Human brain-wide activation of sleep rhythms
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, China
- Chinese Institute for Brain Research, China
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, China
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, China
- McGovern Institute for Brain Research, Peking University, China
Figures
Figure 1 with 1 supplement
Sleep stages and sleep rhythms in 107 subjects.
(a) Sleep rhythms and task schematic. Subjects slept the first half of the night with simultaneous EEG-fMRI recordings. Since detecting hippocampal ripples directly from scalp EEG is challenging, our focus was on capturing SOs, spindles, and their couplings. Regions of interest (ROIs) are color-coded: green for the thalamus (spindle), purple for the mPFC (SOs), and orange for the hippocampus (ripples). (b) Sleep staging and EEG spectrogram. N2/3 sleep stages (red line) were initially identified using an offline automatic sleep staging algorithm (Vallat and Walker, 2021) and then manually validated. (c) Schematic of EEG data across different sleep stages, using preprocessed data from the C3 electrode. (d) Proportion of each sleep stage in the dataset. (e) Amplitudes (μV) of detected SOs (left) and spindles (right) across sleep stages. SO and spindle detection thresholds were defined from N2/3 sleep within each participant and then applied unchanged to N1 and REM for descriptive comparison. Detections in N1 and REM should therefore be interpreted as detector outputs under this fixed N2/3-derived criterion. The SO amplitudes were measured from the 0.16–1.25 Hz filtered EEG data, and spindle amplitudes were measured from the 12–16 Hz filtered EEG data. Each dot represents an individual participant. Error bars indicate SEM. Paired t-test, *** p<0.001.
Figure 1—figure supplement 1
Removal of MRI gradient noise from simultaneously collected EEG data.
(a) Time series of both raw and preprocessed EEG data. The top row depicts the raw EEG data, which contains noise primarily from the MRI gradient magnetic field and electrocardiographic artifacts. The bottom row showcases the preprocessed EEG data (detailed in Methods). (b) Power spectral density of the raw and preprocessed EEG data estimated by the fast Fourier transform. The raw EEG data is shown in the top row, while the preprocessed EEG data is in the bottom row. (c) Time-frequency spectrogram of the raw and preprocessed EEG data, by the short-time Fourier transform. The top row represents the raw EEG data, and the bottom row displays the preprocessed EEG data.
Figure 2 with 3 supplements
Sleep rhythms and SO-spindle coupling.
(a) The SO-spindle coupling in the temporal frequency domain. The upper two rows illustrate the spindle (12–16 Hz) phase-locked in the transition to UP-state of SO (0.16–1.25 Hz). The bottom row shows the averaged temporal frequency pattern across all instances of SO-spindle coupling and over all subjects. (b) SO-spindle coupling density across sleep stages (N=107), using SO and spindle detections obtained with fixed N2/3-derived thresholds. Coupling events in N1 and REM are shown only for descriptive comparison. The EEG-informed fMRI analyses used N2/3 coupling events only. (c) Differences between coupled and uncoupled sleep rhythms (N=107). The left panel shows the difference in amplitude between spindles coupled with SOs (Coupling) and spindles not coupled with SOs (Other). The right panel displays the difference in amplitude between SOs coupled with spindles (Coupling) and SOs not coupled with spindles (Other). (d) Phase modulation of SO-spindle coupling. Spindle peaks cluster slightly before the UP-state peak of SO (i.e. 0°), where –π/2 reflects the transition from DOWN to UP-state. The histogram represents the distribution of coupling directions across all subjects, with the red line showing the mean. Coupling phases for each subject are plotted on the circle, with coupling strength color-coded. (e) Distribution of spindle peaks on the SO phase during all SO-spindle coupling events across participants. The distribution is represented by a probability density function, and the density is evaluated at 100 equally spaced points covering the data range. Each dot represents data from an individual subject. Error bars indicate the SEM. Paired t-test, *** p<0.001.
Figure 2—figure supplement 1
ERPs of SOs and spindles coupling during different sleep stages across all 107 subjects.
(a) ERP of SOs in different sleep stages using the broadband (0.1–30 Hz) EEG data. We align the trough of the DOWN-state of each SO at time zero (see Methods for details). The orange line represents the SO ERP in the N1 stage, the black line represents the SO ERP in the N2 and N3 stages, and the green line represents the SO ERP in the REM stage. (b) ERP of spindles in different sleep stages using the broadband (0.1–30 Hz) EEG data. We align the peak of each spindle at time zero (see Methods for details). The color scheme is the same as in panel (a).
Figure 2—figure supplement 2
ERP and time-frequency patterns of SO-spindle coupling in the N1 stage.
The averaged temporal frequency pattern and ERP across all instances of SO-spindle coupling, computed over all subjects, following the same procedure as in Figure 2a, but for the N1 stage.
Figure 2—figure supplement 3
ERP and time-frequency patterns of SO-spindle coupling in the REM stage.
The averaged temporal frequency pattern and ERP across all instances of SO-spindle coupling, computed over all subjects, again following the same procedure as in Figure 2a, but for the REM stage.
Figure 3 with 3 supplements
Brain-wide activation associated with sleep rhythms.
(a) Simultaneous EEG-fMRI analysis framework for detecting brain-wide activation during sleep rhythms. Detected SOs, spindles, and their coupling were convolved with the hemodynamic response function (HRF) and downsampled to match fMRI temporal resolution. These events formed the design matrix for the general linear model (GLM) analysis of fMRI activity during sleep, linking the EEG-derived timing of sleep rhythms to the corresponding brain responses in fMRI. (b) Brain-wide activation associated with SOs. The upper row illustrates SOs, and the lower row shows the fMRI activation pattern during SO events, whole-brain family-wise error (FWE) corrected at the cluster level (p<0.05) with a cluster-forming voxel threshold of p<0.001. (c) Brain-wide activation associated with spindles. Same as panel b, but for spindle events. (d) Brain-wide activation associated with SO-spindle coupling (compared to non-coupling events).
