Source modeling sleep slow waves

Michael Murphya,b,1, Brady A. Riednera,b,c,1, Reto Hubera, Marcello Massiminia, Fabio Ferrarellia, and Giulio Tononia,2

aDepartment of Psychiatry, University of Wisconsin, Madison, WI 53719; and bNeuroscience Training Program and cClinical Neuroengineering Training Program, University of Wisconsin, Madison, WI 53706

Edited by Marcus E. Raichle, Washington University School of Medicine, St. Louis, MO, and approved December 12, 2008 (received for review August 11, 2008) Slow waves are the most prominent electroencephalographic (EEG) distant from the recording electrodes (13). Intracranial recordings feature of sleep. These waves arise from the synchronization of slow are limited in scope and sensitivity; it is impossible to fully sample oscillations in the membrane potentials of millions of neurons. Scalp- the large cortical areas thought to be involved in slow waves. level studies have indicated that slow waves are not instantaneous Imaging modalities like functional magnetic resonance imaging events, but rather they travel across the brain. Previous studies of EEG (fMRI) and positron emission tomography (PET) possess superior slow waves were limited by the poor spatial resolution of EEGs and spatial resolution to EEG source modeling, but their temporal by the difficulty of relating scalp potentials to the activity of the resolution is insufficient to capture the changes in cortical activity underlying cortex. Here we use high-density EEG (hd-EEG) source that occur during an individual slow wave (14, 15). Source-level modeling to show that individual spontaneous slow waves have analyses of hd-EEG sleep slow waves, however, offer a considerable distinct cortical origins, propagate uniquely across the cortex, and advantage in temporal resolution while achieving much higher involve unique subsets of cortical structures. However, when the spatial resolution than traditional scalp recordings, closer to that of waves are examined en masse, we find that there are diffuse hot other functional imaging methods. Therefore, in this study, we used spots of slow wave origins centered on the lateral sulci. Furthermore, hd-EEG source modeling to investigate the origin, propagation, and .slow wave propagation along the anterior؊posterior axis of the cortical involvement of individual sleep slow waves brain is largely mediated by a cingulate highway. As a group, slow waves are associated with large currents in the , Results the , the , the anterior Source Modeling Individual Slow Waves Induced by Transcranial cingulate, the , and the posterior cingulate. These areas Magnetic Stimulation (TMS) Reliably Produces Maximal Sources Con- overlap with the major connectional backbone of the cortex and with sistent with the Induction Site. In a previous study, we demonstrated many parts of the default network. that individual pulses of TMS during sleep elicit EEG slow waves that closely resemble spontaneous sleep slow waves (16). For the brain mapping ͉ default network ͉ electroencephalography ͉ current study, we capitalized on this phenomenon by first source slow oscillation ͉ traveling wave modeling these evoked slow waves. This allowed us to ascertain whether our source localization method produced consistent results he transition from waking to sleep in humans is accompanied in the case where the locus of the induction of the slow wave was Tby dramatic changes in the electroencephalograph (EEG). The predetermined. Briefly, we recorded EEGs from 15 subjects with waking EEG is dominated by low-voltage fast activity, while during a TMS compatible system (Nexstim). After the subjects had tran- non-rapid eye movement (NREM) sleep low-frequency compo- sitioned into stage 2 NREM sleep, we applied TMS pulses targeted nents appear in the EEG. During sleep, cortical neurons exhibit a to midline sensorimotor cortex (Fig. 1A). We selected a subject with characteristic bistability, oscillating about once every second from a minimal TMS artifact and analyzed the TMS-evoked slow waves a hyperpolarized downstate to a depolarized upstate (1). During the with several different source modeling methods, including L2 downstate these neurons are almost entirely silent whereas during minimum norm with the local autoregressive average (LAURA) the upstate neuronal firing rates can be as high as in quiet (17) constraint, low-resolution electromagnetic tomography wakefulness (2). Intracranial recordings of anesthetized cats indi- (LORETA) (18, 19), standardized low-resolution electromagnetic cate that the origins of the slow oscillation are cortical and that tomography (sLORETA) (20, 21), and the Bayesian minimum cortico–cortical connections are necessary for its synchronization norm (21) [see Methods and SI Text]. To normalize the currents, we (3). Large groups of slowly oscillating neurons synchronize during selected5sofpreceding EEG without slow waves, spindles, or sleep and the currents produced concomitantly with these changes artifacts as a baseline (see Methods). Regardless of the algorithm, in membrane potential sum to generate large slow waves in the scalp TMS-triggered slow waves spread through source space in a ste- EEG (4). Increasing evidence suggests that slow waves may mediate reotyped pattern (Fig. 1 B and C). About 120 ms after the TMS some of the functional benefits of sleep, at both the cellular and the pulse, relative current begins to increase in the , systems levels (5–7). precuneus, and cingulate gyrus, directly below the site of TMS Topographical analyses of slow waves in humans have shown that stimulation on the scalp. Near the negative peak of the potential they have a nonuniform scalp distribution, suggesting that areas of wave, these sources increase in strength and are joined by relative the cortex are differentially involved in slow waves (8, 9). Further- currents in the anterior and posterior cingulate, lateral parietal, and more, recent analyses have suggested that individual slow waves, some temporal areas. As the wave weakens, most activation retreats rather than being simultaneous events, likely travel throughout the cortex (9). In addition, a growing body of evidence supports the idea that sleep slow waves can be locally regulated (10–12). Therefore, Author contributions: G.T. designed research; M. Murphy, B.A.R., R.H., M. Massimini, and questions remain about the role of specific cortical structures in F.F. performed research; M. Murphy and B.A.R. analyzed data; and M. Murphy, B.A.R., and individual slow waves. Where do slow waves originate? Do they G.T. wrote the paper. travel through specific pathways or globally invade the cortex? Do The authors declare no conflict of interest. all cortical areas participate equally in slow waves? This article is a PNAS Direct Submission. Although the slow oscillation has been investigated using several 1M. Murphy and B.A.R. contributed equally to this work. different techniques, source modeling of high-density EEG (hd- 2To whom correspondence should be addressed. E-mail: [email protected]. EEG) sleep slow waves offers a novel way to answer these questions. This article contains supporting information online at www.pnas.org/cgi/content/full/ Scalp-level analyses of EEG sleep slow waves are made difficult by 0807933106/DCSupplemental. the fact that local potentials can be generated by sources that are © 2009 by The National Academy of Sciences of the USA

