Neural Cross-Frequency Coupling Functions in Sleep

Manasova, Dragana; Stankovski, Tomislav

doi:10.1016/j.neuroscience.2023.05.016

Cited by 3 publications

(3 citation statements)

References 76 publications

(103 reference statements)

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“…2 ) is mostly consistent with well-established knowledge of the relative spectral characteristics of wakefulness versus various sleep stages ( Imperatori et al. 2021 , Manasova and Stankovski 2023 ). In N2 sleep, we observe an increase in the relative delta and theta powers, as well as a decrease in alpha, beta, and gamma bands, which is aligned with previous reports ( Imperatori et al.…”

Section: Discussionsupporting

confidence: 86%

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Content–state dimensions characterize different types of neuronal markers of consciousness

Pérez,

Manasova,

Hermann

et al. 2024

Neuroscience of Consciousness

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Identifying the neuronal markers of consciousness is key to supporting the different scientific theories of consciousness. Neuronal markers of consciousness can be defined to reflect either the brain signatures underlying specific conscious content or those supporting different states of consciousness, two aspects traditionally studied separately. In this paper, we introduce a framework to characterize markers according to their dynamics in both the “state” and “content” dimensions. The 2D space is defined by the marker’s capacity to distinguish the conscious states from non-conscious states (on the x-axis) and the content (e.g. perceived versus unperceived or different levels of cognitive processing on the y-axis). According to the sign of the x- and y-axis, markers are separated into four quadrants in terms of how they distinguish the state and content dimensions. We implement the framework using three types of electroencephalography markers: markers of connectivity, markers of complexity, and spectral summaries. The neuronal markers of state are represented by the level of consciousness in (i) healthy participants during a nap and (ii) patients with disorders of consciousness. On the other hand, the neuronal markers of content are represented by (i) the conscious content in healthy participants’ perception task using a visual awareness paradigm and (ii) conscious processing of hierarchical regularities using an auditory local–global paradigm. In both cases, we see separate clusters of markers with correlated and anticorrelated dynamics, shedding light on the complex relationship between the state and content of consciousness and emphasizing the importance of considering them simultaneously. This work presents an innovative framework for studying consciousness by examining neuronal markers in a 2D space, providing a valuable resource for future research, with potential applications using diverse experimental paradigms, neural recording techniques, and modeling investigations.

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Section: Discussionsupporting

confidence: 86%

“…2019 , Imperatori et al. 2021 , Manasova and Stankovski 2023 ) and fMRI activity patterns characterizing brain activity in wakefulness and during different sleep stages ( Dang-Vu et al. 2010 , Peigneux 2014 , Song and Tagliazucchi 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Content–state dimensions characterize different types of neuronal markers of consciousness

Pérez,

Manasova,

Hermann

et al. 2024

Neuroscience of Consciousness

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“…In contrast to the profound reduction of delta oscillations observed in individuals with Dup15q syndrome [ 10 , 12 ], we found no difference in delta oscillation power in Dup15q mice. Delta oscillations are crucial for the restorative functions of deep sleep, including memory consolidation and overall cognitive rejuvenation and reflect the homeostatic mechanisms underlying sleep organization [ 73 – 76 ]. The preservation of delta oscillations in Dup15q mice suggests a divergence from the clinical presentation in humans and underscores the importance of species-species considerations in translational research.…”

Section: Discussionmentioning

confidence: 99%

Sleep EEG signatures in mouse models of 15q11.2-13.1 duplication (Dup15q) syndrome

