A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings
Sleep spindles are a hallmark of NREM sleep. They result from a widespread thalamo-cortical loop and involve synchronous cortical networks that are still poorly understood. We investigated whether brain activity during spindles can be characterized by specific patterns of functional connectivity amo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00310/full |
| _version_ | 1818469099255103488 |
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| author | Younes eZerouali Jean-Marc eLINA Jean-Marc eLINA Zoran eSekerovic Zoran eSekerovic Jonthan eGodbout Jonathan eDube Jonathan eDube Pierre eJolicoeur Julie eCarrier Julie eCarrier |
| author_facet | Younes eZerouali Jean-Marc eLINA Jean-Marc eLINA Zoran eSekerovic Zoran eSekerovic Jonthan eGodbout Jonathan eDube Jonathan eDube Pierre eJolicoeur Julie eCarrier Julie eCarrier |
| author_sort | Younes eZerouali |
| collection | DOAJ |
| description | Sleep spindles are a hallmark of NREM sleep. They result from a widespread thalamo-cortical loop and involve synchronous cortical networks that are still poorly understood. We investigated whether brain activity during spindles can be characterized by specific patterns of functional connectivity among cortical generators. For that purpose, we developed a wavelet-based approach aimed at imaging the synchronous oscillatory cortical networks from simultaneous MEG-EEG recordings. First, we detected spindles on the EEG and extracted the corresponding frequency-locked MEG activity under the form of an analytic ridge signal in the time-frequency plane (Zerouali et al., 2013). Secondly, we performed source reconstruction of the ridge signal within the Maximum Entropy on the Mean framework (Amblard et al., 2004), yielding a robust estimate of the cortical sources producing observed oscillations. Lastly, we quantified functional connectivity among cortical sources using phase-locking values. The main innovations of this methodology are 1) to reveal the dynamic behavior of functional networks resolved in the time-frequency plane and 2) to characterize functional connectivity among MEG sources through phase interactions. We showed, for the first time, that the switch from fast to slow oscillatory mode during sleep spindles is required for the emergence of specific patterns of connectivity. Moreover, we show that earlier synchrony during spindles was associated with mainly intra-hemispheric connectivity whereas later synchrony was associated with global long-range connectivity. We propose that our methodology can be a valuable tool for studying the connectivity underlying neural processes involving sleep spindles, such as memory, plasticity or aging. |
| first_indexed | 2024-04-13T21:19:41Z |
| format | Article |
| id | doaj.art-a5fc929410d842149e1d504012590c42 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-13T21:19:41Z |
| publishDate | 2014-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-a5fc929410d842149e1d504012590c422022-12-22T02:29:33ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2014-10-01810.3389/fnins.2014.0031082168A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordingsYounes eZerouali0Jean-Marc eLINA1Jean-Marc eLINA2Zoran eSekerovic3Zoran eSekerovic4Jonthan eGodbout5Jonathan eDube6Jonathan eDube7Pierre eJolicoeur8Julie eCarrier9Julie eCarrier10Ecole de Technologie SupérieureEcole de Technologie SupérieureUniversité de MontréalHôpital du Sacré-CoeurUniversité de MontréalHôpital du Sacré-CoeurHôpital du Sacré-CoeurUniversité de MontréalUniversité de MontréalHôpital du Sacré-CoeurUniversité de MontréalSleep spindles are a hallmark of NREM sleep. They result from a widespread thalamo-cortical loop and involve synchronous cortical networks that are still poorly understood. We investigated whether brain activity during spindles can be characterized by specific patterns of functional connectivity among cortical generators. For that purpose, we developed a wavelet-based approach aimed at imaging the synchronous oscillatory cortical networks from simultaneous MEG-EEG recordings. First, we detected spindles on the EEG and extracted the corresponding frequency-locked MEG activity under the form of an analytic ridge signal in the time-frequency plane (Zerouali et al., 2013). Secondly, we performed source reconstruction of the ridge signal within the Maximum Entropy on the Mean framework (Amblard et al., 2004), yielding a robust estimate of the cortical sources producing observed oscillations. Lastly, we quantified functional connectivity among cortical sources using phase-locking values. The main innovations of this methodology are 1) to reveal the dynamic behavior of functional networks resolved in the time-frequency plane and 2) to characterize functional connectivity among MEG sources through phase interactions. We showed, for the first time, that the switch from fast to slow oscillatory mode during sleep spindles is required for the emergence of specific patterns of connectivity. Moreover, we show that earlier synchrony during spindles was associated with mainly intra-hemispheric connectivity whereas later synchrony was associated with global long-range connectivity. We propose that our methodology can be a valuable tool for studying the connectivity underlying neural processes involving sleep spindles, such as memory, plasticity or aging.http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00310/fullsource localizationfunctional connectivityWavelet Transformphase synchronyGroup analysismaximum entropy |
| spellingShingle | Younes eZerouali Jean-Marc eLINA Jean-Marc eLINA Zoran eSekerovic Zoran eSekerovic Jonthan eGodbout Jonathan eDube Jonathan eDube Pierre eJolicoeur Julie eCarrier Julie eCarrier A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings Frontiers in Neuroscience source localization functional connectivity Wavelet Transform phase synchrony Group analysis maximum entropy |
| title | A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings |
| title_full | A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings |
| title_fullStr | A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings |
| title_full_unstemmed | A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings |
| title_short | A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings |
| title_sort | time frequency analysis of the dynamics of cortical networks of sleep spindles from meg eeg recordings |
| topic | source localization functional connectivity Wavelet Transform phase synchrony Group analysis maximum entropy |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00310/full |
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