1608–1613 ͉ PNAS ͉ February 3, 2009 ͉ vol. 106 ͉ no. 5 www.pnas.org͞cgi͞doi͞10.1073͞pnas.0807933106 Downloaded by guest on October 1, 2021 the other wave starts in the left hemisphere near the insula, invades the cingulate gyrus, and then migrates through the right . The results were qualitatively similar for other source mod- eling algorithms. Our data indicate that spontaneous sleep slow waves are quite heterogeneous, and therefore the mathematical ‘‘average track’’ is not a clear demonstration of how these waves act. Qualitatively, the ‘‘average’’ sleep slow wave probably begins around the insula, propagates posteriorly along midline structures, and includes large currents in the frontal gyri, anterior cingulate, precuneus, and posterior cingulate. However, because each spon- taneous wave is an idiosyncratic event, we systematically charac- terized individual waves by determining their origin, pattern of propagation, and cortical areas involved.

Spontaneous Slow Waves Originate More Frequently in the Insula and Cingulate Gyrus. For every slow wave, we calculated which voxels were likely to be the origin of that wave (see Methods and SI Text). Examination of the origins of spontaneous waves revealed a con- sistent pattern across subjects (Fig. 3 A and B and Fig. S1). Although slow waves could originate in either hemisphere, 17 of the top 20 voxels with the most origins were in the left hemisphere. In terms of regional topography, we found a diffuse hot spot of origins centered on the (including regions of insular, tempo- ral, frontal, and parietal cortices). Approximately 46% of sponta- neous slow waves across all subjects included at least one insular voxel in the origin. There was a secondary disjoint hot spot for slow wave origins (Ϸ12%) around the medial cingulate gyrus (Fig. 3A).

Like the lateral hot spot, the medial cingulate gyrus hot spot also NEUROSCIENCE included adjacent portions of the neighboring cortical structures. This secondary origin hot spot was not evident in all subjects (Fig. 3B). Previous analysis of surface EEG slow waves suggested that many waves originated from the anteriormost part of the scalp, often Fig. 1. TMS-evoked slow waves travel across the cortex in a stereotyped laterally, while a second group of waves originated centromedially manner. (A) Anatomical MRI showing the site of TMS stimulation (green cross- (9). To compare our results with those measured from the scalp, we hairs). (B) Representative butterfly plot of all 60-channel mastoid referenced EEG separated waves into 2 groups on the basis of whether they had at traces showing the response to an individual TMS pulse (green bar). (C) Cortical least 1 probabilistic origin voxel in the left insula but not the flat maps illustrating the relative current density during the slow wave at times cingulate gyrus (or vice versa). We then calculated the origins of the after TMS indicated by the red lines in B. PCun, precuneus; CG, cingulate gyrus; surface EEG waves (see Methods) in each group (Fig. 3C). We AC, anterior cingulate; PosC, postcentral gyrus; INS, insula; PCC, posterior found that the relative current waves that originated in the left cingulate. insula most often manifested on the scalp as potential waves that began at the left anterior portion of the scalp. Relative current waves that originated in the cingulate gyrus typically manifested on back to the area around the induction site. The response is so robust the scalp as potential waves with centromedial origins. that the source model of any individual TMS-triggered slow wave closely approximates the source model of the average of all of the Spontaneous Slow Waves Stereotypically Propagate Along Certain responses recorded during a single session. The variance within Paths. For each slow wave, we used the timing of the relative current each voxel across different waves is always less than the variance maxima in each voxel to map how the wave propagated across the across the voxels within each single wave. We found that the origin, cortex (see Methods and SI Text). We found that each slow wave propagation, and involvement (see Methods) for TMS-evoked slow travels uniquely across the cortex (see individual example in Fig. waves were highly consistent across waves. 4A). The propagation speed in source space ranged from 0.4 to 6.3 m/s. The average speed was Ϸ2.2 m/s, was remarkably consistent Source Modeling Spontaneous Slow Waves Reveals a Unique Cortical across subjects (ranging from 2.0 to 2.3 m/s), and was roughly in Current Distribution for Each Wave. Having confirmed that our agreement with previous estimations from EEG and computer analysis correctly localized the current sources of individual TMS- simulations (9, 23). Visual inspection of the streamlines for the evoked slow waves, we next examined the source modeled behavior spontaneous slow waves for each subject suggested the existence of of spontaneous sleep slow waves. Hd-EEG was collected from 6 a mesial slow wave highway oriented along the anterior–posterior subjects during waking and sleep. After appropriate filtering of the axis (Fig. 4B and Fig. S2): regardless of where they originated, waves data (see Methods), we used a detection algorithm allowing for an that traveled along the anterior–posterior axis usually did so along unbiased detection of slow waves in terms of size and location, to the mesial aspect of the cortex. identify waves during slow wave sleep (NREM stages 3 and 4) of To determine what structures were included in this mesial slow the first sleep cycle (see SI Text). The number of waves selected for wave highway, we calculated the number of streamlines that passed this analysis for each subject ranged from 60 to 237. through each voxel (Fig. 4 C and D and Fig. S2). As expected given We found that, unlike the TMS-evoked slow waves, each spon- the origin results, there were high densities of streamlines in the taneous slow wave showed a unique pattern of propagation across insula. However, the highest density of streamlines was in the the cortex. For example, two representative waves are shown in Fig. anterior portions of the cingulate. There were also high densities of 2. These waves originate from different cortical areas and involve streamlines in the posterior cingulate and precuneus. These mesial distinct but overlapping portions of the cortical surface. One wave streamlines are not merely the result of the waves originating in the spreads caudally and bilaterally from the anterior cingulate, while cingulate gyrus because more slow waves propagate through the

