Investigation of sleep spindle activity and morphology as predictors of neurocognitive functioning in medicated patients with schizophrenia

Sleep spindles as neurophysiological biomarkers of schizophrenia

Schizophrenia (SCZ) is a complex psychiatric disorder characterized by a wide range of clinical symptoms, including disrupted sleep. In recent years, there has been growing interest in assessing alterations in sleep parameters in patients with SCZ. Sleep spindles are brief (0.5-2 s) bursts of 12- to 16-Hz rhythmic electroencephalogram (EEG) oscillatory activity occurring during non-rapid eye movement (NREM) sleep. Spindles have been implicated in several critical brain functions, including learning, memory and plasticity, and are thought to reflect the integrity of underlying thalamocortical circuits. This review aims to provide an overview of the current research investigating sleep spindles in SCZ. After briefly describing the neurophysiological features of sleep spindles, I will discuss alterations in spindle characteristics observed in SCZ, their associations with the clinical symptomatology of these patients and their putative underlying neuronal and molecular mechanisms. I will then discuss the utility of sleep spindle measures as predictors of treatment response and disease progression. Finally, I will highlight future directions for research in this emerging field, including the prospect of utilizing sleep spindles as neurophysiological biomarkers of SCZ.

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing

Sleep spindles are critical for memory consolidation and strongly linked to neurological disease and aging. Despite their significance, the relative influences of factors like sleep depth, cortical up/down states, and spindle temporal patterns on individual spindle production remain poorly understood. Moreover, spindle temporal patterns are typically ignored in favor of an average spindle rate. Here, we analyze spindle dynamics in 1008 participants from the Multi-Ethnic Study of Atherosclerosis using a point process framework. Results reveal fingerprint-like temporal patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night. We observe increased timing variability with age and distinct gender/age differences. Strikingly, and in contrast to the prevailing notion, individualized spindle patterns are the dominant determinant of spindle timing, accounting for over 70% of the statistical deviance explained by all of the factors we assessed, surpassing the contribution of slow oscillation (SO) phase (~14%) and sleep depth (~16%). Furthermore, we show spindle/SO coupling dynamics with sleep depth are preserved across age, with a global negative shift towards the SO rising slope. These findings offer novel mechanistic insights into spindle dynamics with direct experimental implications and applications to individualized electroencephalography biomarker identification.

Unveiling the Associations between EEG Indices and Cognitive Deficits in Schizophrenia-Spectrum Disorders: A Systematic Review

Cognitive dysfunctions represent a core feature of schizophrenia-spectrum disorders due to their presence throughout different illness stages and their impact on functioning. Abnormalities in electrophysiology (EEG) measures are highly related to these impairments, but the use of EEG indices in clinical practice is still limited. A systematic review of articles using Pubmed, Scopus and PsychINFO was undertaken in November 2021 to provide an overview of the relationships between EEG indices and cognitive impairment in schizophrenia-spectrum disorders. Out of 2433 screened records, 135 studies were included in a qualitative review. Although the results were heterogeneous, some significant correlations were identified. In particular, abnormalities in alpha, theta and gamma activity, as well as in MMN and P300, were associated with impairments in cognitive domains such as attention, working memory, visual and verbal learning and executive functioning during at-risk mental states, early and chronic stages of schizophrenia-spectrum disorders. The review suggests that machine learning approaches together with a careful selection of validated EEG and cognitive indices and characterization of clinical phenotypes might contribute to increase the use of EEG-based measures in clinical settings.

Sleep disturbances and cognitive impairment in schizophrenia spectrum disorders: a systematic review and narrative synthesis

Individuals with schizophrenia spectrum disorders (SSD) experience frequent sleep disturbances in addition to enduring cognitive impairments. The purpose of the present review was to systematically summarise our current understanding of the association between sleep disturbances and cognition in SSD. Through this, it was aimed to identify features of disturbed sleep that are reliably associated with cognitive deficits in SSD and identify the gaps within the current literature that require future investigation. Eighteen relevant studies were identified following a two-stage screening process. Following a structured narrative synthesis of key study components, no clear and consistent pattern emerged. Considerable methodological variability was present amongst the reviewed studies. Although some broad consistencies were identified, such as associations between sleep spindle density and sleep-dependent memory consolidation, the overall pattern of results lacked a cohesive composition due to the diverse list of sleep parameters and cognitive domains investigated, as well as a lack of replication. Additional research is needed before more definitive remarks can be made regarding the influence of sleep disturbances on cognitive function in SSD.

