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• W2952410712 abstract "•Theta and alpha oscillations are spatially clustered in the human neocortex•Clustered oscillations display traveling waves•Traveling waves generally propagate in a posterior-to-anterior direction•Traveling waves can be modeled as coupled oscillators Human cognition requires the coordination of neural activity across widespread brain networks. Here, we describe a new mechanism for large-scale coordination in the human brain: traveling waves of theta and alpha oscillations. Examining direct brain recordings from neurosurgical patients performing a memory task, we found contiguous clusters of cortex in individual patients with oscillations at specific frequencies within 2 to 15 Hz. These oscillatory clusters displayed spatial phase gradients, indicating that they formed traveling waves that propagated at ∼0.25–0.75 m/s. Traveling waves were relevant behaviorally because their propagation correlated with task events and was more consistent when subjects performed the task well. Human traveling theta and alpha waves can be modeled by a network of coupled oscillators because the direction of wave propagation correlated with the spatial orientation of local frequency gradients. Our findings suggest that oscillations support brain connectivity by organizing neural processes across space and time. Human cognition requires the coordination of neural activity across widespread brain networks. Here, we describe a new mechanism for large-scale coordination in the human brain: traveling waves of theta and alpha oscillations. Examining direct brain recordings from neurosurgical patients performing a memory task, we found contiguous clusters of cortex in individual patients with oscillations at specific frequencies within 2 to 15 Hz. These oscillatory clusters displayed spatial phase gradients, indicating that they formed traveling waves that propagated at ∼0.25–0.75 m/s. Traveling waves were relevant behaviorally because their propagation correlated with task events and was more consistent when subjects performed the task well. Human traveling theta and alpha waves can be modeled by a network of coupled oscillators because the direction of wave propagation correlated with the spatial orientation of local frequency gradients. Our findings suggest that oscillations support brain connectivity by organizing neural processes across space and time. Oscillations have a distinctive role in brain function because they coordinate neuronal activity on multiple scales. Brain oscillations are important at the microscale, because they modulate the timing of neuronal spiking (Bragin et al., 1995Bragin A. Jandó G. Nádasdy Z. Hetke J. Wise K. Buzsáki G. Gamma (40-100 Hz) oscillation in the hippocampus of the behaving rat.J. Neurosci. 1995; 15: 47-60Crossref PubMed Google Scholar, Jacobs et al., 2007Jacobs J. Kahana M.J. Ekstrom A.D. Fried I. Brain oscillations control timing of single-neuron activity in humans.J. Neurosci. 2007; 27: 3839-3844Crossref PubMed Scopus (260) Google Scholar), and at the macroscale, where they synchronize distributed cortical networks that are communicating (Fries, 2005Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.Trends Cogn. Sci. 2005; 9: 474-480Abstract Full Text Full Text PDF PubMed Scopus (2717) Google Scholar). Owing to oscillations’ ability to coordinate neural processes across multiple scales, characterizing their spatiotemporal properties may reveal how neurons across multiple regions are dynamically coordinated to support behavior (Kopell et al., 2014Kopell N.J. Gritton H.J. Whittington M.A. Kramer M.A. Beyond the connectome: the dynome.Neuron. 2014; 83: 1319-1328Abstract Full Text Full Text PDF PubMed Scopus (208) Google Scholar). The human cortex displays oscillations at various frequencies during cognition (Buzsáki and Draguhn, 2004Buzsáki G. Draguhn A. Neuronal oscillations in cortical networks.Science. 2004; 304: 1926-1929Crossref PubMed Scopus (4108) Google Scholar). To understand how these patterns relate to behavior, researchers have generally examined the properties of oscillations at individual frequencies in local networks (Raghavachari et al., 2006Raghavachari S. Lisman J.E. Tully M. Madsen J.R. Bromfield E.B. Kahana M.J. Theta oscillations in human cortex during a working-memory task: evidence for local generators.J. Neurophysiol. 