Murphy et al. PNAS ͉ February 3, 2009 ͉ vol. 106 ͉ no. 5 ͉ 1609 Downloaded by guest on October 1, 2021 Fig. 2. Individual spontaneous slow waves are unique cortical events. (A and C) The butterfly plots of all 256-channel mastoid referenced EEG traces for 2 spontaneous slow waves are shown. (B) Cortical flat maps showing relative current density at the time points indicated by the red lines for wave A.(D) Cortical flat maps showing relative current density at the time points indicated by the red lines for wave C. Note the difference between the first wave (A and B) where recruitment occurs from a relatively stationary hot spot and the second wave (C and D), where there is distinct traveling from one hemisphere to the other. AC, anterior cingulate; MedF, medial frontal gyrus; INS, Insula; CG, cingulate gyrus; ITG, .

cingulate gyrus than originate there (paired t test, P Ͻ 0.005). highway. To a lesser extent this highway also incorporates dorsal Therefore, many waves originate elsewhere and then propagate to midline structures such as the . or through the , confirming the existence of a mesial Propagation along the highway could occur in either direction, but most often traveling occurred from front to back. For each voxel, we also examined the average direction of all streamlines that passed through and calculated in what dimension the average direction vectors were anisotropic. In the highway, the largest group of voxels was anisotropic along the anterior–posterior axis (43.4%, standard deviation 7.0%) with the average direction vector heading posteriorly for each subject. There was comparatively less move- ment along the anterior–posterior axis in the lateral aspect of the cortex. The percentage of anisotropic voxels in the highway was significantly larger than in the rest of the brain (26.1%, standard deviation 1.8%, paired t test, P Ͻ 0.005). Furthermore, waves travel about the same distance along the highway whether they are moving anteriorly (4.0 dipoles/wave) or posteriorly (3.6 dipoles/wave, paired t test, P Ͼ 0.3). Waves that moved anteriorly on the highway tended to have more posterior origins than waves that moved posteriorly, but this difference was not statistically significant.

Spontaneous Slow Waves Preferentially Involve a Subset of Brain Fig. 3. Spontaneous slow waves can originate from anywhere in the cortex, but Areas and Avoid Others. preferentially originate in the insula or the cingulate gyrus. (A) Average cortical We next examined the amount of current flat map showing the percentage of slow waves that included that voxel as an produced by different cortical areas during a slow wave. For each origin. There are two disjoint origin hot spots, one centered around the insula and voxel, we defined the involvement in that voxel for each individual one centered around the cingulate gyrus. (B) Cortical flat maps for each subject. slow wave as the average relative current during a 100 ms window The insula origin hot spot is strongly present in every subject, and the cingulate centered on the negative peak of the EEG slow wave (see SI Text). gyrus hot spot is at least weakly apparent in every subject. (C) The scalp origins of This analysis revealed that certain brain structures are more in- waves that originate in either the left insula or the cingulate gyrus. (Upper)We volved than others by sleep slow waves. Relative current hot spots selected waves with source origins that contained voxels from the left insula hot included the precuneus, the cingulate gyrus, the posterior cingulate, spot but not the cingulate gyrus hot spot. The topoplot indicates the percentage the anterior cingulate, and left and to a lesser extent right frontal of waves that originate near each electrode, calculated using the scalp EEG method (9, 58). Waves with insula cortical origins often appeared on the surface structures including the middle frontal gyri, the medial frontal gyri, as if they originated from the left anterior edge of the scalp. (Lower) Waves with the inferior frontal gyri, and the insula (Fig. 5 and Fig. S1). This origins that contained voxels from the cingulate gyrus hot spot but not the left result could not be inferred from the voltage topography alone (Fig. insula hot spot typically appear on the scalp as EEG waves originating centrome- S3). While there was some variation between subjects (Fig. 5B), on dially. INS, Insula; CG, cingulate gyrus. the whole, the left middle, medial, and inferior frontal gyri were