U-Sleep: resilient high-frequency sleep staging

Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging ( sleep.ai.ku.dk ). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.

Sleep quality and cognitive impairments in remitted patients with schizophrenia in Nigeria

BACKGROUND

Despite the ubiquity of sleep disturbance in schizophrenia, it has generally been overlooked as a potential contributor to cognitive impairments. The main aim of this study was to find out if impaired sleep quality contributes to cognitive impairments in patients with a diagnosis of schizophrenia who are in remission.

METHODS

The study was conducted at the University College Hospital, Ibadan and State Hospital, Ibadan, Nigeria. The Pittsburgh Sleep Quality Index (PSQI) and Screen for Cognitive Impairment in Psychiatry (SCIP) were applied in this cross-sectional study, to all consecutive and consenting remitted outpatients with schizophrenia (N=130). Other instruments such as Hamilton Depression Rating Scale (HDRS), the Positive and Negative Syndrome Scale (PANSS), sociodemographic and clinical measures were also applied.

RESULTS

There were 130 participants made up of 69 females (53.1%) and 61males(46.9%). The mean age of the participants was 38.5±9.1 years. The prevalence of poor sleep quality in remitted patients with schizophrenia was 56.9%. Sleep quality was significantly negatively correlated with Verbal Learning Test-Immediate (VLT-I) (r(128)=-.18, P=.044) and Verbal Learning Test-Delayed (VLT-D) (r(128)=-.18, P=.037). The variables that independently predicted cognitive functioning were the VLT-I, odds ratio (OR) 0.66; 95% confidence interval ((CI) 0.49-0.88) and education (OR) 0.61;(CI) 0.40- 0.92).

CONCLUSION

Poor subjective sleep quality measured by the PSQI is linked to cognitive impairment in remitted patients with schizophrenia. We suggest that sleep quality in remitted patients with a diagnosis of schizophrenia should receive better attention by physicians.

Sleep, sleep spindles, and cognitive functions in drug-naive patients with first-episode psychosis

STUDY OBJECTIVES

Various lines of clinical findings have suggested abnormalities in macro- or microstructural parameters of sleep in patients with schizophrenia. Meanwhile findings are inconclusive due to some confounding factors, such as the heterogeneity of the disorder, drug regimen, and duration of the illness. There are a few studies in the literature that have been conducted on drug-free patients with first-episode psychosis (FEP). Based on this knowledge, we aimed to explore sleep characteristics, sleep spindles, and neuropsychological profiles of the drug-naive patients with FEP.

METHODS

The study sample consisted of 21 drug-naive patients with FEP and 21 healthy participants. Polysomnography recordings were conducted for 2 subsequent nights. A neuropsychological test battery was administered for assessing cognitive functions. The Positive and Negative Syndrome Scale was applied to measure symptom severity of the patients. Spindle detection was performed visually.

RESULTS

According to the results of the study, the patient group's percentage of stage N2 sleep and sleep efficiency index was lower than in the control group. Among sleep spindle parameters, spindle density was found to be reduced in the patient group. The results of neuropsychological tests measuring executive functions, learning, and memory support the idea that there is a global cognitive deterioration from the early course of the disorder. In the psychotic group, negative symptoms were negatively correlated with verbal memory, learning, verbal fluency, and semantic organization. We found that the percentage of stage N3 sleep decreased while negative symptom severity increased. In addition, the percentage of stage N1 sleep increased as negative symptom severity increased. Reduction in stage N3 sleep was associated with an impairment in learning, verbal fluency, and response inhibition. The sleep spindle density and cognitive functions did not show any associations.

CONCLUSIONS

Taken together, these findings suggest that patients with FEP show global cognitive impairment (except for attention and processing speed), which is associated with changes in sleep architecture and higher score in a scale assessing negative symptoms. We conclude that cognitive function and spindle parameters differ nonlinearly among patients with FEP.

Systematic review: the relationship between sleep spindle activity with cognitive functions, positive and negative symptoms in psychosis

BACKGROUND

Sleep disturbances are associated with worse cognitive and psychotic symptoms in individuals with schizophrenia. Growing literature reveals sleep spindle deficits in schizophrenia may be an endophenotype reflecting a dysfunctional thalamo-thalamic reticular nucleus-cortical circuit. Since thalamic functions link to cognitive, positive and negative symptoms, it is possible that sleep spindle activity is associated with these symptoms. The primary objectives of this systematic review were to assess the associations of sleep spindle activity in psychotic patients with 1) cognitive functions; and 2) positive and negative symptom severity. A secondary objective was to examine which spindle parameter would be the most consistent parameter correlating with cognitive functions, and positive and negative symptoms.