2006; 95: 1630-1638Crossref PubMed Scopus (228) Google Scholar, Jacobs et al., 2007Jacobs J. Kahana M.J. Ekstrom A.D. Fried I. Brain oscillations control timing of single-neuron activity in humans.J. Neurosci. 2007; 27: 3839-3844Crossref PubMed Scopus (260) Google Scholar) or in point-to-point links between distinct regions (Watrous et al., 2013Watrous A.J. Tandon N. Conner C.R. Pieters T. Ekstrom A.D. Frequency-specific network connectivity increases underlie accurate spatiotemporal memory retrieval.Nat. Neurosci. 2013; 16: 349-356Crossref PubMed Scopus (208) Google Scholar). These approaches ignore a key feature of cortical oscillations that emerged from animal studies—that oscillations at multiple frequencies form spatially continuous neural patterns (Freeman and Schneider, 1982Freeman W.J. Schneider W. Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors.Psychophysiology. 1982; 19: 44-56Crossref PubMed Scopus (305) Google Scholar, Freeman et al., 2000Freeman W.J. Rogers L.J. Holmes M.D. Silbergeld D.L. Spatial spectral analysis of human electrocorticograms including the alpha and gamma bands.J. Neurosci. Methods. 2000; 95: 111-121Crossref PubMed Scopus (228) Google Scholar, Agarwal et al., 2014Agarwal G. Stevenson I.H. Berényi A. Mizuseki K. Buzsáki G. Sommer F.T. Spatially distributed local fields in the hippocampus encode rat position.Science. 2014; 344: 626-630Crossref PubMed Scopus (89) Google Scholar). One such pattern is a traveling wave, which consists of a spatially coherent oscillation that propagates progressively across the cortex, reminiscent of a wave moving across water. Traveling waves have been studied most extensively in animal models, where they were observed most often in fine-scale recordings and were shown to be functionally important to various behaviors, including visual perception (Zanos et al., 2015Zanos T.P. Mineault P.J. Nasiotis K.T. Guitton D. Pack C.C. A sensorimotor role for traveling waves in primate visual cortex.Neuron. 2015; 85: 615-627Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar), spatial navigation (Lubenov and Siapas, 2009Lubenov E.V. Siapas A.G. Hippocampal theta oscillations are travelling waves.Nature. 2009; 459: 534-539Crossref PubMed Scopus (282) Google Scholar, Patel et al., 2012Patel J. Fujisawa S. Berényi A. Royer S. Buzsáki G. Traveling theta waves along the entire septotemporal axis of the hippocampus.Neuron. 2012; 75: 410-417Abstract Full Text Full Text PDF PubMed Scopus (156) Google Scholar), and movement (Rubino et al., 2006Rubino D. Robbins K.A. Hatsopoulos N.G. Propagating waves mediate information transfer in the motor cortex.Nat. Neurosci. 2006; 9: 1549-1557Crossref PubMed Scopus (308) Google Scholar). In conjunction with predictions of computational models, these findings suggest that traveling waves are a key mechanism for guiding the spatial propagation of neural activity and computational processes across the brain (Ermentrout and Kleinfeld, 2001Ermentrout G.B. Kleinfeld D. Traveling electrical waves in cortex: insights from phase dynamics and speculation on a computational role.Neuron. 2001; 29: 33-44Abstract Full Text Full Text PDF PubMed Scopus (316) Google Scholar, Muller et al., 2018Muller L. Chavane F. Reynolds J. Sejnowski T.J. Cortical travelling waves: mechanisms and computational principles.Nat. Rev. Neurosci. 2018; 19: 255-268Crossref PubMed Scopus (194) Google Scholar). There were some reports of traveling-wave-like patterns in humans, but these patterns were generally observed during sleep or rest (Bahramisharif et al., 2013Bahramisharif A. van Gerven M.A. Aarnoutse E.J. Mercier M.R. Schwartz T.H. Foxe J.J. Ramsey N.F. Jensen O. Propagating neocortical gamma bursts are coordinated by traveling alpha waves.J. Neurosci. 2013; 33: 18849-18854Crossref PubMed Scopus (80) Google Scholar, Massimini et al., 2004Massimini M. Huber R. Ferrarelli F. Hill S. Tononi G. The sleep slow oscillation as a traveling wave.J. Neurosci. 2004; 24: 6862-6870Crossref PubMed Scopus (767) Google Scholar, Muller et al., 2016Muller L. Piantoni G. Koller D. Cash S.S. Halgren E. Sejnowski T.J. Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night.eLife. 2016; 5: e17267Crossref PubMed Google Scholar). Given the potential importance of spatially coordinated brain oscillations for distributed cortical processes, several studies tested for large-scale synchronized oscillations in the human cortex during cognition. However, this oscillatory synchrony was rare or present only on a small scale in humans (Bullock et al., 1995Bullock T.H. McClune M.C. Achimowicz J.Z. Iragui-Madoz V.J. Duckrow R.B. Spencer S.S. EEG coherence has structure in the millimeter domain: subdural and hippocampal recordings from epileptic patients.Electroencephalogr. Clin. Neurophysiol. 1995; 95: 161-177Abstract Full Text PDF PubMed Scopus (131) Google Scholar, Menon et al., 1996Menon V. Freeman W.J. Cutillo B.A. Desmond J.E. Ward M.F. Bressler S.L. Laxer K.D. Barbaro N. Gevins A.S. Spatio-temporal correlations in human gamma band electrocorticograms.Electroencephalogr. Clin. Neurophysiol. 1996; 98: 89-102Abstract Full Text PDF PubMed Scopus (164) Google Scholar, Raghavachari et al., 2006Raghavachari S. Lisman J.E. Tully M. Madsen J.R. Bromfield E.B. Kahana M.J. Theta oscillations in human cortex during a working-memory task: evidence for local generators.J. Neurophysiol. 2006; 95: 1630-1638Crossref PubMed Scopus (228) Google Scholar). These results shed doubt on the possibility that large-scale spatially coordinated oscillations, such as traveling waves, figured prominently in human cortical processing. We re-examined the potential role of cortical traveling waves in human cognition by analyzing electrocorticographic (ECoG) brain recordings from 77 neurosurgical patients. We analyzed the data with a new technique that identifies traveling waves at the single-trial level across various frequencies and electrode configurations. As we describe below, we found traveling waves in 84% (65 of 77) of subjects (for subject details, see Table S1). Traveling waves were present across a wide frequency range (2 to 15 Hz) that included the theta and alpha bands and were relevant behaviorally, as their propagation correlated with subject performance and events in a memory task. Our results indicate that human behavior is supported by traveling waves of theta- and alpha-band oscillations that propagate across the cortex. To identify traveling waves in the human cortex, we examined direct ECoG brain recordings from neurosurgical patients performing a working memory task (Sternberg, 1966Sternberg S. High-speed scanning in human memory.Science. 1966; 153: 652-654Crossref PubMed Scopus (2536) Google Scholar), which was previously shown to elicit large-amplitude oscillations related to memory at various frequencies (Raghavachari et al., 2001Raghavachari S. Kahana M.J. Rizzuto D.S. Caplan J.B. Kirschen M.P. Bourgeois B. Madsen J.R. Lisman J.E. Gating of human theta oscillations by a working memory task.J. Neurosci. 2001; 21: 3175-3183Crossref PubMed Google Scholar, Jacobs and Kahana, 2009Jacobs J. Kahana M.J. Neural representations of individual stimuli in humans revealed by gamma-band electrocorticographic activity.J. Neurosci. 2009; 29: 10203-10214Crossref PubMed Scopus (90) Google Scholar). Here, we analyzed these data using a new analytical framework that can identify traveling waves by characterizing the spatiotemporal structure of the oscillations in each patient individually. One form of a traveling wave that could appear in ECoG signals from one patient is a phase wave, which is a neuronal oscillation that is visible simultaneously on multiple electrodes at the same frequency with a systematic timing (or phase) gradient across space. Owing to the spatial phase gradient, the oscillation appears to propagate across the cortex (Ermentrout and Kleinfeld, 2001Ermentrout G.B. Kleinfeld D. Traveling electrical waves in cortex: insights from phase dynamics and speculation on a computational role.Neuron. 