1610 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0807933106 Murphy et al. Downloaded by guest on October 1, 2021 Fig. 4. Spontaneous slow waves often propagate via the cingulate. For each wave, we calculated a streamline for each origin voxel. (A) The origin voxels (red circles) and streamlines (red tubes) for a single slow wave overlaid on an anatomical MRI. This slow wave originates primarily from right anterior cortex (including the inferior frontal and middle frontal gyri). It propagates medially and posteriorly. (B) Streamlines for an individual subject. Only the longest 10 streamlines for each wave are shown. A dense streamline highway can be seen running along the anterior–posterior axis in the medial portion of the cortex. (C) For each voxel, we calculated the number of waves with streamlines that passed through it. A flat cortical map of the propagation density is shown for each voxel, expressed as a percentage of the total number of waves. Propagation hot spots are seen along the cingulate cortices. (D) Flat cortical maps of the propagation density for each subject. AC, anterior cingulate; CG, cingulate gyrus; INS, insula; PCC, posterior cingulate.

more involved than the right middle, medial, and inferior frontal that each of these waves has a stereotyped source topography with gyri (paired t test, P Ͻ 0.05). There was also a subset of areas that the largest relative currents under the site of stimulation. We then

typically showed weak involvement. This subset included some source modeled individual spontaneous slow waves and determined NEUROSCIENCE sensory areas (such as the postcentral gyrus, , and that each corresponds to a unique relative current distribution. association areas) and the , the middle While individual slow waves could originate from a wide variety of temporal gyrus, and regions that were electrotonically distant from cortical locations, as a group, there was some indication that some the recording electrodes (such as the , hippocampus, and areas were more likely to be origins, namely the insula and, to a ) (Fig. 5). lesser extent, the cingulate gyrus. Slow waves often travel along the anterior–posterior axis via the cingulate cortices. In addition, slow Discussion Slow waves are the most prominent electrophysiological waves preferentially involve several cortical areas including the feature of NREM sleep. In this paper, we first validated our source inferior frontal gyrus, the medial frontal gyrus, the middle frontal modeling results by analyzing slow waves evoked by TMS. We found gyrus, the insula, the anterior cingulate gyrus, the posterior cingu- late gyrus, and the precuneus. Slow waves for the most part avoid the visual and sensory cortices and the fusiform gyrus.

