The human brain spontaneously generates neural oscillations with a large variability in frequency, amplitude, duration, and recurrence. Little, however, is known about the long-term spatiotemporal structure of the complex patterns of ongoing activity. A central unresolved issue is whether fluctuations in oscillatory activity reflect a memory of the dynamics of the system for more than a few seconds.We investigated the temporal correlations of network oscillations in the normal human brain at time scales ranging from a few seconds to several minutes. Ongoing activity during eyes-open and eyes-closed conditions was recorded with simultaneous magnetoencephalography and electroencephalography. Here we show that amplitude fluctuations of 10 and 20 Hz oscillations are correlated over thousands of oscillation cycles. Our analyses also indicated that these amplitude fluctuations obey power-law scaling behavior. The scaling exponents were highly invariant across subjects. We propose that the large variability, the long-range correlations, and the power-law scaling behavior of spontaneous oscillations find a unifying explanation within the theory of self-organized criticality, which offers a general mechanism for the emergence of correlations and complex dynamics in stochastic multiunit systems. The demonstrated scaling laws pose novel quantitative constraints on computational models of network oscillations. We argue that critical-state dynamics of spontaneous oscillations may lend neural networks capable of quick reorganization during processing demands. Key words: spontaneous oscillations; large-scale dynamics; temporal properties; correlations; scaling behavior; selforganized criticality; complexityOscillations at various frequencies are a prominent feature of the spontaneous electroencephalogram (EEG) (Berger, 1929;Connors and Amitai, 1997) and are believed to reflect functional states of the brain (Llinás, 1988;Steriade et al., 1993;Arieli et al., 1996;Herculano-Houzel et al., 1999;Tsodyks et al., 1999). These oscillations arise from correlated activity of a large number of neurons whose interactions are generally nonlinear (Steriade et al., 1990(Steriade et al., , 1993Lopez da Silva, 1991). The intrinsic neural properties and intricate patterns of connectivity add further complexity to the behavior of neural systems (Llinás, 1988;Connors and Amitai, 1997;Destexhe et al., 1998). The mechanisms and dynamics of network oscillations have been widely studied with electrophysiological recordings (Destexhe et al., 1998(Destexhe et al., , 1999, as well as with computational models (Destexhe et al., 1998;Stam et al., 1999). Neural oscillations in vivo exhibit large variability in both amplitude and frequency. The dynamic nature of these fluctuations, however, has remained unclear. Particularly for the human electroencephalogram, 8 -13 Hz oscillations have attracted widespread interest in this context. However, the complexity of the EEG has rendered it impossible to reliably distinguish the waxing and waning of oscillations o...

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Synchronization of neuronal activity, often associated with network oscillations, is thought to provide a means for integrating anatomically distributed processing in the brain. Neuronal processing, however, involves simultaneous oscillations in various frequency bands. The mechanisms involved in the integration of such spectrally distributed processing have remained enigmatic. We demonstrate, using magnetoencephalography, that robust cross-frequency phase synchrony is present in the human cortex among oscillations with frequencies from 3 to 80 Hz. Continuous mental arithmetic tasks demanding the retention and summation of items in the working memory enhanced the cross-frequency phase synchrony among ␣ (ϳ10 Hz), ␤ (ϳ20 Hz), and ␥ (ϳ30-40 Hz) oscillations. These tasks also enhanced the "classical" within-frequency synchrony in these frequency bands, but the spatial patterns of ␣, ␤, and ␥ synchronies were distinct and, furthermore, separate from the patterns of cross-frequency phase synchrony. Interestingly, an increase in task load resulted in an enhancement of phase synchrony that was most prominent between ␥-and ␣-band oscillations. These data indicate that crossfrequency phase synchrony is a salient characteristic of ongoing activity in the human cortex and that it is modulated by cognitive task demands. The enhancement of cross-frequency phase synchrony among functionally and spatially distinct networks during mental arithmetic tasks posits it as a candidate mechanism for the integration of spectrally distributed processing.

Scale-free fluctuations are ubiquitous in behavioral performance and neuronal activity. In time scales from seconds to hundreds of seconds, psychophysical dynamics and the amplitude fluctuations of neuronal oscillations are governed by power-law-form longrange temporal correlations (LRTCs). In millisecond time scales, neuronal activity comprises cascade-like neuronal avalanches that exhibit power-law size and lifetime distributions. However, it remains unknown whether these neuronal scaling laws are correlated with those characterizing behavioral performance or whether neuronal LRTCs and avalanches are related. Here, we show that the neuronal scaling laws are strongly correlated both with each other and with behavioral scaling laws. We used source reconstructed magneto-and electroencephalographic recordings to characterize the dynamics of ongoing cortical activity. We found robust power-law scaling in neuronal LRTCs and avalanches in resting-state data and during the performance of audiovisual threshold stimulus detection tasks. The LRTC scaling exponents of the behavioral performance fluctuations were correlated with those of concurrent neuronal avalanches and LRTCs in anatomically identified brain systems. The behavioral exponents also were correlated with neuronal scaling laws derived from a resting-state condition and with a similar anatomical topography. Finally, despite the difference in time scales, the scaling exponents of neuronal LRTCs and avalanches were strongly correlated during both rest and task performance. Thus, long and short time-scale neuronal dynamics are related and functionally significant at the behavioral level. These data suggest that the temporal structures of human cognitive fluctuations and behavioral variability stem from the scaling laws of individual and intrinsic brain dynamics.spontaneous activity | threshold detection | criticality H uman cognitive and behavioral performance is highly variable and exhibits slow fluctuations that are salient in continuous performance tasks (CPTs) (1). Psychophysical time series have been known since the early 1950s to be nonrandomly clustered (2), and later studies have shown that hit-rate and/or reaction-time fluctuations in CPT data are fractal and power-law autocorrelated across hundreds of seconds (3-9). The biological origins and relevance of these dynamic, however, remain unclear (10, 11).Similar to those in behavioral performance, the fluctuations of collective neuronal activity at many levels of the nervous system are scale-free and governed by power-law scaling laws. On short time scales (10 −3 −10 −1 s), negative deflections in local field potentials form spatiotemporal cascades of activity, "neuronal avalanches" (32-34), the size and lifetime distributions of which are power laws akin to those of a critical branching process (33). Neuronal avalanches characterize spontaneous neuronal network activity in organotypic cultures (32), brain slices in vitro (35), and monkey (34) and human cortex (36) in vivo. In monkey cortex, the avalanche...

