Emergence of Synchronous EEG Spindles From Asynchronous MEG Spindles

Multimodal Imaging Laboratory, Department of Radiology, University of California, San Diego, California, USA.
Human Brain Mapping (Impact Factor: 5.97). 12/2011; 32(12):2217-27. DOI: 10.1002/hbm.21183
Source: PubMed


Sleep spindles are bursts of rhythmic 10-15 Hz activity, lasting ∼0.5-2 s, that occur during Stage 2 sleep. They are coherent across multiple cortical and thalamic locations in animals, and across scalp EEG sites in humans, suggesting simultaneous generation across the cortical mantle. However, reports of MEG spindles occurring without EEG spindles, and vice versa, are inconsistent with synchronous distributed generation. We objectively determined the frequency of MEG-only, EEG-only, and combined MEG-EEG spindles in high density recordings of natural sleep in humans. About 50% of MEG spindles occur without EEG spindles, but the converse is rare (∼15%). Compared to spindles that occur in MEG only, those that occur in both MEG and EEG have ∼1% more MEG coherence and ∼15% more MEG power, insufficient to account for the ∼55% increase in EEG power. However, these combined spindles involve ∼66% more MEG channels, especially over frontocentral cortex. Furthermore, when both MEG and EEG are involved in a given spindle, the MEG spindle begins ∼150 ms before the EEG spindle and ends ∼250 ms after. Our findings suggest that spindles begin in focal cortical locations which are better recorded with MEG gradiometers than referential EEG due to the biophysics of their propagation. For some spindles, only these regions remain active. For other spindles, these locations may recruit other areas over the next 200 ms, until a critical mass is achieved, including especially frontal cortex, resulting in activation of a diffuse and/or multifocal generator that is best recorded by referential EEG derivations due to their larger leadfields.