Source Modeling hd-EEG Is Uniquely Suited to Studying Individual Slow Waves. While other neuroimaging techniques like PET and fMRI offer superior spatial resolution, source modeling of hd-EEG is uniquely suited to the analysis of sleep slow waves. First, the superior time resolution of EEG permits tracking the evolution of individual slow waves. Furthermore, although source modeling typically restricts the space to cortical sources, evidence from several different modalities suggests that the sleep slow oscillations do indeed originate in, travel across, and are maintained by the cortex. Isolated cortical slabs undergo slow oscillations, suggesting that the cortex is sufficient for the generation of slow waves (24, 25). Disrupting thalamo–cortical connections leaves the slow oscillation intact while damaging cortico–cortical connections disrupts the synchronization of the slow oscillation (3). Additionally, TMS does not directly reach Fig. 5. Spontaneous slow waves preferentially involve certain cortical areas. For each voxel, we averaged the relative current over a 100-ms window centered on subcortical structures (26), yet it can reliably elicit slow waves the negative peak of the EEG wave. (A) Cortical flat map displaying for all waves (15). Spreading upstates have been observed in multiple- the total involvement of each voxel, expressed as a percentage of the maximum. electrode single-unit recordings of cortical neurons during slow Involvement hot spots include the anterior cingulate, the cingulate, the posterior wave oscillations in anesthesia (27). The possibility of a subcor- cingulate, the precuneus, and the left inferior frontal gyrus and insula. Involve- tical basis for EEG slow waves is also discounted by the fact that ment cold spots include the inferior and middle temporal gyri, the middle cortical decreases in regional cerebral blood flow during NREM occipital gyrus, the postcentral gyrus, the inferior parietal lobule, the fusiform sleep are correlated with EEG delta power and are independent gyrus, and the . (B) Cortical flat maps for each subject. of thalamic activity (28). Furthermore, the variability between Although the relative values of the involvement hot spots vary between subjects, the same areas are highly involved in all subjects. IPL, inferior parietal lobule; IFG, individual spontaneous EEG slow waves suggests that a single inferior frontal gyrus; MOG, middle occipital gyrus; FG, fusiform gyrus; MTG, subcortical substrate is unlikely (9). ; PHG, parahippocampal gyrus. AC, anterior cingulate; CG, While the above evidence suggests that source modeling is cingulate gyrus; INS, insula; PosC, postcentral gyrus; PCC, posterior cingulate; particularly well suited to describing slow waves, there are several PCun, precuneus; ITG, inferior temporal gyrus. caveats. First, despite improvements in source modeling, there is the

