Satu Palva

New vistas for α-frequency band oscillations

The amplitude of α-frequency band (8–14Hz) activity in the human electroencephalogram is suppressed by eye opening, visual stimuli and visual scanning, whereas it is enhanced during internal tasks, such as mental calculation and working memory. α-Frequency band oscillations have hence been thought to reflect idling or inhibition of task-irrelevant cortical areas. However, recent data on α-amplitude and, in particular, α-phase dynamics posit a direct and active role for α-frequency band rhythmicity in the mechanisms of attention and consciousness. We propose that simultaneous α-, β- (14–30Hz) and γ- (30–70Hz) frequency band oscillations are required for unified cognitive operations, and hypothesize that cross-frequency phase synchrony between α, β and γ oscillations coordinates the selection and maintenance of neuronal object representations during working memory, perception and consciousness.

Phase synchrony among neuronal oscillations in the human cortex

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 α (∼10 Hz), β (∼20 Hz), and γ (∼30-40 Hz) oscillations. These tasks also enhanced the “classical” within-frequency synchrony in these frequency bands, but the spatial patterns of α, β, and γ 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 γ- and α-band oscillations. These data indicate that cross-frequency 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.

Neuronal synchrony reveals working memory networks and predicts individual memory capacity

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.

Early Neural Correlates of Conscious Somatosensory Perception

The cortical processing of consciously perceived and unperceived somatosensory stimuli is thought to be identical during the first 100-120 ms after stimulus onset. Thereafter, the electrophysiological correlates of conscious perception have been shown to be reflected in the N1 component of the evoked response as well as in later (>200 ms) nonstimulus-locked γ-band (28-50 Hz) oscillatory activity. To evaluate more specifically the time course and correlation of neuronal oscillations with conscious perception, we recorded neuromagnetic responses to threshold-intensity somatosensory stimuli. We show here that cortical broadband activities phase locked to the subsequently perceived stimuli in somatosensory, frontal, and parietal regions as early as 30-70 ms from stimulus onset, whereas the phase locking to the unperceived stimuli was weak and primarily restricted to somatosensory regions. Such stimulus locking also preceded the perceived stimuli, indicating that the phase of ongoing cortical activities biases subsequent perception. Furthermore, the data show that the stimulus locking was present in the θ- (4-8 Hz), α- (8-14 Hz), β- (14-28 Hz), and γ- (28-40 Hz) frequency bands, of which the widespread α-band component was dominant for the consciously perceived stimuli but virtually unobservable for the unperceived stimuli. Our results show that the neural correlates of conscious perception are already found during the earliest stages of cortical processing from 30 to 150 ms after stimulus onset and suggest that α-frequency-band oscillations have a role in the neural mechanisms of sensory awareness.

Functional Roles of Alpha-Band Phase Synchronization in Local and Large-Scale Cortical Networks

Alpha-frequency band (8–14 Hz) oscillations are among the most salient phenomena in human electroencephalography (EEG) recordings and yet their functional roles have remained unclear. Much of research on alpha oscillations in human EEG has focused on peri-stimulus amplitude dynamics, which phenomenologically support an idea of alpha oscillations being negatively correlated with local cortical excitability and having a role in the suppression of task-irrelevant neuronal processing. This kind of an inhibitory role for alpha oscillations is also supported by several functional magnetic resonance imaging and trans-cranial magnetic stimulation studies. Nevertheless, investigations of local and inter-areal alpha phase dynamics suggest that the alpha-frequency band rhythmicity may play a role also in active task-relevant neuronal processing. These data imply that inter-areal alpha phase synchronization could support attentional, executive, and contextual functions. In this review, we outline evidence supporting different views on the roles of alpha oscillations in cortical networks and unresolved issues that should be addressed to resolve or reconcile these apparently contrasting hypotheses.

Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs

The systems-level neuronal mechanisms that coordinate temporally, anatomically and functionally distributed neuronal activity into coherent cognitive operations in the human brain have remained poorly understood. Synchronization of neuronal oscillations may regulate network communication and could thus serve as such a mechanism. Evidence for this hypothesis, however, was until recently sparse, as methodological challenges limit the investigation of interareal interactions with non-invasive magneto- and electroencephalography (M/EEG) recordings. Nevertheless, recent advances in M/EEG source reconstruction and clustering methods support complete phase-interaction mappings that are essential for uncovering the large-scale neuronal assemblies and their functional roles. These data show that synchronization is a robust and behaviorally significant phenomenon in task-relevant cortical networks and could hence bind distributed neuronal processing to coherent cognitive states.

Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws

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 long-range 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.

Discrimination of Speech and of Complex Nonspeech Sounds of Different Temporal Structure in the Left and Right Cerebral Hemispheres

The key question in understanding the nature of speech perception is whether the human brain has unique speech-specific mechanisms or treats all sounds equally. We assessed possible differences between the processing of speech and complex nonspeech sounds in the two cerebral hemispheres by measuring the magnetic equivalent of the mismatch negativity, the brains automatic change-detection response, which was elicited by speech sounds and by similarly complex nonspeech sounds with either fast or slow acoustic transitions. Our results suggest that the right hemisphere is predominant in the perception of slow acoustic transitions, whereas neither hemisphere clearly dominates the discrimination of nonspeech sounds with fast acoustic transitions. In contrast, the perception of speech stimuli with similarly rapid acoustic transitions was dominated by the left hemisphere, which may be explained by the presence of acoustic templates (long-term memory traces) for speech sounds formed in this hemisphere.

Graph properties of synchronized cortical networks during visual working memory maintenance

Oscillatory synchronization facilitates communication in neuronal networks and is intimately associated with human cognition. Neuronal activity in the human brain can be non-invasively imaged with magneto- (MEG) and electroencephalography (EEG), but the large-scale structure of synchronized cortical networks supporting cognitive processing has remained uncharacterized. We combined simultaneous MEG and EEG (MEEG) recordings with minimum-norm-estimate-based inverse modeling to investigate the structure of oscillatory phase synchronized networks that were active during visual working memory (VWM) maintenance. Inter-areal phase-synchrony was quantified as a function of time and frequency by single-trial phase-difference estimates of cortical patches covering the entire cortical surfaces. The resulting networks were characterized with a number of network metrics that were then compared between delta/theta- (3-6 Hz), alpha- (7-13 Hz), beta- (16-25 Hz), and gamma- (30-80 Hz) frequency bands. We found several salient differences between frequency bands. Alpha- and beta-band networks were more clustered and small-world like but had smaller global efficiency than the networks in the delta/theta and gamma bands. Alpha- and beta-band networks also had truncated-power-law degree distributions and high k-core numbers. The data converge on showing that during the VWM-retention period, human cortical alpha- and beta-band networks have a memory-load dependent, scale-free small-world structure with densely connected core-like structures. These data further show that synchronized dynamic networks underlying a specific cognitive state can exhibit distinct frequency-dependent network structures that could support distinct functional roles.

Infra-Slow EEG Fluctuations Are Correlated with Resting-State Network Dynamics in fMRI

Ongoing neuronal activity in the CNS waxes and wanes continuously across widespread spatial and temporal scales. In the human brain, these spontaneous fluctuations are salient in blood oxygenation level-dependent (BOLD) signals and correlated within specific brain systems or “intrinsic-connectivity networks.” In electrophysiological recordings, both the amplitude dynamics of fast (1–100 Hz) oscillations and the scalp potentials per se exhibit fluctuations in the same infra-slow (0.01–0.1 Hz) frequency range where the BOLD fluctuations are conspicuous. While several lines of evidence show that the BOLD fluctuations are correlated with fast-amplitude dynamics, it has remained unclear whether the infra-slow scalp potential fluctuations in full-band electroencephalography (fbEEG) are related to the resting-state BOLD signals. We used concurrent fbEEG and functional magnetic resonance imaging (fMRI) recordings to address the relationship of infra-slow fluctuations (ISFs) in scalp potentials and BOLD signals. We show here that independent components of fbEEG recordings are selectively correlated with subsets of cortical BOLD signals in specific task-positive and task-negative, fMRI-defined resting-state networks. This brain system-specific association indicates that infra-slow scalp potentials are directly associated with the endogenous fluctuations in neuronal activity levels. fbEEG thus yields a noninvasive, high-temporal resolution window into the dynamics of intrinsic connectivity networks. These results support the view that the slow potentials reflect changes in cortical excitability and shed light on neuronal substrates underlying both electrophysiological and behavioral ISFs.