Literature DB >> 27840241

Spatiotemporal characteristics of sleep spindles depend on cortical location.

Giovanni Piantoni1, Eric Halgren2, Sydney S Cash3.   

Abstract

Since their discovery almost one century ago, sleep spindles, 0.5-2s long bursts of oscillatory activity at 9-16Hz during NREM sleep, have been thought to be global and relatively uniform throughout the cortex. Recent work, however, has brought this concept into question but it remains unclear to what degree spindles are global or local and if their properties are uniform or location-dependent. We addressed this question by recording sleep in eight patients undergoing evaluation for epilepsy with intracranial electrocorticography, which combines high spatial resolution with extensive cortical coverage. We find that spindle characteristics are not uniform but are strongly influenced by the underlying cortical regions, particularly for spindle density and fundamental frequency. We observe both highly isolated and spatially distributed spindles, but in highly skewed proportions: while most spindles are restricted to one or very few recording channels at any given time, there are spindles that occur over widespread areas, often involving lateral prefrontal cortices and superior temporal gyri. Their co-occurrence is affected by a subtle but significant propagation of spindles from the superior prefrontal regions and the temporal cortices towards the orbitofrontal cortex. This work provides a brain-wide characterization of sleep spindles as mostly local graphoelements with heterogeneous characteristics that depend on the underlying cortical area. We propose that the combination of local characteristics and global organization reflects the dual properties of the thalamo-cortical generators and provides a flexible framework to support the many functions ascribed to sleep in general and spindles specifically.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Co-occurrence; Local; Propagation; Sleep; Spindles

Mesh:

Year:  2016        PMID: 27840241      PMCID: PMC5321858          DOI: 10.1016/j.neuroimage.2016.11.010

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  68 in total

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4.  Sleep spindles are locally modulated by training on a brain-computer interface.

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5.  Magnetoencephalography demonstrates multiple asynchronous generators during human sleep spindles.

Authors:  Nima Dehghani; Sydney S Cash; Andrea O Rossetti; Chih Chuan Chen; Eric Halgren
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Authors:  Matthias Mölle; Til O Bergmann; Lisa Marshall; Jan Born
Journal:  Sleep       Date:  2011-10-01       Impact factor: 5.849

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Journal:  Eur J Neurosci       Date:  2010-12-07       Impact factor: 3.386

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9.  Surface-based mixed effects multilevel analysis of grouped human electrocorticography.

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Authors:  Birgit Frauscher; Nicolás von Ellenrieder; François Dubeau; Jean Gotman
Journal:  Neuroimage       Date:  2014-10-23       Impact factor: 6.556

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  21 in total

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2.  Large-scale structure and individual fingerprints of locally coupled sleep oscillations.

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3.  Coordination of Human Hippocampal Sharpwave Ripples during NREM Sleep with Cortical Theta Bursts, Spindles, Downstates, and Upstates.

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4.  Heterogeneous Origins of Human Sleep Spindles in Different Cortical Layers.

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5.  Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load.

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6.  Human Spindle Variability.

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7.  Theta Bursts Precede, and Spindles Follow, Cortical and Thalamic Downstates in Human NREM Sleep.

Authors:  Christopher E Gonzalez; Rachel A Mak-McCully; Burke Q Rosen; Sydney S Cash; Patrick Y Chauvel; Hélène Bastuji; Marc Rey; Eric Halgren
Journal:  J Neurosci       Date:  2018-09-21       Impact factor: 6.167

8.  Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics.

Authors:  B Q Rosen; G P Krishnan; P Sanda; M Komarov; T Sejnowski; N Rulkov; I Ulbert; L Eross; J Madsen; O Devinsky; W Doyle; D Fabo; S Cash; M Bazhenov; E Halgren
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9.  Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles.

Authors:  Roy Cox; Anna C Schapiro; Dara S Manoach; Robert Stickgold
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10.  Using MEG to Understand the Progression of Light Sleep and the Emergence and Functional Roles of Spindles and K-Complexes.

Authors:  Andreas A Ioannides; Lichan Liu; Vahe Poghosyan; George K Kostopoulos
Journal:  Front Hum Neurosci       Date:  2017-06-16       Impact factor: 3.169

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