Murphy et al. PNAS ͉ February 3, 2009 ͉ vol. 106 ͉ no. 5 ͉ 1611 Downloaded by guest on October 1, 2021 fundamental limitation that infinitely many cortical current distri- We found that slow waves were associated with large relative butions can give rise to a given scalp voltage topography (13). In currents in the middle frontal, medial frontal, and inferior frontal addition, there are a variety of technical limitations specific to our cortices, as well as the anterior cingulate, cingulate, insula, posterior analysis. We used the same MRI and set of channel locations for cingulate, and precuneus. By contrast, sensory areas, such as the each subject; as individual subjects and net placements vary from postcentral and , did not contribute much current to this ideal, the appropriateness of this approximation decreases. Our slow waves. This result is in agreement with previous PET studies forward model was relatively simple, consisting of 4 nested isotropic suggesting that decreases in regional metabolism during NREM spheres. More sophisticated forward models incorporate complex sleep occur in the anterior cingulate, insula, medial frontal cortex, geometry and/or anisotropy (29). While each of the source mod- and precuneus (28, 45, 46). There was also considerable overlap eling techniques used here revealed similar results, future refine- between areas that were consistently not involved in slow waves and ments in forward or inverse modeling should further improve the areas that showed increases in regional metabolism. It is unclear localization of current sources through hd-EEG. why different brain areas are differentially involved in slow waves. It may be that only certain areas of the brain possess the necessary Current Sources Reflect the Cellular Physiology of the Slow Oscilla- dendritic geometry to produce such large scalp potentials (13). tion. We found large relative current maxima around the negative However, increasing evidence suggests that sleep plays an impor- peak of the EEG slow wave. Prior results indicate that this negative tant role in brain plasticity and that it can be locally regulated (11, peak occurs at the same time as a positive peak in the local field 47, 48). Therefore, one intriguing possibility is that during waking potential (LFP) in the underlying cortex (30, 31). The timing of primary sensory areas may undergo less plasticity than the higher- these peaks reflects the beginning of the transition from downstate order cortical areas and therefore may be less involved in slow to upstate and the resumption of neural firing (9, 32, 33). Unit waves. recording studies also support this interpretation (34). While cor- tical currents are typically thought to arise from the summation of Sleep Slow Waves Propagate Across the Default Network. Slow waves inhibitory and excitatory postsynaptic potentials from the local preferentially propagate along the anterior–posterior axis through neural population (13), synaptic input is greatly reduced during the the cingulate gyrus and neighboring structures. This slow wave downstate (23, 35–37). This suggests that postsynaptic potentials are highway may act to functionally connect anterior high-involvement not entirely responsible for the large currents recorded during a slow areas (the anterior cingulate, middle frontal gyri, and inferior wave. Although the precise mechanism by which these cortical currents frontal gyri) with posterior high-involvement areas (the precuneus, are generated is unknown, contributing factors could be synchro- the posterior cingulate). Diffusion spectrum imaging (DSI) has nous changes in cellular currents including the hyperpolarization- revealed that an anatomical backbone of fibers runs along the activated cation current and/or the potassium leak current (23). anterior–posterior axis (49). The overlap between the highly inter- connected regions making up the backbone and the cortical areas Origins, Propagation, and Involvement Each Reveal Novel Information that are highly traveled by slow waves is striking. This suggests that About the Cortical Generators of Slow Waves. We used 3 parameters a functional role of the connectional backbone revealed by DSI may to characterize slow waves in source space: origin, propagation, and be to mediate the propagation of sleep slow waves between distinct involvement. Slow waves often originate