2001; 29: 33-44Abstract Full Text Full Text PDF PubMed Scopus (316) Google Scholar). A requirement for this type of traveling wave is that the signals across multiple neighboring electrodes exhibit oscillations at the same frequency. Thus, our first step in identifying human cortical traveling waves was to find clusters of cortex where contiguous electrodes showed oscillations at the same frequency. To identify these patterns, we examined the recording from each electrode individually, identified sites that showed narrowband oscillations, and then measured their frequency. We distinguished these oscillations by using a peak-picking algorithm, which found narrowband oscillatory peaks that were elevated over the background 1/f ECoG power spectrum (Manning et al., 2009Manning J.R. Jacobs J. Fried I. Kahana M.J. Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans.J. Neurosci. 2009; 29: 13613-13620Crossref PubMed Scopus (527) Google Scholar). Using this technique, we identified electrodes with narrowband oscillations at various frequencies. Most patients had spatially contiguous clusters of electrodes that showed narrowband oscillations at the same or a similar frequency. We identified these electrode groups using a clustering algorithm (see STAR Methods; Figures S1A–S1F). We refer to a contiguous group of four or more electrodes with oscillations at similar frequencies as an “oscillation cluster.” Across 77 patients, we found a total of 208 oscillation clusters. Oscillation clusters were present at frequencies from 2 to 15 Hz, involved 59% of all electrodes (2,401 of 4,077), and were present in 74 (96%) patients. The frequencies of oscillation clusters often differed across individuals even for electrodes in the same anatomical region (Figure S2). This suggested to us that oscillation clusters could reflect distinctive cortical networks that were individualized for a given patient. To assess whether there were true intersubject differences in the frequencies of oscillation clusters, we tested for a spatial correlation in the frequencies of narrowband oscillations across electrodes in each subject using Moran’s I statistic (Moran, 1950Moran P.A. Notes on continuous stochastic phenomena.Biometrika. 1950; 37: 17-23Crossref PubMed Scopus (4565) Google Scholar). Here, we computed I for each subject and compared the mean I with the values computed from a shuffling procedure that randomly interchanged electrodes between subjects. This analysis thus tested the hypothesis that the frequencies of oscillations were more correlated between nearby electrodes within a patient compared to electrodes at similar anatomical locations in other patients. The mean within-subject frequency correlation that we observed (I=0.03) was entirely outside the range of values computed from the shuffled data (p<10−3; Figure S1G). This result indicated that clusters of ECoG electrodes with narrowband oscillations at the same frequency reflected robust within-subject spatial frequency clustering. The widespread presence of oscillation clusters indicates that neuronal oscillations at a single frequency were present across large regions of the human cortex. If the timing of these oscillations were synchronized, it could provide evidence for large-scale oscillatory networks (Kopell et al., 2014Kopell N.J. Gritton H.J. Whittington M.A. Kramer M.A. Beyond the connectome: the dynome.Neuron. 2014; 83: 1319-1328Abstract Full Text Full Text PDF PubMed Scopus (208) Google Scholar). Thus, we next characterized the timing of activity across each oscillation cluster to identify patterns of phase synchrony, such as traveling waves (Prechtl et al., 1997Prechtl J.C. Cohen L.B. Pesaran B. Mitra P.P. Kleinfeld D. Visual stimuli induce waves of electrical activity in turtle cortex.Proc. Natl. Acad. Sci. USA. 1997; 94: 7621-7626Crossref PubMed Scopus (228) Google Scholar, Rubino et al., 2006Rubino D. Robbins K.A. Hatsopoulos N.G. Propagating waves mediate information transfer in the motor cortex.Nat. Neurosci. 2006; 9: 1549-1557Crossref PubMed Scopus (308) Google Scholar, Patel et al., 2012Patel J. Fujisawa S. Berényi A. Royer S. Buzsáki G. Traveling theta waves along the entire septotemporal axis of the hippocampus.Neuron. 2012; 75: 410-417Abstract Full Text Full Text PDF PubMed Scopus (156) Google Scholar, Patten et al., 2012Patten T.M. Rennie C.J. Robinson P.A. Gong P. Human cortical traveling waves: dynamical properties and correlations with responses.PLoS ONE. 2012; 7: e38392Crossref PubMed Scopus (43) Google Scholar, Bahramisharif et al., 2013Bahramisharif A. van Gerven M.A. Aarnoutse E.J. Mercier M.R. Schwartz T.H. Foxe J.J. Ramsey N.F. Jensen O. Propagating neocortical gamma bursts are coordinated by traveling alpha waves.J. Neurosci. 