Dataset Information

Topographical frequency dynamics within EEG and MEG sleep spindles.

ABSTRACT:

Objective

Spindles are rhythmic bursts of 10-16 Hz activity, lasting ∼1 s, occur during normal stage 2 sleep. Spindles are slower in frontal EEG and possibly MEG. The posterior-fast EEG pattern may predominate early in the spindle, and the anterior-slow pattern late. We aimed to determine the proportion of spindles showing this spatio-spectro-temporal interaction for EEG, and whether it occurs in MEG.

Methods

We recorded high density EEG and MEG from seven healthy subjects during normal stage 2 sleep. High vs. low frequency (12 vs. 14 Hz) power was measured early vs. late (25th-45th vs. 55th-75th duration percentile) in 183 spindle discharges.

Results

The predicted spatio-spectro-temporal interaction was shown by 48% of EEG and 34% of MEG spindles (chance=25%). Topographically, high frequency EEG power was greatest at midline central contacts, and low frequency power at midline frontal. This frequency-specific topography was fixed over the course of the spindle.

Conclusions

An evolution from posterior-fast to anterior-slow generators commonly occurs during spindles, and this is visible with EEG and to a lesser extent, MEG.

Significance

The spatio-spectral-temporal evolution of spindles may reflect their possible involvement in coordinating cortical activity during consolidation.

SUBMITTER: Dehghani N

PROVIDER: S-EPMC2978261 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Publications

Topographical frequency dynamics within EEG and MEG sleep spindles.

Dehghani Nima N Cash Sydney S SS Halgren Eric E

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 20100715 2

<h4>Objective</h4>Spindles are rhythmic bursts of 10-16 Hz activity, lasting ∼1 s, occur during normal stage 2 sleep. Spindles are slower in frontal EEG and possibly MEG. The posterior-fast EEG pattern may predominate early in the spindle, and the anterior-slow pattern late. We aimed to determine the proportion of spindles showing this spatio-spectro-temporal interaction for EEG, and whether it occurs in MEG.<h4>Methods</h4>We recorded high density EEG and MEG from seven healthy subjects during ...[more]

PMID: 20637689

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Project description:Sleep spindles are approximately 1-second bursts of 10-15 Hz activity, occurring during normal stage 2 sleep. In animals, sleep spindles can be synchronous across multiple cortical and thalamic locations, suggesting a distributed stable phase-locked generating system. The high synchrony of spindles across scalp EEG sites suggests that this may also be true in humans. However, prior MEG studies suggest multiple and varying generators.We recorded 306 channels of MEG simultaneously with 60 channels of EEG during naturally occurring spindles of stage 2 sleep in 7 healthy subjects. High-resolution structural MRI was obtained in each subject, to define the shells for a boundary element forward solution and to reconstruct the cortex providing the solution space for a noise-normalized minimum norm source estimation procedure. Integrated across the entire duration of all spindles, sources estimated from EEG and MEG are similar, diffuse and widespread, including all lobes from both hemispheres. However, the locations, phase and amplitude of sources simultaneously estimated from MEG versus EEG are highly distinct during the same spindles. Specifically, the sources estimated from EEG are highly synchronous across the cortex, whereas those from MEG rapidly shift in phase, hemisphere, and the location within the hemisphere.The heterogeneity of MEG sources implies that multiple generators are active during human sleep spindles. If the source modeling is correct, then EEG spindles are generated by a different, diffusely synchronous system. Animal studies have identified two thalamo-cortical systems, core and matrix, that produce focal or diffuse activation and thus could underlie MEG and EEG spindles, respectively. Alternatively, EEG spindles could reflect overlap at the sensors of the same sources as are seen from the MEG. Although our results generally match human intracranial recordings, additional improvements are possible and simultaneous intra- and extra-cranial measures are needed to test their accuracy.

Project description:ObjectiveCognitive comorbidities are common in epilepsy; however, symptomatic treatment is currently the only available effective therapy. Sleep, cognition, and epilepsy are closely associated. Therefore, many studies on epilepsy and cognition have focused on sleep structures, such as sleep spindles, which are considered windows to understanding the sleeping brain. This study aimed to investigate the relationship between sleep spindles and the severity of cognitive impairment in adult epilepsy.MethodsFifty-seven adults with epilepsy underwent overnight sleep electroencephalogram recordings and cognitive testing. Slow (9-12 Hz) and fast (12-15 Hz) spindle characteristics during N2 sleep were calculated using a convolutional neural network-based sleep staging system and automatic spindle detection algorithm. Repeated-measures analysis of variance was used to analyze differences in fast and slow spindle densities among subgroups of patients based on cognitive impairment severity.ResultsA significant between-group effect was observed for both slow and fast spindle densities. Multiple comparisons showed that slow and fast spindle densities of the severe cognitive impairment subgroup were lower than those of the noncognitive impairment subgroup (P < 0.05). Simple-effect analysis revealed differences in slow spindle density distributed among the EEG channels Fp1, Fp2, F3, C3, P4, O1, O2, F8, T4, T5, T6, Fz, and Cz (P < 0.05). Differences in fast spindle density were distributed among the channels Fp1, Fp2, F3, C3, O1, O2, F7, F8, T4, T5, T6, and Fz (P < 0.05).SignificanceSignificant differences in topographical distribution of fast and slow spindle densities were observed at the scalp level among patients with different cognitive statuses. Compared with patients with no cognitive impairment, those with severe cognitive impairment had lower slow and fast spindle densities over multiple scalp regions during N2 sleep. This study provides a reference for objective assessment of cognitive dysfunction in epilepsy patients.

Dataset Information

Topographical frequency dynamics within EEG and MEG sleep spindles.

Objective

Methods

Results

Conclusions

Significance

Publications

Topographical frequency dynamics within EEG and MEG sleep spindles.

Similar Datasets

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