from areas adjacent to the cortical areas. Perhaps the repeated occurrence of downstates in lateral sulci (insula, superior temporal gyri), preferentially propa- this connectivity backbone (49) may contribute to the disruption of gate via the cingulate cortices, and typically involve the anterior information transmission within the that accompa- cingulate gyrus, the middle, medial, and inferior frontal gyri, and nies deep stages of NREM sleep (50). The areas showing maximal the posterior cingulate and precuneus. This means that, as is the involvement in slow waves also show considerable overlap with the case with surface analyses (9), slow waves are not always largest default network, consistent with recently published event-related where they originate; rather, they can grow in relative current fMRI data, triggered on the negative peak of spontaneous slow amplitude as they travel. We also noted that slow waves were more waves (15). This functional network was first identified as a set of likely to originate in the left insula than in the right insula and that brain regions active during rest that undergo task-dependent de- slow waves on average involved left frontal gyri more than they creases in activity as measured by fMRI and PET (51, 52). involved right frontal gyri. Although topographic studies of human Subsequent observations revealed that this network exhibits cor- sleep EEG have failed to detect consistent interhemispheric dif- relations in spontaneous BOLD signal (53, 54) that persist into ferences in slow wave activity (SWA, EEG power 0.5–4.5 Hz), such sleep (55). The overlap between default network structures and the differences have been observed in other animals (38–40). Further- areas maximally involved by sleep slow waves is intriguing, consid- more, sleep deprivation elicits larger rebounds in SWA in the left ering that this network has been implicated in monitoring the hemisphere (8, 41). external environment, social cognition, and memory (51, 56) and The probabilistic origin of the slow wave is an estimate of where that it can be altered by sleep deprivation (57). the transition from the downstate to the upstate first occurs before additional areas are recruited. Although every voxel was included Methods in the origin of at least one slow wave, there was a hot spot of origins Subjects and Recordings. Six male subjects (ages 24–35, 3 right-handed) partic- centered on the lateral sulci and spreading into the adjacent insular, ipated in the spontaneous sleep portion of the study. All participants gave written temporal, parietal, and frontal cortices. Our findings suggest that informed consent, and the experiment was approved by the University of Wis- the transition to the upstate can begin anywhere in the cortex but consin Human Subjects Committee. Hd-EEG (Geodesic, 256 electrodes) was re- preferentially begins in the area around the insula and superior corded across an entire night of sleep. EEG recordings were sampled at 500 Hz. temporal gyrus. The reason for this regional origin selectivity is Noisy channels were replaced with spline interpolations using NetStation soft- unknown. It may be that upstates that originate in these areas are ware (EGI). The EEG was visually scored for sleep stages (20-s epochs) on the basis better able to propagate to other cortical areas and manifest on the of standard criteria (58). Artifacts were rejected on the basis of an automated scalp. This agrees with the observation that the area around the threshold-crossing detection algorithm. Subjects who had TMS also had T1 weighted MRIs (resolution 0.5 mm) acquired with a 3T GE Signa scanner. lateral sulcus is important for the initiation and propagation of seizures in many cases of temporal lobe epilepsy (42, 43). Intrigu- Transcranial Magnetic Stimulation Data. The data for the TMS-evoked slow ingly, patients with insular strokes report significantly more tired- waves were collected from a single male subject (age 21) as part of a previous ness than patients with similar strokes that do not involve the insula study (16). In this prior study, we used a 60-channel TMS-compatible EEG net (44). It is possible that impaired slow waves contribute to this (Nexstim) to record the response to TMS in 15 sleeping males (ages 21–36). clinical presentation. Stimulation of the sensorimotor cortex at 65% of maximal stimulator output