Visual working memory (VWM) is used to maintain sensory information for cognitive operations, and its deficits are associated with several neuropsychological disorders. VWM is based on sustained neuronal activity in a complex cortical network of frontal, parietal, occipital, and temporal areas. The neuronal mechanisms that coordinate this distributed processing to sustain coherent mental images and the mechanisms that set the behavioral capacity limit have remained unknown. We mapped the anatomical and dynamic structures of network synchrony supporting VWM by using a neuro informatics approach and combined magnetoencephalography and electroencephalography. Interareal phase synchrony was sustained and stable during the VWM retention period among frontoparietal and visual areas in α-(10-13 Hz), β-(18-24 Hz), and γ-(30-40 Hz) frequency bands. Furthermore, synchrony was strengthened with increasing memory load among the frontoparietal regions known to underlie executive and attentional functions during memory maintenance. On the other hand, the subjects' individual behavioral VWM capacity was predicted by synchrony in a network in which the intraparietal sulcus was the most central hub. These data suggest that interareal phase synchrony in the α-, β-, and γ-frequency bands among frontoparietal and visual regions could be a systems level mechanism for coordinating and regulating the maintenance of neuronal object representations in VWM.cortical synchrony | graph theory | magnetoencephalography | source modelling | functional connectivity F unctional MRI (fMRI) studies have shown that human visual working memory (VWM) is supported by neuronal activity in several cortical regions in the frontal, parietal, occipital, and temporal lobes (1-6), where the frontoparietal regions mediate attentional and central executive functions (2-4, 7, 8) and the visual areas underlie the formation of neuronal object representations (9-11) and sustain them in VWM (8). However, fMRI does not have the subsecond temporal precision required for revealing the neuronal mechanisms that integrate and coordinate the processing in the functionally distinct regions during VWM maintenance. These functions could be carried out by oscillatory synchrony (i.e., rhythmical millisecond-range temporal correlations of neuronal activity), which modulates neuronal interactions and regulates network communication (12)(13)(14)(15)(16). The functional role of oscillatory synchrony can be studied noninvasively by combining magnetoencephalography and electroencephalography (MEEG) recordings with source reconstruction techniques that reveal the anatomical structures producing the MEEG signals. Earlier studies have considered interactions among approximately three to nine cortical regions of interest and revealed attentional modulations of interareal synchrony (17-19). The interactions underlying VWM have remained uncharacterized. We hypothesized that neuronal synchronization is instrumental for the maintenance of object representations in VWM. To have this role, s...

Our ability to perceive weak signals is correlated among consecutive trials and fluctuates slowly over time. Although this "streaking effect" has been known for decades, the underlying neural network phenomena have remained largely unidentified. We examined the dynamics of human behavioral performance and its correlation with infraslow (0.01-0.1 Hz) fluctuations in ongoing brain activity. Full-band electroencephalography revealed prominent infraslow fluctuations during the execution of a somatosensory detection task. Similar fluctuations were predominant also in the dynamics of behavioral performance. The subjects' ability to detect the sensory stimuli was strongly correlated with the phase, but not with the amplitude of the infraslow EEG fluctuations. These data thus reveal a direct electrophysiological correlate for the slow fluctuations in human psychophysical performance. We then examined the correlation between the phase of infraslow EEG fluctuations and the amplitude of 1-40 Hz neuronal oscillations in six frequency bands. Like the behavioral performance, the amplitudes in these frequency bands were robustly correlated with the phase of the infraslow fluctuations. These data hence suggest that the infraslow fluctuations reflect the excitability dynamics of cortical networks. We conclude that ongoing 0.01-0.1 Hz EEG fluctuations are prominent and functionally significant during execution of cognitive tasks.

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The presence of various ongoing oscillations in the brain is correlated with behavioral states such as restful wakefulness or drowsiness. However, even when subjects aim to maintain a high level of vigilance, ongoing oscillations exhibit large amplitude variability on time scales of hundreds of milliseconds to seconds, suggesting that the functional state of local cortical networks is continuously changing. How this volatility of ongoing oscillations influences the perception of sensory stimuli has remained essentially unknown.We investigated the relationship between prestimulus neuronal oscillations and the subjects' ability to consciously perceive and react to somatosensory stimuli near the threshold of detection. We show that, for prestimulus oscillations at ϳ10, 20, and 40 Hz detected over the sensorimotor cortex, intermediate amplitudes were associated with the highest probability of conscious detection and the shortest reaction times. In contrast, for 10 and 20 Hz prestimulus oscillations detected over the parietal region, the largest amplitudes were associated with the best performance.Our data indicate that the prestimulus oscillatory activity detected over sensorimotor and parietal cortices has a profound effect on the processing of weak stimuli. Furthermore, the results suggest that ongoing oscillations in sensory cortices may optimize the processing of sensory stimuli with the same mechanism as noise sources in intrinsic stochastic resonance.