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Available from: Nima Dehghani, Oct 07, 2015
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    • "Recent studies using simultaneous EEG/MEG revealed that MEG spindles have low spatial coherence and exhibit low correlation with EEG spindles (Dehghani et al., 2011). The authors speculated that multiple asynchronous sources as revealed by MEG may overlap sufficiently to appear synchronously in the EEG (Dehghani et al., 2011). This is still an object of controversy and can be addressed with combined scalp and intracranial EEG. "
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    ABSTRACT: In humans, the knowledge of intracranial correlates of spindles is mainly gathered from noninvasive neurophysiologic and functional imaging studies which provide an indirect estimate of neuronal intracranial activity. This potential limitation can be overcome by intracranial electroencephalography used in presurgical epilepsy evaluation. We investigated the intracranial correlates of scalp spindles using combined scalp and intracerebral depth electrodes covering the frontal, parietal and temporal neocortex, and the scalp and intracranial correlates of hippocampal and insula spindles in 35 pre-surgical epilepsy patients. Spindles in the scalp were accompanied by widespread cortical increases in sigma band energy (10–16 Hz): the highest percentages were observed in the frontoparietal lateral and mesial cortex, whereas in temporal lateral and mesial structures only a low or no simultaneous increase was present. This intracranial involvement during scalp spindles showed no consistent pattern, and exhibited unexpectedly low synchrony across brain regions. Hippocampal spindles were shorter and spatially restricted with a low synchrony even within the temporal lobe. Similar results were found for the insula. We suggest that the generation of spindles is under a high local cortical influence contributing to the concept of sleep as a local phenomenon and challenging the notion of spindles as widespread synchronous oscillations.
    NeuroImage 10/2014; 105. DOI:10.1016/j.neuroimage.2014.10.048 · 6.36 Impact Factor
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    • "The padding Δ í µí± í µí± is used according to the hypothesis that the time-frequency pattern shortly before and after the sleep spindle is generally more closely related to the spindle (irrespective of the channel on which this spindle is recorded) than to activity that is not associated with this spindle. This is partly supported by the observation that MEG spindle activity spans a longer time interval than what can be seen with the EEG (i.e., starting ~150 ms before the EEG spindle activity and ending ~250 ms after it, according to Dehghani et al. (2011a)). This suggests that there is an associated process acting on a wider time window that could result in correlated activity in different channels within regions just preceding or following the detected EEG spindle activity. "
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    ABSTRACT: A convergence of studies has revealed sleep spindles to be associated with sleep-related cognitive processing and even with fundamental waking state capacities such as intelligence. However, some spindle characteristics, such as propagation direction and delay, may play a decisive role but are only infrequently investigated because of technical difficulties. New METHOD: A new methodology for assessing sleep spindle propagation over the human scalp using noninvasive electroencephalography (EEG) is described. This approach is based on the alignment of time-frequency representations of spindle activity across recording channels. This first of a two-part series concentrates on framing theoretical considerations related to EEG spindle propagation and on detailing the methodology. A short example application is provided that illustrates the repeatability of results obtained with the new propagation measure in a sample of 32 night recordings. A more comprehensive experimental investigation is presented in part two of the series. Comparison with Existing Method(s): Compared to existing methods, this approach is particularly well adapted for studying the propagation of sleep spindles because it estimates time delays rather than phase synchrony and it computes propagation properties for every individual spindle with windows adjusted to the specific spindle duration. The proposed methodology is effective in tracking the propagation of spindles across the scalp and may thus help in elucidating the temporal aspects of sleep spindle dynamics, as well as other transient EEG and MEG events. A software implementation (the Spyndle Python package) is provided as open source software.
    Journal of Neuroscience Methods 08/2013; 221. DOI:10.1016/j.jneumeth.2013.08.013 · 2.05 Impact Factor
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    • "A possible explanation for these and our findings is that >12-Hz and <12-Hz EEG activity arise at different hyperpolarization levels of thalamocortical neurons (see above) and that different thalamic nuclei project to different cortical regions (i.e., posterior and lateral dorsal nuclei project to centroparietal cortex, whereas ventral and anterior dorsal nuclei mainly project to prefrontal regions) [50] [51]. Then these locations may recruit other areas, especially the frontal cortex, until a critical mass is achieved and it results in a diffuse generation of EEG spindles [49]. Compared to the presleep interval, the EEG after SO also is characterized by a generalized decrease of b activity power over almost all scalp locations, with a maximum over temporal areas. "
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    ABSTRACT: We hypothesized that the brain shows specific and predictable patterns of spatial and temporal differences during sleep onset (SO) reflecting a temporal uncoupling of electrical activity between different cortical regions and a dissociated wakelike and sleeplike electrocortical activity in different cortical areas. We analyzed full-scalp electroencephalographic (EEG) recordings of 40 healthy subjects to investigate spatial and temporal changes of EEG activity across the wake-sleep transition. We quantified EEG sleep recordings by a fast Fourier transform (FFT) algorithm and by a better oscillation (BOSC) detection method to the EEG signals, which measured oscillatory activity within a signal containing a nonrhythmic portion. The most representative spatial change at SO is the frontalization of slow-wave activity (SWA), while the θ activity, which mostly shares a similar temporal and spatial pattern with SWA, exhibits a temporo-occipital diffusion. The time course of these oscillations confirms that the changes of the dominant waves coexist with topographic changes. The waking occipital prevalence of α oscillations is progressively replaced by an occipital prevalence of θ oscillations. On the other hand, more anterior areas show a wide synchronization pattern mainly expressed by slow waves just below 4Hz and by spindle oscillations. The whole pattern of results confirms that the centrofrontal areas showed an earlier synchronization (i.e., they fall asleep first). This finding implies a coexistence of wakelike and sleeplike electrical activity during sleep in different cortical areas. It also implies that the process of progressive brain disconnection from the external world as we fall asleep does not necessarily affect primary and higher-order cortices at the same time.
    Sleep Medicine 08/2013; 14(11). DOI:10.1016/j.sleep.2013.05.021 · 3.15 Impact Factor
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