas in wake, the TRN is dynamically involved in controlling thalamic sensory gain and in gating less important sensory information (Halassa et al., 2014; Wimmer et al., 2015). While very few direct reports link the TRN to the neurophysiological bases of auditory EEG potentials listed above (Lakatos et al., 2020), there is a general agreement that efficient sensory processing inherently relies on precise coordination between thalamic nuclei and auditory cortex, and the TRN is a modulator between cortex and thalamus (Ferrarelli and Tononi, 2010; Fernandez and Lüthi, 2020; Kimura et al., 2012). Only a few studies to date have investigated the links between wake and sleep cortical alterations in SCZ. One recent study demonstrated that increased functional connectivity as measured by MRI between thalamus and somatosensory and motor cortex in wake inversely correlated with spindle density, suggesting deficient TRN inhibition as a plausible common mechanism (Baran et al., 2019). However, our findings did not point to strong linkages between individual differences in sleep metrics and wake auditory processing abnormalities. Furthermore, in response to a recently reported link between spindle deficits and auditory hallucinations in SCZ (Sun et al., 2021), we found that reduction in spindle density was not restricted to the subset of patients with auditory hallucinations. In summary, our present results suggest that the sleep EEG carries non-redundant information on thalamocortical circuits beyond the established ERP paradigms.

Our study is not without limitations. Although previous studies reported that spindle differences between SCZ and CTR were observed similarly during the first and the second naps/nights of recording (Mylonas et al., 2020a; Mylonas et al., 2020b), we could not directly test the first-night effect due to the absence of the adaptation night in our sample. We also could not directly assess the likelihood and impact of circadian disruption in SCZ, as our cohort was composed of inpatients whose sleep-wake patterns were constrained by hospital-imposed schedules. In our sample, almost all patients with SCZ were prescribed antipsychotic or adjunctive medication and some used more than one type of antipsychotic. While there is evidence of spindle deficits in unmedicated patients – and we also controlled for medication dosage – we cannot completely exclude medication effects in observed EEG alterations. Another potential caveat relates to difficulties interpreting functional connectivity at a sensor level. Some issues, for example, volume conduction, were partially mitigated by the choice of connectivity metric, but accurate source reconstruction may facilitate interpretations with respect to precise cortical topography.

In summary, this work reinforces previous findings of a spindle deficit as a highly reproducible trait associated with SCZ pathophysiology across ethnically/racially distinct samples, as well as pointing to notable distinctions between SS and FS in patients. Although the spindle density deficit was the most marked, our analyses point to multiple domains of abnormalities of the NREM sleep and wake EEG that were independently associated with SCZ, potentially indexing different facets of thalamic and cortical dysfunction. Elucidating how these neurophysiological abnormalities co-exist and interact, and which aspects of SCZ pathophysiology they reflect is critical for a better understanding of SCZ etiology and heterogeneity. Analyzing these measures in genetically informative designs may reveal associations with disease risk, and lead to knowledge of the underlying molecular and neural circuits to reveal novel therapeutic targets to mitigate relevant abnormalities. Future research, including longitudinal characterization across the natural history of SCZ, will be needed to dissect which of these EEG features index different behavioral and cognitive manifestations of SCZ, in order to stratify patient populations, and understand their temporal development. Our findings support that sleep-based biomarkers have the potential to provide powerful phenotyping in animal, genetic, and clinical research aimed at developing novel therapeutic and preventive strategies for SCZ.

Anonymized individual-level data for the derived EEG metrics, disease status, and demographics are available in a Dryad archive (doi:10.5061/dryad.j0zpc86h4). R scripts to reproduce the key figures are available at this GitLab repository: https://gitlab-scm.partners.org/zzz-public/grins/https://gitlab-scm.partners.org/zzz-public/grins/ (copy archived at swh:1:rev:6e5a8a8586f1a8b2a0996cf4ed55fff1e31c3333).