2013; 33: 18849-18854Crossref PubMed Scopus (80) Google Scholar, Zhang and Jacobs, 2015Zhang H. Jacobs J. Traveling theta waves in the human hippocampus.J. Neurosci. 2015; 35: 12477-12487Crossref PubMed Scopus (80) Google Scholar). Visual inspection of the signals across many oscillation clusters indicated that the timing of individual oscillation cycles varied systematically with the electrode location, which is indicative of a traveling wave. As an example, Figures 1A–1D show the activity on one trial across an 8.3-Hz oscillation cluster in an electrode grid from patient 1. While 8.3-Hz oscillations were visible on all channels in this grid, the relative timing of this signal varied systematically, such that the onset time of each oscillation cycle correlated with the electrode’s anterior-posterior position. We quantified this phenomenon by calculating the relative phase of the oscillation on each electrode and trial. On this trial, the electrodes in this cluster showed a continuous spatial phase shift across a range of ∼240° (Figure 1B). In this scheme, positive phase shifts correspond to oscillators that have been advancing for a longer period of time. Thus, because the phase was largest at posterior electrodes, it indicates that the electrode cluster showed an anterior-to-posterior traveling wave on this trial (see Video S1). eyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiJiMThmNGIwOWRlOWQwZjkyYzAzNTgyNzZkY2IxMzNjZSIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNjc4NzA5MjM0fQ.iElJwRFrtD-zijy8kqnyuUtWnQn73PySx3Aza765Y9qaydd5e6iWfu2Sg3An3xmZJu-ASPsh9Om2mIaevtZGnGDdWTwDh9q1fW3Nch6Uw73czu01Nwrqb0bUhhFZuACdcGEo-t82jvs86VdI7k5jPDl-5Tq6FfOt7IZ6PCeQu5Vg6XKPQSSzo47UUwDKS41qWs8qtzfLz2U01MFUeub1Ujo325sM1DaNAJN24Po-AKBKNXMxWJZmOXoRg9h55201q-R_lkfpUvpFL0p18OAtRI0OqN0HNpeNqxqZsMMRZoKDeqcreNAP5XylKdkC0N9NPrOmdBHJLlfKccAwa-gwJg Download .mp4 (1.38 MB) Help with .mp4 files Video S1. Animation of a Traveling Wave in One Trial from Patient 1, Related to Figure 1 We used circular statistics (Fisher, 1993Fisher N.I. Statistical Analysis of Circular Data. Cambridge University Press, 1993Crossref Google Scholar) to first identify human cortical traveling waves at the single-trial level and then to compare their properties at the group level. For each oscillation cluster, at each time point within a trial, we used a circular-linear model to characterize the relation between electrode position and oscillation phase (Figure 1C). This procedure models each cluster’s instantaneous phase distribution as a plane wave, finding the best-fitting spatial phase gradient. The fitted phase gradient provides a quantitative estimate of the speed and direction of traveling wave propagation (Figure S3). We compute the instantaneous robustness of the traveling wave on each electrode cluster by computing, for each trial, the proportion of phase variation that is explained by the circular-linear model, which we call the phase-gradient directionality (PGD). We assessed whether each oscillation cluster exhibited a reliable traveling wave using a permutation procedure. Here, we compared each electrode cluster’s median PGD value to the distribution of PGD values expected by chance (Figure 1E). This analysis demonstrated that traveling waves on the cluster in Figures 1A–1D were statistically reliable on a single-trial basis (mean PGD=0.35, p<0.001). Furthermore, by assessing the distribution of propagation directions across trials for this cluster, we determined that these traveling waves consistently moved in an anterior-to-posterior direction (r¯=0.94, Rayleigh p<0.001; Figure 1F). The traveling wave on this cluster is also visible by using a simpler approach based on temporal averaging (Figure 1G). Figures 1H–1J shows example electrode clusters in other patients that also showed robust traveling waves. We applied this methodology across our dataset and found that 140 (of 208; 67%) oscillation clusters had consistent traveling waves, defined as showing both reliable plane waves at the single-trial level and having a consistent propagation direction (see STAR Methods). 