Statistical Analysis of Sleep Spindle Occurrences (original) (raw)
Related papers
Topographical frequency dynamics within EEG and MEG sleep spindles
Clinical Neurophysiology, 2011
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.
Assessing EEG sleep spindle propagation. Part 1: Theory and proposed methodology
Journal of neuroscience methods, 2013
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.
Sleep Spindles in Humans: Insights from Intracranial EEG and Unit Recordings
Journal of Neuroscience, 2011
Sleep spindles are an electroencephalographic (EEG) hallmark of non-rapid eye movement (NREM) sleep and are believed to mediate many sleep-related functions, from memory consolidation to cortical development. Spindles differ in location, frequency, and association with slow waves, but whether this heterogeneity may reflect different physiological processes and potentially serve different functional roles remains unclear. Here we used a unique opportunity to record intracranial depth EEG and single-unit activity in multiple brain regions of neurosurgical patients to better characterize spindle activity in human sleep. We find that spindles occur across multiple neocortical regions, and less frequently also in the parahippocampal gyrus and hippocampus. Most spindles are spatially restricted to specific brain regions. In addition, spindle frequency is topographically organized with a sharp transition around the supplementary motor area between fast (13-15 Hz) centroparietal spindles often occurring with slow-wave up-states, and slow (9 -12 Hz) frontal spindles occurring 200 ms later on average. Spindle variability across regions may reflect the underlying thalamocortical projections. We also find that during individual spindles, frequency decreases within and between regions. In addition, deeper NREM sleep is associated with a reduction in spindle occurrence and spindle frequency. Frequency changes between regions, during individual spindles, and across sleep may reflect the same phenomenon, the underlying level of thalamocortical hyperpolarization. Finally, during spindles neuronal firing rates are not consistently modulated, although some neurons exhibit phase-locked discharges. Overall, anatomical considerations can account well for regional spindle characteristics, while variable hyperpolarization levels can explain differences in spindle frequency.
Spatiotemporal patterns of sleep spindle activity in human anterior thalamus and cortex
2022
Sleep spindles (8 - 16 Hz) are transient electrophysiological events during non-rapid eye movement sleep. While sleep spindles are routinely observed in the cortex using scalp electroencephalography (EEG), recordings of their thalamic counterparts have not been widely studied in humans. Based on a few existing studies, it has been hypothesized that spindles occur as largely local phenomena. We investigated intra-thalamic and thalamocortical spindle co-occurrence, which may underlie thalamocortical communication. We obtained scalp EEG and thalamic recordings from 7 patients that received bilateral deep brain stimulation (DBS) electrodes to the anterior thalamus for the treatment of drug resistant focal epilepsy. Spindles were categorized into subtypes based on their main frequency (i.e., slow (10±2 Hz) or fast (14±2 Hz)) and their level of thalamic involvement (spanning one channel, or spreading uni- or bilaterally within the thalamus). For the first time, we contrasted observed spin...
Assessing EEG sleep spindle propagation. Part 2: Experimental characterization
Journal of neuroscience methods, 2013
BACKGROUND:This communication is the second of a two-part series that describes and tests a new methodology for assessing the propagation of EEG sleep spindles. Whereas the first part describes the methodology in detail, this part proposes a thorough evaluation of the approach by applying it to a sample of laboratory sleep recordings. NEW METHOD:The tested methodology is based on the alignment of time-frequency representations of spindle activity across recording channels and is used for assessing sleep spindle propagation over the human scalp using noninvasive EEG. RESULTS: Spindle propagation displays features that suggest wave displacements of global synaptic potential fields. Propagation patterns that are coherent (as opposed to random), laterally symmetrical, and highly repeatable within and between subjects were observed. Propagation was slower from posterior to anterior and from central to lateral brain regions than in the opposite directions. Propagation speeds varied between 2.3 and 7.0m/s were obtained. A distinct grouping of propagation properties was noted for a small cluster of frontal electrodes. No propagation between distantly separated scalp locations was observed. The values of spindle characteristics such as average frequency, RMS amplitude, frequency slope, and duration, depend largely on propagation direction but are only mildly correlated with propagation delay. COMPARISON WITH EXISTING METHOD(S): Results obtained are in line with many results published in the literature and offer new measures for describing sleep spindle behavior. CONCLUSIONS: Propagation properties provide new information about sleep spindle behaviors and thus allow more precise automated assessments of spindle-related functions.