Figure 3—figure supplement 1
Brain-wide activity between SO UP-state (peak) and DOWN-state (trough).
(a) Brain activity with SO DOWN-state (trough) modeled as event onset, whole-brain FWE corrected at the cluster level (p<0.05) with a cluster-forming voxel threshold of punc.<0.001. (b) Brain activity with SO UP-state (peak) modeled as event onset. (c) Differences in brain activity corresponding to SO UP-state and SO DOWN-state. The whole-brain results were displayed at an uncorrected threshold of p<0.01 for visualization purposes only. No brain region was found significant in this contrast.
Figure 3—figure supplement 2
Influence of the percentile threshold for SO detection on hippocampal activation (ROI) during SO-spindle coupling.
We changed the percentile threshold for SO event detection in the EEG data analysis and then reconstructed the GLM design matrix based on the SO events detected at each threshold. The brain-wide activation pattern of SO-spindle couplings in the N2/3 stage was extracted using the same method as shown in Figure 3. The gray horizontal line represents the significant range (71%–80%). * p<0.05.
Figure 3—figure supplement 3
Functional decoding using the ROI association method in Neurosynth.
(a) Decoding results using positive activation. (b) Decoding results using negative activation. Each row corresponds to the brain-wide activation patterns for sleep rhythms shown in Figure 3b–d, while each column corresponds to topics in the Neurosynth database (detailed in Methods). Only topics with a decoded significance level of p<0.05 are displayed.
Figure 4
Functional connectivity (FC) changes during SO-spindle coupling.
(a) The PPI analysis framework for detecting brain-wide connectivity changes during SO-spindle coupling. This starts by setting a specific ROI (e.g. the hippocampus) as the seed to extract the BOLD signal (physiological condition) and using identified SO-spindle coupling events as the psychological condition to compute the interaction term. The design matrix includes the main effects of the physiological and psychological conditions, along with their interaction. This analysis examines whether whole-brain communication with the hippocampus changes as a function of SO-spindle coupling. (b) Hippocampus-based FC with the whole brain (main effect of hippocampus BOLD signal in PPI analysis). The hippocampus ROI is bilateral, anatomically defined (bottom, orange color). Brain-wide connectivity is shown with whole-brain FWE correction at the cluster level (p<1e-7) with a cluster-forming voxel threshold of punc <0.001 for visualization purposes. (c) Same with panel (b), but based on thalamus (bilateral anatomically defined ROI). (d) Same with panel (b), but based on the mPFC (bilateral functionally defined ROI, detailed in Methods). (e) FC changes during SO-spindle coupling for hippocampus-based (left bottom, orange color), thalamus-based (middle, green color), and mPFC-based (right bottom, purple color) connectivity. The results of ROI analysis for each direction are shown on the arrows. * p<0.05, ns., not significant. Abbreviations: PPI - psychophysiological interaction.
Additional files
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Supplementary file 1
Descriptive results of demographic information and sleep characteristics.
Note: The total recorded time is equal to the awake time plus the total sleep time. The sleep onset latency is the time taken to reach the first sleep epoch. The sleep efficiency is the ratio of actual sleep time to total recording time.
- https://cdn.elifesciences.org/articles/103956/elife-103956-supp1-v1.docx
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Supplementary file 2
Statistics of sleep duration, SO, spindle, and coupling event numbers and densities for all 107 subjects during N1 stage.
- https://cdn.elifesciences.org/articles/103956/elife-103956-supp2-v1.docx
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Supplementary file 3
Statistics of sleep duration, SO, spindle, and coupling event numbers and densities for all 107 subjects during N2&N3 stage.
- https://cdn.elifesciences.org/articles/103956/elife-103956-supp3-v1.docx
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Supplementary file 4
Statistics of sleep duration, SO, spindle, and coupling event numbers and densities for all 107 subjects during REM stage.
- https://cdn.elifesciences.org/articles/103956/elife-103956-supp4-v1.docx
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Supplementary file 5
Peak and significant cluster of fMRI activity during SO main effect.
We used the SO main effect whole-brain activation patterns in Figure 3b. ROIs were defined anatomically (see Methods). Cluster sizes are reported punc.<0.001.
- https://cdn.elifesciences.org/articles/103956/elife-103956-supp5-v1.docx
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Supplementary file 6
Peak and significant cluster of fMRI activity during spindle main effect.
We used the spindle main effect whole-brain activation patterns in Figure 3c. ROIs were defined anatomically (see Methods). Cluster sizes are reported punc.<0.001.
- https://cdn.elifesciences.org/articles/103956/elife-103956-supp6-v1.docx
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Supplementary file 7
Peak and significant cluster of fMRI activity during SO-spindle interaction.
We used the SO-spindle interaction effect whole-brain activation patterns in Figure 3d. ROIs were defined anatomically (see Methods). Cluster sizes are reported punc.<0.001.
- https://cdn.elifesciences.org/articles/103956/elife-103956-supp7-v1.docx
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MDAR checklist
- https://cdn.elifesciences.org/articles/103956/elife-103956-mdarchecklist1-v1.pdf
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Human brain-wide activation of sleep rhythms
eLife 14:RP103956.
https://doi.org/10.7554/eLife.103956.4