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Author details

1. Nataliia Kozhemiako

   Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, United States

   Contribution

   Formal analysis, Investigation, Validation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Jun Wang and Chenguang Jiang

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6450-4959

2. Jun Wang

   The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China

   Contribution

   Data acquisition, Data curation, Formal analysis, Investigation, Resources, Writing – review and editing

   Contributed equally with

   Nataliia Kozhemiako and Chenguang Jiang

   Competing interests

   No competing interests declared

3. Chenguang Jiang

   The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China

   Contribution

   Data acquisition, Data curation, Formal analysis, Investigation, Project administration, Writing – review and editing

   Contributed equally with

   Nataliia Kozhemiako and Jun Wang

   Competing interests

   No competing interests declared

4. Lei A Wang

   Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Guanchen Gai

   The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China

   Contribution

   Data acquisition, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Kai Zou

   The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China

   Contribution

   Data acquisition, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Zhe Wang

   The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China

   Contribution

   Data acquisition, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Xiaoman Yu

   The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China

   Contribution

   Data acquisition, Data curation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Lin Zhou

   Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   Data curation, Resources

   Competing interests

   No competing interests declared

10. Shen Li

    Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, United States

    Contribution

    Formal analysis, Writing – review and editing

    Competing interests

    No competing interests declared

11. Zhenglin Guo

    Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States

    Contribution

    Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

12. Robert Law

    Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, United States

    Contribution

    Formal analysis, Writing – review and editing

    Competing interests

    No competing interests declared

13. James Coleman

    Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States

    Contribution

    Formal analysis, Writing – review and editing

    Competing interests

    No competing interests declared

14. Dimitrios Mylonas

    Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, United States

    Contribution

    Data curation, Validation, Writing – review and editing

    Competing interests

    No competing interests declared

15. Lu Shen

    Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

16. Guoqiang Wang

    The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China

    Contribution

    Data acquisition, Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

17. Shuping Tan

    Huilong Guan Hospital, Beijing University, Beijing, China

    Contribution

    Data curation, Methodology, Study design, Writing – review and editing

    Competing interests

    No competing interests declared

18. Shengying Qin

    Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8458-5960

19. Hailiang Huang

    1. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States
    2. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, United States

    Contribution

    Conceptualization, Writing – review and editing

    Competing interests

    No competing interests declared

20. Michael Murphy

    Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, United States

    Contribution

    Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

21. Robert Stickgold

    1. Beth Israel Deaconess Medical Center, Boston, United States
    2. Department of Psychiatry, Harvard Medical School, Boston, United States

    Contribution

    Conceptualization, Methodology, Validation, Writing – review and editing

    Competing interests

    No competing interests declared

22. Dara Manoach

    Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, United States

    Contribution

    Conceptualization, Validation, Writing – review and editing

    Competing interests

    No competing interests declared

23. Zhenhe Zhou

    The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China

    Contribution

    Conceptualization, Data curation, Project administration, Study design, Supervision, Writing – review and editing

    Contributed equally with

    Wei Zhu, Mei-Hua Hal, Shaun M Purcell and Jen Q Pan

    For correspondence

    zhouzh@njmu.edu.cn

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1334-8335

24. Wei Zhu

    The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China

    Contribution

    Conceptualization, Data curation, Project administration, Study design, Writing – review and editing

    Contributed equally with

    Zhenhe Zhou, Mei-Hua Hal, Shaun M Purcell and Jen Q Pan

    Competing interests

    No competing interests declared

25. Mei-Hua Hal

    Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, United States

    Contribution

    Conceptualization, Formal analysis, Methodology, Study design, Supervision, Writing – review and editing

    Contributed equally with

    Zhenhe Zhou, Wei Zhu, Shaun M Purcell and Jen Q Pan

    For correspondence

    mhall@mclean.harvard.edu

    Competing interests

    No competing interests declared

26. Shaun M Purcell

    1. Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
    2. Department of Psychiatry, Harvard Medical School, Boston, United States

    Contribution

    Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Study design, Supervision, Writing – original draft, Writing – review and editing

    Contributed equally with

    Zhenhe Zhou, Wei Zhu, Mei-Hua Hal and Jen Q Pan

    For correspondence

    smpurcell@bwh.harvard.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7402-5812

27. Jen Q Pan

    Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, United States

    Contribution

    Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Study design, Supervision, Writing – original draft, Writing – review and editing

    Contributed equally with

    Zhenhe Zhou, Wei Zhu, Mei-Hua Hal and Shaun M Purcell

    For correspondence

    jpan@broadinstitute.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0767-086X

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