1612 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0807933106 Murphy et al. Downloaded by guest on October 1, 2021 reliably elicited high-amplitude (Ͼ80 ␮V) slow wave responses (negative zero K-complexes, or movement artifacts. Relative current calculations were per- crossing to subsequent positive zero crossing duration of 0.25–1 s) that began formed using MATLAB (The Math Works). Ϸ125 ms after delivery of a pulse. Targeting of the sensorimotor cortex was achieved using a brain navigated stimulation of the individual subject’s MRI. The Relative Current Maxima, Origins, and Propagation. For each slow wave, we sound of the TMS click was masked throughout the stimulation period of sleep by defined a window of 100 ms centered on the detection of the negative voltage playing a digitized continuous version of the click through ear phones. peak. We restricted our analysis to the behavior of relative current voxels within this window. We set a threshold equal to 25% of the maximum relative current Source Localization. Source localization was performed on filtered (0.5–6 Hz) value achieved on this interval. Next, for each voxel, we determined the timing of EEG data, using a 4-shell head model derived from a magnetic resonance image any local maxima that occurred during our time window of interest. We rejected of an individual whose head closely approximates the Montreal Neurological all maxima that did not exceed the threshold. For each voxel, we selected the Institute head. A standard, coregistered set of electrode positions was used for maxima that occurred most closely to the voltage peak. If 2 maxima were the construction of the forward model. The inverse matrix was calculated using equidistant from the detection time, we selected the earliest maximum. This the minimum norm least-squares (L2) method, subject to depth weighting, procedure ensured that every voxel had at most 1 maximum. These maxima were orientation weighting, truncated singular value decomposition regularization at sorted by the time they occurred. We defined the probabilistic origin as the Ϫ 10 3 to stabilize the solution, and the LAURA constraint (17). The source space earliest 10% of voxels to have a maximum for each wave (see SI Text). A was restricted to 2447 cortical voxels (7 mm3) that were each assigned to a gyrus 3-dimensional gradient based upon the timing was used to calculate streamlines on the basis of the Montreal Neurological Institute probabilistic atlas. All inverse for the propagation of each slow wave (see SI Text). modeling was performed using GeoSource (EGI). Relative current was deter- mined by dividing the inverse model of each slow wave by the average inverse ACKNOWLEDGMENTS. We thank David Balduzzi and Chiara Cirelli for their model of several (average ϭ 4.5) seconds of quiet waking data from the same helpful comments. This work was supported by Grant MH077967 (to G.T.) from subject during the same recording. For TMS, the baseline period was stage 2 sleep the National Institute of Mental Health and Grant NS05515 (to G.T.) from the dominated by low-voltage fast activity; there were no spontaneous slow waves, National Institute of Neurological Disorders and Stroke.

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