30 (14%) oscillation clusters showed reliable plane waves at the single-trial level but did not have a consistent propagation direction across trials; the remaining 38 (18%) clusters did not show reliable single-trial plane waves. Traveling waves involved 47% of all electrodes and were present in all lobes of the neocortex across both left and right hemispheres (Table S2). Thus, traveling waves are a broad phenomenon across the human brain. Having established that human cortical traveling waves were widespread, we next studied their properties in more detail at the population level. First, we compared the properties of traveling waves from oscillation clusters identified in different brain areas (Figures 2A and 2B ; Video S2). Traveling waves in the frontal and temporal lobes generally propagated in a posterior-to-anterior direction (p values <0.01, Rayleigh tests). In addition, frontal traveling waves had a tendency to propagate toward the midline. In the occipital and parietal lobes, the propagation direction of traveling waves varied and were not reliably clustered (p>0.05). eyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiJhYTBmNGQ2ZTRlM2Y2NjZhN2Y2YjY2NmZiMjVjYjI1ZSIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNjc4NzA5MjM0fQ.AaM3o_-TFEkVTl_AYdc-LJaParOv4oZCAFy8aJ0B7Vv0JrPr-rZY41Mec_2Zv5MuD6fSN56caVBCnR7Gb0yYGhDp4O1jtwe7aA0gKGU1THjRMqkRUuyirfrrKg1Qcq5Gp6igJ7Gtn5ARIEAZ3iKzITz27-ZPPcqiuBoRe7wSX3Jn21jXAfw357ecEVxqjYfOgmQUxmUSgiLShuyeYlBhO0q4b-QER47tTjT0uAiPLKBT5vWa_Ta9f293zK7-MggjCiUCEX8VqPgCUQkNtcITi8ZpcYWVNPlws1z9yolWN3_SpOb0DgalTdVySlz2f0SM8pn_5TYkP5VPoTZNnYLgqA Download .mp4 (16.23 MB) Help with .mp4 files Video S2. Rotating Animation Showing the Topography of Traveling Wave Propagation, Related to Figure 2Format follows Figure 2A. We also compared the temporal frequencies of the oscillation clusters that showed significant traveling waves (Figure 2C). Traveling waves were present at frequencies from 2 to 15 Hz. Traveling waves in the frontal lobe had a slower mean temporal frequency in the theta range (6 Hz). In contrast, traveling waves in occipital and temporal regions had faster alpha-band frequencies (mean 9 Hz; ANOVA, F(2,137)=9.7, p<0.01). It is notable that the frequencies of the traveling waves in these areas were similar to the frequencies of the oscillations that had been reported in these regions earlier (Klimesch, 1999Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis.Brain Res. Brain Res. Rev. 1999; 29: 169-195Crossref PubMed Scopus (4479) Google Scholar, Canolty et al., 2006Canolty R.T. Edwards E. Dalal S.S. Soltani M. Nagarajan S.S. Kirsch H.E. Berger M.S. Barbaro N.M. Knight R.T. High gamma power is phase-locked to theta oscillations in human neocortex.Science. 2006; 313: 1626-1628Crossref PubMed Scopus (1760) Google Scholar, Voytek et al., 2010Voytek B. Canolty R.T. Shestyuk A. Crone N.E. Parvizi J. Knight R.T. Shifts in gamma phase-amplitude coupling frequency from theta to alpha over posterior cortex during visual tasks.Front. Hum. Neurosci. 2010; 4: 191Crossref PubMed Scopus (289) Google Scholar, Groppe et al., 2013Groppe D.M. Bickel S. Keller C.J. Jain S.K. Hwang S.T. Harden C. Mehta A.D. Dominant frequencies of resting human brain activity as measured by the electrocorticogram.Neuroimage. 2013; 79: 223-233Crossref PubMed Scopus (103) Google Scholar) because it suggests that many previously reported neural oscillations could in fact be traveling waves. We computed additional properties of traveling waves at the group level. Although most subjects had only one or two electrode clusters with traveling waves, a small number of subjects showed up to five such clusters (Figure 3A). In most cases (99% of electrodes), the multiple clusters in a subject did not overlap. Traveling waves with frequencies near ∼8 Hz had the highest power (Figure 3B). The electrode clusters with traveling waves ranged in size substantially (Figure 3C), having a median radius of 2.5 cm (∼20 cm2) up to a maximum of ∼6 cm (∼113 cm2). Traveling waves had a median propagation speed of 0.55 m/s and a median wavelength of 11.7 cm, but these values varied substantially across the population (Figures 3D and 3E). Finally, we measured the prevalence of traveling waves at the single-trial level. Across the significant oscillation clusters, traveling waves were present on 61% of single trials (median), although some clusters showed traveling waves on 80%–100% of trials (Figure 3F). We hypothesized that the spatial propagation of traveling waves reflected the movement of neural activity across the cortex in a manner that was important for behavior. Although some previous studies had measured human cortical traveling waves during tasks, they did not show clear correlations to behavior (Massimini et al., 2004Massimini M. Huber R. Ferrarelli F. Hill S. Tononi G. The sleep slow oscillation as a traveling wave.J. Neurosci. 