METHOD

Observational studies reporting an association between sleep spindle activity and cognitive functions, positive and negative symptoms in patients with psychotic disorders were considered eligible. We developed a comprehensive electronic search strategy to identify peer-reviewed studies in Pubmed, Embase, PsycINFO and CINAHL covering all dates up to the search date in May 2020 with no language restriction. The references of published articles were hand-searched for additional materials. The authors of published articles were contacted for newer or unpublished data. Risk of bias was assessed by Appraisal of Cross-sectional Studies (AXIS).

RESULTS

A total 11 cross-sectional studies (n = 255) with low-to-moderate quality, were selected for the systematic review. 8 of them addressed the association between sleep spindle activity and cognitive functions (n = 193), of which 6 studies reported positive correlations (r only reported in 4 studies, from 0.45 to 0.75). Out of multiple cognitive domains, we have only found attention/cognitive processing speed to have a more consistent positive association with sleep spindle activity. On the other hand, 8 studies investigated the relationship between sleep spindle and positive/negative symptom severity (n = 190), but findings were inconsistent. Spindle density is the most consistent parameter correlating with cognitive functions, while the best spindle parameter for correlating with positive and negative symptom severity cannot be identified due to mixed results.

DISCUSSION

This systematic review confirms the linkage between sleep spindle activity and cognitive functions. However, included studies had small sample sizes, with high risks of sampling and response bias. Moreover, confounders were often not controlled. The heterogeneous report of spindle parameters and use of cognitive assessment tools rendered meta-analysis infeasible. It is necessary to examine the longitudinal change of sleep spindle activity with the course of illness, as well as the effect of sleep spindle enhancing agents on cognitive function.

Do sleep disturbances contribute to cognitive impairments in schizophrenia spectrum and bipolar disorders?

Sleep disturbances and cognitive impairments are both frequent across psychotic disorders, with debilitating effects on functioning and quality of life. This study aims to investigate if sleep disturbances are related to cognitive impairments in schizophrenia spectrum (SCZ) and bipolar disorders (BD), if this relationship varies between different sleep disturbances (insomnia, hypersomnia or delayed sleep phase (DSP)) and lastly, if this relationship differs between clinical groups and healthy controls (HC). We included 797 patients (SCZ = 457, BD = 340) from the Norwegian Centre for Mental Disorders Research (NORMENT) study in Norway. Sleep disturbances were based on items from the Inventory of Depressive Symptoms-Clinician rated scale (IDS-C). Their relationship with several cognitive domains was tested using separate ANCOVAs. A three-way between-groups ANOVA was conducted to test if the relationship with cognitive impairments varies between different sleep disturbances. These analyses revealed significantly poorer processing speed and inhibition in those with any sleep disturbance versus those without, also after adjusting for several covariates. The relationship between sleep disturbances and cognition was similar across SCZ and BD, and there were significant effects of insomnia and hypersomnia on both processing speed and inhibition. No association between sleep disturbances and cognition was found in HC. Sleep disturbances contribute to cognitive impairments in psychotic disorders. Processing speed and inhibition is poorer in patients with sleep disturbances. Impairments in these domains are related to insomnia and hypersomnia. These findings suggest that treating sleep disturbances is important to protect cognitive functioning, alongside cognitive remediation in psychotic disorders.

Sleep disturbances in schizophrenia: what we know, what still needs to be done

Sleep disturbances are commonly observed in schizophrenia (SCZ) and are associated with worse psychotic symptoms and poorer clinical outcomes. Early polysomnography studies have focused on characterizing differences in sleep architecture between patients with SCZ and healthy controls. More recently, research has focused on sleep-specific EEG oscillations, such as sleep spindles and slow waves, which reflect the integrity of underlying thalamo-cortical networks. Furthermore, high-density (hd)-EEG (≥64 channels), which affords enhanced spatial resolution, has been employed to better localize abnormalities in sleep characteristics and related thalamo-cortical circuits in patients with SCZ and related disorders. In this article, we will review the most relevant sleep abnormalities reported in SCZ, with an emphasis on recent findings, and propose directions for future research.