Saravanapandian,

Madani,

Nichols

et al. 2024

J Neurodevelop Disord

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Background Sleep disturbances are a prevalent and complex comorbidity in neurodevelopmental disorders (NDDs). Dup15q syndrome (duplications of 15q11.2-13.1) is a genetic disorder highly penetrant for NDDs such as autism and intellectual disability and it is frequently accompanied by significant disruptions in sleep patterns. The 15q critical region harbors genes crucial for brain development, notably UBE3A and a cluster of gamma-aminobutyric acid type A receptor (GABAAR) genes. We previously described an electrophysiological biomarker of the syndrome, marked by heightened beta oscillations (12-30 Hz) in individuals with Dup15q syndrome, akin to electroencephalogram (EEG) alterations induced by allosteric modulation of GABAARs. Those with Dup15q syndrome exhibited increased beta oscillations during the awake resting state and during sleep, and they showed profoundly abnormal NREM sleep. This study aims to assess the translational validity of these EEG signatures and to delve into their neurobiological underpinnings by quantifying sleep physiology in chromosome-engineered mice with maternal (matDp/ + mice) or paternal (patDp/ + mice) inheritance of the full 15q11.2-13.1-equivalent duplication, and mice with duplication of just the UBE3A gene (Ube3a overexpression mice; Ube3a OE mice) and comparing the sleep metrics with their respective wildtype (WT) littermate controls. Methods We collected 48-h EEG/EMG recordings from 35 (23 male, 12 female) 12–24-week-old matDp/ + , patDp/ + , Ube3a OE mice, and their WT littermate controls. We quantified baseline sleep, sleep fragmentation, spectral power dynamics during sleep states, and recovery following sleep deprivation. Within each group, distinctions between Dup15q mutant mice and WT littermate controls were evaluated using analysis of variance (ANOVA) and student’s t-test. The impact of genotype and time was discerned through repeated measures ANOVA, and significance was established at p < 0.05. Results Our study revealed that across brain states, matDp/ + mice mirrored the elevated beta oscillation phenotype observed in clinical EEGs from individuals with Dup15q syndrome. Time to sleep onset after light onset was significantly reduced in matDp/ + and Ube3a OE mice. However, NREM sleep between Dup15q mutant and WT littermate mice remained unaltered, suggesting a divergence from the clinical presentation in humans. Additionally, while increased beta oscillations persisted in matDp/ + mice after 6-h of sleep deprivation, recovery NREM sleep remained unaltered in all groups, thus suggesting that these mice exhibit resilience in the fundamental processes governing sleep-wake regulation. Conclusions Quantification of mechanistic and translatable EEG biomarkers is essential for advancing our understanding of NDDs and their underlying pathophysiology. Our study of sleep physiology in the Dup15q mice underscores that the beta EEG biomarker has strong translational validity, thus opening the door for pre-clinical studies of putative drug targets, using the biomarker as a translational measure of drug-target engagement. The unaltered NREM sleep may be due to inherent differences in neurobiology between mice and humans. These nuanced distinctions highlight the complexity of sleep disruptions in Dup15q syndrome and emphasize the need for a comprehensive understanding that encompasses both shared and distinct features between murine models and clinical populations.

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Delta-alpha cross-frequency coupling for different brain regions

Lukarski,

Petkoski,

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Neural interactions occur on different levels and scales. It is of particular importance to understand how they are distributed among different neuroanatomical and physiological relevant brain regions. We investigated neural cross-frequency couplings between different brain regions according to the Desikan–Killiany brain parcellation. The adaptive dynamic Bayesian inference method was applied to EEG measurements of healthy resting subjects in order to reconstruct the coupling functions. It was found that even after averaging over all subjects, the mean coupling function showed a characteristic waveform, confirming the direct influence of the delta-phase on the alpha-phase dynamics in certain brain regions and that the shape of the coupling function changes for different regions. While the averaged coupling function within a region was of similar form, the region-averaged coupling function was averaged out, which implies that there is a common dependence within separate regions across the subjects. It was also found that for certain regions the influence of delta on alpha oscillations is more pronounced and that oscillations that influence other are more evenly distributed across brain regions than the influenced oscillations. When presenting the information on brain lobes, it was shown that the influence of delta emanating from the brain as a whole is greatest on the alpha oscillations of the cingulate frontal lobe, and at the same time the influence of delta from the cingulate parietal brain lobe is greatest on the alpha oscillations of the whole brain.

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Neural Cross-Frequency Coupling Functions in Sleep

Cited by 3 publications

References 76 publications

Content–state dimensions characterize different types of neuronal markers of consciousness

Content–state dimensions characterize different types of neuronal markers of consciousness

Sleep EEG signatures in mouse models of 15q11.2-13.1 duplication (Dup15q) syndrome

Delta-alpha cross-frequency coupling for different brain regions

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