Regional slow waves and spindles in human sleep

Abstract

The most prominent EEG events in sleep are slow waves, reflecting a slow (<1 Hz) oscillation between up and down states in cortical neurons. It is unknown whether slow oscillations are synchronous across the majority or the minority of brain regions--are they a global or local phenomenon? To examine this, we recorded simultaneously scalp EEG, intracerebral EEG, and unit firing in multiple brain regions of neurosurgical patients. We find that most sleep slow waves and the underlying active and inactive neuronal states occur locally. Thus, especially in late sleep, some regions can be active while others are silent. We also find that slow waves can propagate, usually from medial prefrontal cortex to the medial temporal lobe and hippocampus. Sleep spindles, the other hallmark of NREM sleep EEG, are likewise predominantly local. Thus, intracerebral communication during sleep is constrained because slow and spindle oscillations often occur out-of-phase in different brain regions.

Copyright © 2011 Elsevier Inc. All rights reserved.

Figures

Figure 1. Sleep Studies and Data Overview

(A) Set-up for polysomnographic sleep recordings. (B) Hypnogram: time-course ofsleep-wake stages in one representative individual. W, wake; R, REM sleep; N1-N3, NREM sleep, stages 1–3. (C) Average power spectra of scalp EEG in the same individual computed separately in stages N2 (blue), N3 (red) and REM sleep (green). Note high power in slow wave (

Figure 2. Example of EEG and Single-Unit Activity During Global Sleep Slow Waves

Example of EEG and unit activities in multiple brain regions during 11.5 s of deep NREM sleep in one individual. Rows (top to bottom) depict activity in scalp EEG (Cz), right supplementary motor area (R-SMA), left entorhinal cortex (L-EC), right entorhinal cortex (R-EC), left hippocampus (L-HC), and left amygdala (L-Am). Red, scalp EEG; blue, depth EEG; black lines, unit spikes. Cyan dots show individual slow waves detected automatically in each channel separately. Gray and white vertical bars mark ON and OFF periods occurring in unison across multiple brain regions.

Figure 3. Spiking Activity Underlying EEG Slow Waves

(A) Wave-triggered averaging of unit spiking activity, time locked to the positive peak of depth EEG slow waves (OFF periods). Red shades denote SEM across neurons (n = 600 in all brain structures). (B) Same as (A) time locked to the negative peak of depth EEG slow waves (ON periods). (C) Examples of neurons in different individuals and brain structures with spiking activity highly linked to EEG slow waves. (D) Percentage of units linked to sleep slow waves in different brain structures. Numbers at base of bars mark the number of units in each brain structure, and error bars denote SEM across electrodes. (E) Wave-triggered averaging of units in all brain structures (n = 600) as a function of amplitude of the positive EEG peaks. Slow waves were divided into three amplitude percentiles so that black, dark gray, and light gray depict high, medium, and low-amplitude slow waves, respectively.

Figure 4. Local Sleep Slow Waves

(A) An example of local sleep slow waves occurring at different times in left and right posterior cingulate cortices, where 100% of units are locked to slow waves. Rows (top to bottom) depict activity in scalp EEG (Cz, red), left posterior cingulate, and right posterior cingulate. Blue, depth EEG; green, MUA; black lines, single-unit spikes. White shadings mark local OFF periods. (B) Units in concordant “target” regions (expressing the same EEG slow wave, upper panel) exhibit a clear OFF period, while units in non-concordant “target” regions do not exhibit a clear OFF period. Wave-triggered averaging of “target” units is time locked to the positive peak of depth EEG in the “seed” region (blue trace). Red shades denote SEM across neurons (n = 410 in all brain structures). (C) Most slow waves are local. Distribution of slow wave involvement (percentage of monitored brain structures expressing each wave) is shown. (D) Global slow waves are of high amplitude. Scatter plot shows slow wave amplitudes as a function of involvement (percentage of brain structures expressing each wave). (E) A graph depicting the tendency of each pair of regions to express waves concordantly. Nodes (individual regions) are depicted schematically as seen from above, where deep regions away from scalp (MTL) are smaller and scalp electrodes are surrounded by cyan. Node color (legend) denotes the rank of each region—that is, how often it is involved inslow waves seen in other regions. Edge width and color (legend) denotes the probability of each pair of regions to express slow waves concordantly. Note that regions in prefrontal cortex have higher ranks and show concordance across hemispheres relative to MTL.

