Whole brain signatures surrounding sleep spindles in human NREM sleep
Poster No:
2090
Submission Type:
Abstract Submission
Authors:
Isabella Orlando1, Nathan Cross2, Florence Pomares3, Aude Jegou3, Aurore Perrault4, James Shine1, Thien Thanh Dang Vu5, Christophe Grova5, Claire O'Callaghan1
Institutions:
1The University of Sydney, Sydney, NSW, 2University of Sydney, Camperdown, New South Wales, 3Concordia University, Montreal, Québec, 4Macquarie University, Sydney, NSW, 5Concordia University, Montreal, QC
First Author:
Co-Author(s):
Introduction:
The interplay of brain rhythms during non-rapid eye movement (NREM) sleep is temporally constrained by an infraslow oscillatory pattern (Fernandez & Lüthi, 2020; Lecci et al., 2017). This infraslow organisation – operating at around 0.02Hz on a 50 second timescale – promotes clustering of sleep spindles within slow waves, creating phases of spindle-rich sleep and lower arousability, which together are conducive for memory consolidation (Lecci et al., 2017). This oscillatory pattern is likely driven by hypothalamic and brainstem circuits, with phasic bouts of locus coeruleus activity proposed as a contributing mechanism (Kjaerby et al., 2022; Osorio-Forero et al., 2021; Swift et al., 2018). However, much of this evidence is based on rodent studies and has yet to be thoroughly investigated in human sleep.
Methods:
We examined whole-brain signatures around sleep spindles during NREM2, using simultaneous EEG-fMRI in 18 healthy adults (10 females; mean age = 21.44) during a 60-minute sleep opportunity following 24 hours of sleep deprivation. fMRI data were preprocessed and denoised using fMRIPrep (Esteban et al., 2019) and xcpEngine (Ciric et al. 2017). Mean BOLD signal time-series were extracted for 400 cortical (Schaefer et al., 2018), 28 cerebellar (Diedrichsen, 2006) and 54 subcortical regions (Tian et al., 2020). EEG data from a MR compatible 256 channel high-density array was preprocessed to correct for MR and pulse-related artifacts using Brain Vision Analyzer, with sleep scored in 30-second epochs by two scorers using Wonambi. Sleep spindles were detected from the Cz channel using individual specific sigma peaks (9–16 Hz) via Wonambi, FOOOF and seapipe toolboxes, following established guidelines (Mölle et al., 2011). BOLD signals from the first NREM2 period >5 minutes were extracted and convolved with the HRF around spindle events. We characterised spindle-related BOLD activity, including integration via participation coefficient estimated from dynamic functional connectivity, low-frequency spectral signatures, and correlations with noradrenergic receptor densities obtained from the Allen Human Brain Atlas (Hawrylycz et al., 2015).
Results:
Using a general linear model, we compared BOLD activity immediately before (-5 sec) and after (+5 sec) sleep spindle events during NREM2 sleep. Temporal lobe, thalamic, and motor regions were recruited preceding spindles, while default mode network regions were deactivated following a spindle (Figure 1A-B). Dynamic functional connectivity analysis revealed increased network integration around spindles compared to inter-spindle periods (p < 0.001) (Figure 1C-F). Frequency spectrum analysis of regional BOLD timeseries showed a spectral power peak at 0.025Hz, an established infraslow oscillatory frequency related to spindle clustering and noradrenergic tone (Figure 2A). Thalamic, cerebellar, visual, and parietal regions exhibited the highest 0.025Hz power, while default mode network regions showed the least (Figure 2B). Regional 0.025Hz power significantly correlated with post-spindle BOLD activity (r = 0.224, p < 0.001) and adrenergic α2A receptor density (r = 0.126, pspin = 0.022) (Figure 2C-D).
·Figure 1
·Figure 2
Conclusions:
These results provide an in-depth characterisation of fMRI signatures surrounding spindles during non-REM sleep. We show evidence for hemodynamic, topological and spectral patterns that define the periods immediately preceding and following spindle events. These results substantiate large-scale brain state changes in the immediate periods before and after sleep spindles during human non-REM sleep. We speculate that underlying noradrenergic modulation may contribute to these patterns – closely tying with recent animal work confirming the influence of noradrenergic modulation over brain states during sleep.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Systems
Novel Imaging Acquisition Methods:
BOLD fMRI
Multi-Modal Imaging 2
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 1
Keywords:
Consciousness
Electroencephaolography (EEG)
FUNCTIONAL MRI
Memory
Noradrenaline
Sleep
Thalamus
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Fernandez, L. M. J., & Lüthi, A. (2020). Sleep Spindles: Mechanisms and Functions. Physiological Reviews, 100(2), 805–868.
Hawrylycz, M. (2015). Canonical genetic signatures of the adult human brain. Nature Neuroscience, 18(12), 1832–1844.
Kjaerby, C. (2022). Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine. Nature Neuroscience, 25(8), Article 8.
Lecci, S. (2017). Coordinated infraslow neural and cardiac oscillations mark fragility and offline periods in mammalian sleep. Science Advances, 3(2), e1602026.
Mölle, M. (2011). Fast and Slow Spindles during the Sleep Slow Oscillation: Disparate Coalescence and Engagement in Memory Processing. Sleep, 34(10), 1411–1421.
Osorio-Forero, A. (2021). Noradrenergic circuit control of non-REM sleep substates. Current Biology, 31(22), 5009-5023.e7.
Schaefer, A. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28(9), 3095–3114.
Swift, K. M. (2018). Abnormal Locus Coeruleus Sleep Activity Alters Sleep Signatures of Memory Consolidation and Impairs Place Cell Stability and Spatial Memory. Current Biology, 28(22), 3599-3609.e4.
Tian, Y. (2020). Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nature Neuroscience, 23(11), Article 11.