Sleep spindles as neurophysiological biomarkers of schizophrenia

Ferrarelli, Fabio

doi:10.1111/ejn.16178

Cited by 2 publications

(3 citation statements)

References 80 publications

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“…Examples of such sleep EEG analyses include an association between sleep spindles and sleep-dependent memory consolidation (8–13), regional differences in slow-wave activity during development (14), changes in slow-wave-spindle coupling in old age (15), and neural correlates of dreaming (16). In the clinic, advanced analysis that goes beyond sleep architecture reveals an association between disrupted frontal slow waves and β-amyloid pathology in Alzheimer’s disease (17), altered central sleep spindles in schizophrenia (18), and how epileptic seizures emerge from sleep oscillations (19).…”

Section: Introductionmentioning

confidence: 99%

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SleepEEGpy: a Python-based software “wrapper” package to organize preprocessing, analysis, and visualization of sleep EEG data

Belonosov,

Falach,

Schmidig

et al. 2023

Preprint

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Sleep research uses electroencephalography (EEG) to infer brain activity in health and disease. Beyond standard sleep scoring, there is increased interest in advanced EEG analysis that investigates activities at specific times, frequencies, and scalp locations. Such analysis requires preprocessing to improve the signal-to-noise ratio, and dedicated analysis algorithms and visualizations. While many EEG software packages exist, sleep research has specific considerations (e.g., avoiding particular artifacts, analysis, and visualization in an ongoing non-epoch nature, detection of characteristic oscillatory events, and interface with sleep staging) that require dedicated tools. Currently, sleep investigators typically use available libraries for specific tasks in a ‘fragmented’ configuration that is inefficient, prone to errors, and requires the burdensome learning of multiple software environments. Here, we present SleepEEGpy, an open-source Python package for sleep EEG data preprocessing and analysis, including (i) cleaning, (ii) independent component analysis, (iii) analysis of sleep events, (iv) analysis of spectral features, and associated visualization tools. SleepEEGpy builds upon MNE-Python, YASA, and SpecParam (formerly FOOOF) tools to provide an all-in-one package for comprehensive yet straightforward sleep EEG research. We demonstrate the SleepEEGpy pipeline and its functionalities by applying it to overnight high-density EEG data in healthy participants, revealing multiple characteristic activity signatures typical of each vigilance state. These include alpha oscillations in wakefulness, sleep spindle and slow wave activities in NREM sleep, and theta activity in REM sleep. We hope that this package will be embraced and further developed by the sleep research community, allowing investigators to focus more on science and improve reproducibility among research groups.

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Section: Introductionmentioning

confidence: 99%

“…Next, compute PSD per sleep stage using the average reference, 2048 samples length of the FFT, and the Hamming window with an overlap of 1024 samples (l. 11-17). Plot scalp topography distribution of PSDs per sleep stage and PSD plot (l [18][19][20][21][22][23][24]…”

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confidence: 99%

SleepEEGpy: a Python-based software “wrapper” package to organize preprocessing, analysis, and visualization of sleep EEG data

Belonosov,

Falach,

Schmidig

et al. 2023

Preprint

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“…Psychoactive drugs significantly impact sleep patterns as well as the sleep EEG (Leong et al, 2022), although associations can be complex: they can normalize some aspects of sleep in SCZ but disrupt others (Krystal et al, 2008). While studies of unmedicated SCZ patients have established that sleep abnormalities (notably, spindle deficits) persist independent of medication (Manoach et al, 2014), recent reviews have pointed to the need for research to better characterize the roles of medications in generating sleep alterations (Ferrarelli, 2023; Kaskie and Ferrarelli, 2020). In particular, larger sample sizes – such as offered by the current study – will be necessary to resolve the impact of different medications on sleep architecture in patients and to disentangle it from underlying disease-associated signatures.…”

Section: Introductionmentioning

confidence: 99%

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

Kozhemiako,

Jiang,

Sun

et al. 2023

Preprint

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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.

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Sleep spindles as neurophysiological biomarkers of schizophrenia

Cited by 2 publications

References 80 publications

SleepEEGpy: a Python-based software “wrapper” package to organize preprocessing, analysis, and visualization of sleep EEG data

SleepEEGpy: a Python-based software “wrapper” package to organize preprocessing, analysis, and visualization of sleep EEG data

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

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