Figure 5. Local Sleep Spindles

(A) Three examples in three different patients of depth EEG along with corresponding spectrograms in the spindle frequency range (10–16 Hz) during 15 s of NREM sleep. Note that, regardless of slow waves, local spindles often occur without spindle activity in other regions, including homotopic regions across hemispheres and regions with equivalent SNR showing the same global slow waves. (B) Quantitative analysis of spindle occurrence across pairs of channels. Top row (concordant events, 34% of cases) shows spectrograms for events in which a spindle was detected in the “seed” channel but not in the “target” channel (N = 13,750 events in 156 pairs of regions in 12 individuals). Bottom row (nonconcordant events, 66% of cases) shows spectrograms for events in which a spindle was detected in the seed channel but not detected in the target channel (N = 26,874 events in 156 pairs of regions in 12 individuals). Note that spindle power in nonconcordant target channels is at near-chance levels, indicating that our detection can reliably separate between presence and absence of spindle activity. (C) Distribution of involvement (percentage of monitored brain structures expressing each spindle) is shown across 22,914 spindles in 50 electrodes of 12 individuals. Note that the distribution is skewed to the left, indicating that most spindles are local. (D) Scatter plot of spindle amplitudes as a function of involvement (percentage of brain structures expressing each spindle) shows that global spindles have some tendency to be of higher amplitude (r = 0.63; p

Figure 6. Changes in Spatial Extent of Slow Waves and Spindles Between Early and Late Sleep

(A) Slow waves become more local in late sleep. Slow wave involvement (percentage of monitored brain structures expressing each wave) in early NREM sleep versus late NREM sleep in five individuals exhibiting a clear homeostatic decline of SWA during sleep (Figure S1). Error bars denote SEM (n = 1436 and 1698 events in early and late sleep). Asterisk denotes statistically significant difference (p −10, unequal variance t test). (B) Kcomplexes are more global and similarinearly and latesleep. Same sleep segments and analysisas (A). Error barsdenote SEM (n = 148 and 181 eventsinearly and late sleep; p = 0.98, unequal variance t test). (C) Spindles become more global in late sleep. Same sleep segments and analysis as (A). Error bars denote SEM (n = 1272 and 2554 events in early and late sleep, p

Figure 7. Sleep Slow Waves Propagate Across Typical Paths

(A) Left: Average depth EEG slow waves in different brain structures of one individual illustrate propagation from frontal cortex (yellow) to MTL (red). All slow waves are triggered by scalp EEG negativity. Black, scalp mean waveform. Right: Distributions of time lags for individual waves in supplementary motor area (SM, yellow) and hippocampus (HC, red) relative to scalp. (B) Mean position in sequences of propagating waves in all 129 electrodes across 13 individuals. Each circle denotes one depth electrode according to its precise anatomical location. Yellow-red colors denote waves observed sooner in frontal cortex compared with MTL (see legend). (C) Quantitative analysis: mean position in propagation sequences as a function of brain region. Abbreviations: SM, supplementary motor area; PC, posterior cingulate; OF, orbitofrontal cortex; AC, anterior cingulate; ST, superior temporal gyrus; EC, entorhinal cortex; Am, amygdala; HC, hippocampus; PH, parahippocampal gyrus. (D) An example of individual slow waves propagating from frontal cortex to MTL. Rows (top to bottom) depict activity in scalp EEG (Cz, red), supplementary motor area (SM), entorhinal cortex (EC), hippocampus (HC), and amygdala (Am). Colors denote the following: blue, depth EEG; green, MUA; and black lines, spikes of isolated units. Red dots mark center of OFF periods in each brain region based on the middle of silent intervals as defined by last and first spikes across the local population. Diagonal green lines are fitted to OFF period times via linear regression and illustrate propagation trend. (E) Left: The average unit activity in frontal cortex (top, n = 76) and MTL (bottom, n = 155), triggered by the same scalp slow waves reveals a robust time delay (illustrated by vertical red arrow). Right: Distribution of time delays in individual frontal (top) and MTL (bottom) units reveals a time delay of 187 ms. Red vertical arrows denote mean time offset relative to scalp EEG. (F) Left: The average unit activity in parahippocampal gyrus (PH, n = 32), entorhinal cortex (EC, n = 49) and hippocampus (HC, n = 35), triggered by the same depth EEG slow waves reveals a cortico-hippocampal gradient of slow wave occurrence (illustrated by vertical red arrows). Right: Distribution of time delays in individual PH, EC, and HC units reveals a time delay of 121 ms between PH and HC. Red vertical arrows denote mean time offset relative to depth EEG.

Figure 8. Afferent Information Predicts Occurrence and Timing of Activity Onsets in Individual Slow Waves

(A) Predicting occurrence of individual amygdala slow waves with information about slow waves in other limbic regions. Each subpanel shows prediction accuracies for one patient using either ipsilateral (red) or contralateral (blue) information, as a function of the number of regions made available to the classifier. Green horizontal line shows chance prediction at 50%. Error bars denote SEM across 100 classifier iterations in which training and testing datasets (individual waves) as well as the identity of available neighbors were shuffled. Black and gray asterisks denote significant differences in prediction accuracy (p−39 in all nine individuals, paired t test, n = 100 classifier iterations). (B) Same as above when predicting the timing of individual amygdala slow waves (before or after slow waves in parietal scalp electrode Pz). Timing prediction was likewise more accurate based on ipsilateral information (p −7 in eight individuals and p = 0.02 in ninth individual, paired t test, n = 100 classifier iterations).