Sleep spindles in human prefrontal cortex: an electrocorticographic study
Neuroscience Research, 2003
To investigate the sleep spindle activity of the human prefrontal cortex (PFC), we simultaneously recorded whole nights of polysomnographic and electrocorticographic (ECoG) activities during the natural sleep of epileptic patients. Subjects were nine patients with intractable epilepsy who had subdural electrodes surgically attached to the orbital (seven cases), medial (three cases), or dorsolateral (two cases) PFC, and in one case to the frontal pole. To examine spindle frequencies, fast Fourier transformation (FFT) and auto-correlation analyses were performed on the PFC ECoG and Cz EEG data, primarily on epochs of stage 2 sleep. Lower sigma band ECoG oscillations of about 12 Hz were widely distributed across all prefrontal cortical areas including the frontal limbic regions, but none of the PFC sigma frequency peaks coincided with the faster (about 14 Hz) Cz EEG sleep spindles. Combining our results with anatomical and electrophysiological facts, it is suggested that the thalamofrontal circuit involving the rostral reticular and the mediodorsal nucleus of the thalamus is responsible for the generation of 12 Hz frontal spindles in humans.
Spectral and temporal characterization of sleep spindles—methodological implications
Journal of Neural Engineering, 2021
Objective. Nested into slow oscillations (SOs) and modulated by their up-states, spindles are electrophysiological hallmarks of N2 sleep stage that present a complex hierarchical architecture. However, most studies have only described spindles in basic statistical terms, which were limited to the spindle itself without analyzing the characteristics of the pre-spindle moments in which the SOs are originated. The aim of this study was twofold: (a) to apply spectral and temporal measures to the pre-spindle and spindle periods, as well as analyze the correlation between them, and (b) to evaluate the potential of these spectral and temporal measures in future automatic detection algorithms. Approach. An automatic spindle detection algorithm was applied to the overnight electroencephalographic recordings of 26 subjects. Ten complementary features (five spectral and five temporal parameters) were computed in the pre-spindle and spindle periods after their segmentation. These features were ...
SLEEP, 2016
The sleep spindle has been implicated in thalamic sensory gating, cortical development, and memory consolidation. These multiple functions may depend on specific spatiotemporal emergence and interactions with other spindles and other forms of brain activity. Therefore, we measured sleep spindle cortical distribution, regional heterogeneity, synchronization, and phase relationships with other electroencephalographic components in freely moving primates. Methods: Transcortical field potentials were recorded from Japanese monkeys via telemetry and were analyzed using the Hilbert-Huang transform. Results: Spindle (12-20 Hz) current sources were identified over a wide region of the frontoparietal cortex. Most spindles occurred independently in their own frequency, but some appeared concordant between cortical areas with frequency interdependence, particularly in nearby regions and bilaterally symmetrical regions. Spindles in the dorsolateral prefrontal cortex appeared around the surface-positive and depth-negative phase of transcortically recorded slow oscillations (< 1 Hz), whereas centroparietal spindles emerged around the opposite phase. The slow-oscillation phase reversed between the prefrontal and central regions. Gamma activities increased before spindle onset. Several regional heterogeneities in properties of human spindles were replicated in the monkeys, including frequency, density, and inter-cortical time lags, although their topographic patterns were different from those of humans. The phaseamplitude coupling between spindle and gamma activity was also replicated. Conclusions: Spindles in widespread cortical regions are possibly driven by independent rhythm generators, but are temporally associated to spindles in other regions and to slow and gamma oscillations by corticocortical and thalamocortical pathways.