2004; 24: 6862-6870Crossref PubMed Scopus (767) Google Scholar, Takahashi et al., 2011Takahashi K. Saleh M. Penn R.D. Hatsopoulos N.G. Propagating waves in human motor cortex.Front. Hum. Neurosci. 2011; 5: 40Crossref PubMed Scopus (66) Google Scholar, Bahramisharif et al., 2013Bahramisharif A. van Gerven M.A. Aarnoutse E.J. Mercier M.R. Schwartz T.H. Foxe J.J. Ramsey N.F. Jensen O. Propagating neocortical gamma bursts are coordinated by traveling alpha waves.J. Neurosci. 2013; 33: 18849-18854Crossref PubMed Scopus (80) Google Scholar). We tested for a potential functional role for traveling waves by comparing their properties through the course of memory processing. In each trial of the memory task (Sternberg, 1966Sternberg S. High-speed scanning in human memory.Science. 1966; 153: 652-654Crossref PubMed Scopus (2536) Google Scholar), patients learned a list of stimuli and then viewed a retrieval cue. By comparing traveling wave properties during the task, we sought to identify functional properties of traveling waves and to test whether they differ across brain regions. We computed each cluster’s directional consistency (DC), which measures the degree to which traveling waves on each cluster showed a consistent propagation direction at a particular time point relative to task events. DC, which is computed across trials, varies between 0 and 1, with 1 indicating that traveling waves always propagated in a single direction and 0 indicating that propagation directions were uniformly distributed. Figure 4A illustrates the time course of mean DC during the cue response interval for the traveling waves in the frontal lobe of patient 26. This plot indicates that the traveling waves on this cluster were not directionally organized at the moment of cue onset, but 500 ms later, they reliably propagated anteriorly (DC = 0.34). A different pattern was present for the traveling waves on a posterior electrode cluster in patient 13 (Figures 4C and 4D), whereby the directional organization was consistent at cue onset and subsequently decreased. We confirmed that these patterns were reliable by measuring the time course of mean traveling wave DC at the group level. Following cue onset, traveling waves in the temporal and frontal lobes showed increases in DC above baseline levels (Figure 4E). Inversely, traveling waves from occipitoparietal clusters showed decreased DC during this same period, which was significantly different from the DC increase in the frontal and temporal lobes (Figure 4F; ANOVA, F(2,137)=5.4, p<0.01). Because frontal and temporal regions specifically show increased DC following cue onset, it indicates that traveling waves in these areas move more consistently during memory retrieval. After a person views a stimulus, the brain exhibits stimulus-locked neural patterns, including phase resets of ongoing brain oscillations and evoked activity (Rizzuto et al., 2003Rizzuto D.S. Madsen J.R. Bromfield E.B. Schulze-Bonhage A. Seelig D. Aschenbrenner-Scheibe R. Kahana M.J. Reset of human neocortical oscillations during a working memory task.Proc. Natl. Acad. Sci. USA. 2003; 100: 7931-7936Crossref PubMed Scopus (204) Google Scholar). Because these can" @default.
• W2952410712 created "2019-06-27" @default.
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• W2952410712 date "2018-06-01" @default.
• W2952410712 modified "2023-10-17" @default.
• W2952410712 title "Theta and Alpha Oscillations Are Traveling Waves in the Human Neocortex" @default.
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• W2952410712 doi "https://doi.org/10.1016/j.neuron.2018.05.019" @default.
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