Sleep stages and sleep rhythms in 107 subjects.

a, Sleep rhythms and task schematic. Subjects slept overnight 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 colour-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 & 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 SOs (left) and spindles (right) across sleep stages. Each dot represents an individual participant. Error bars indicate SEM. *** p < 0.001.

Sleep rhythms and SO-spindle coupling.

a, The SO-spindle coupling in the temporal frequency domain. The upper two rows illustrates 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 different sleep stages. c, Differences between coupled and uncoupled sleep rhythms. 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 toward the UP-state 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. * p < 0.05, ** p < 0.01, *** p < 0.001.

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). e, Functional decoding using the ROI association method in Neurosynth. Each row corresponds to the brain-wide activation patterns for sleep rhythms shown in panels b-d, while each column corresponds to topics in the Neurosynth database (detailed in Methods). The left panel shows results using negative activation, and the right panel shows results using positive activation. Only topics with a decoded significance level of p < 0.05 are displayed.

Functional connectivity 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 functional connectivity with the whole brain (main effect of hippocampus BOLD signal in PPI analysis). The hippocampus ROI is bilateral, anatomically defined (bottom, orange colour). 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 purpose. 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, Functional connectivity changes during SO-spindle coupling for hippocampus-based (left bottom, orange colour), thalamus-based (middle, green colour), and mPFC-based (right bottom, purple colour) connectivity. The results of ROI analysis for each direction are shown on the arrows. * p < 0.05, ns., not significant. Abbreviations: FC - functional connectivity, PPI - psychophysiological interaction.

Removal of MRI gradient noise from simultaneous 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.

Brain-wide activity between SO UP-state (peak) and DOWN-state (trough).

a, Brain activity with SO DOWN-state (trough) modelled 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) modelled 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 purpose only. No brain region was found significant in this contrast.

Peak activity and cluster size of SO main effect whole brain activation patterns.

We used the SO main effect whole-brain activation patterns in Fig. 3b. ROIs were defined anatomically (see Methods). Cluster sizes are reported punc.< 0.001.

Peak activity and cluster size of Spindle main effect whole brain activation patterns.

We used the Spindle main effect whole-brain activation patterns in Fig. 3c. ROIs were defined anatomically (see Methods). Cluster sizes are reported punc. < 0.001.

Peak activity and cluster size of SO-spindle interaction effect whole brain activation patterns.

We used the SO-spindle interaction effect whole-brain activation patterns in Fig. 3d. ROIs were defined anatomically (see Methods). Cluster sizes are reported punc.< 0.001.