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

Neuronal correlates of full and partial visual conscious perception

Stimuli may induce only partial consciousness—an intermediate between null and full consciousness—where the presence but not identity of an object can be reported. The differences in the neuronal basis of full and partial consciousness are poorly understood. We investigated if evoked and oscillatory activity could dissociate full from partial conscious perception. We recorded human cortical activity with magnetoencephalography (MEG) during a visual perception task in which stimulus could be either partially or fully perceived. Partial consciousness was associated with an early increase in evoked activity and theta/low-alpha-band oscillations while full consciousness was also associated with late evoked activity and beta-band oscillations. Full from partial consciousness was dissociated by stronger evoked activity and late increase in theta oscillations that were localized to higher-order visual regions and posterior parietal and prefrontal cortices. Our results reveal both evoked activity and theta oscillations dissociate partial and full consciousness.Peer reviewe

Spectral and Anatomical Patterns of Large-Scale Synchronization Predict Human Attentional Capacity

The capacity of visual attention determines how many visual objects may be perceived at any moment. This capacity can be investigated with multiple object tracking (MOT) tasks, which have shown that it varies greatly between individuals. The neuronal mechanisms underlying capacity limits have remained poorly understood. Phase synchronization of cortical oscillations coordinates neuronal communication within the fronto-parietal attention network and between the visual regions during endogenous visual attention. We tested a hypothesis that attentional capacity is predicted by the strength of pretarget synchronization within attention-related cortical regions. We recorded cortical activity with magneto- and electroencephalography (M/EEG) while measuring attentional capacity with MOT tasks and identified large-scale synchronized networks from source-reconstructed M/EEG data. Individual attentional capacity was correlated with load-dependent strengthening of theta (3-8 Hz), alpha (8-10 Hz), and gamma-band (30-120 Hz) synchronization that connected the visual cortex with posterior parietal and prefrontal cortices. Individual memory capacity was also preceded by crossfrequency phase-phase and phase-amplitude coupling of alpha oscillation phase with beta and gamma oscillations. Our results show that good attentional capacity is preceded by efficient dynamic functional coupling and decoupling within brain regions and across frequencies, which may enable efficient communication and routing of information between sensory and attentional systems.Peer reviewe

Roles of Brain Criticality and Multiscale Oscillations in Temporal Predictions for Sensorimotor Processing

Sensorimotor predictions are essential for adaptive behavior. In natural environments, events that demand sensorimotor predictions unfold across many timescales, and corresponding temporal predictions (either explicit or implicit) should therefore emerge in brain dynamics. Neuronal oscillations are scale-specific processes found in several frequency bands. They underlie periodicity in sensorimotor processing and can represent temporal predictions via their phase dynamics. These processes build upon endogenous neural rhythmicity and adapt in response to exogenous timing demands. While much of the research on periodicity in neural processing has focused on subsecond oscillations, these fast-scale rhythms are in fact paralleled by critical-like, scale-free dynamics and fluctuations of brain activity at various timescales, ranging from seconds to hundreds of seconds. In this review, we put forth a framework positing that critical brain dynamics are essential for the role of neuronal oscillations in timing and that cross-frequency coupling flexibly organizes neuronal processing across multiple frequencies.Peer reviewe

Neuronal oscillations in gamma- and alpha-frequency bands : from object representations to sensory awareness

The synchronization of neuronal activity, especially in the beta- (14-30 Hz) /gamma- (30 80 Hz) frequency bands, is thought to provide a means for the integration of anatomically distributed processing and for the formation of transient neuronal assemblies. Thus non-stimulus locked (i.e. induced) gamma-band oscillations are believed to underlie feature binding and the formation of neuronal object representations. On the other hand, the functional roles of neuronal oscillations in slower theta- (4 8 Hz) and alpha- (8 14 Hz) frequency bands remain controversial. In addition, early stimulus-locked activity has been largely ignored, as it is believed to reflect merely the physical properties of sensory stimuli. With human neuromagnetic recordings, both the functional roles of gamma- and alpha-band oscillations and the significance of early stimulus-locked activity in neuronal processing were examined in this thesis. Study I of this thesis shows that even the stimulus-locked (evoked) gamma oscillations were sensitive to high-level stimulus features for speech and non-speech sounds, suggesting that they may underlie the formation of early neuronal object representations for stimuli with a behavioural relevance. Study II shows that neuronal processing for consciously perceived and unperceived stimuli differed as early as 30 ms after stimulus onset. This study also showed that the alpha band oscillations selectively correlated with conscious perception. Study III, in turn, shows that prestimulus alpha-band oscillations influence the subsequent detection and processing of sensory stimuli. Further, in Study IV, we asked whether phase synchronization between distinct frequency bands is present in cortical circuits. This study revealed prominent task-sensitive phase synchrony between alpha and beta/gamma oscillations. Finally, the implications of Studies II, III, and IV to the broader scientific context are analysed in the last study of this thesis (V). I suggest, in this thesis that neuronal processing may be extremely fast and that the evoked response is important for cognitive processes. I also propose that alpha oscillations define the global neuronal workspace of perception, action, and consciousness and, further, that cross-frequency synchronization is required for the integration of neuronal object representations into global neuronal workspace.Tässä väitöskirjassa tutkittiin aivojen rytmistä toimintaa ja sen merkitystä ärsykkeistä muodostettaviin hermostollisiin esityksiin ja tietoiseen havaintoon. Aivo-oskillaatioita tutkittiin magnetoenkefalografialla (MEG) rekisteröimällä pään pinnalta erittäin pieniä aivojen sähköisen toiminnan aiheuttamia magneettikentän muutoksia. Väitöskirjan ensimmäisessä osatyössä koehenkilöille esitettiin kielellisiä ja ei-kielellisiä ärsykkeitä. Havaittiin, että ärsykettä seuraavat nopeat gammaoskillaatiot erosivat kielellisille ja ei kielellisille ärsykkeille. Pääteltiin, että gammaoskillaatiot voivat heijastaa ärsykkeistä muodostettavaa hermostollista esitystä ja että kielellisten ja ei kielellisten ärsykkeiden käsittely eroaa jo hyvin varhaisessa vaiheessa noin 40 ms ärsykkeen alun jälkeen. Väitöskirjan toisessa osatyössä tutkittiin tietoisen havainnon muodostumista hyvin heikoille tuntoärsykkeille, jotka toisinaan havaittiin, ja jotka toisinaan jäivät havaitsematta. Havaittuja tuntoärsykkeitä seurasi alfaoskillaation vaihelukittuminen ärsykkeeseen, mutta jos ärsykettä ei havaittu, alfaoskillaatioiden vaihelukittumista ärsykkeeseen ei tapahtunut. Alfaoskillaatioiden vaihelukittuminen korreloi siis tietoisen havainnon kanssa. Väitöskirjan kolmannessa osatyössä tutkittiin ärsykettä edeltävän rytmisen aivotoiminnan vaikutusta tietoisen havainnon syntyyn hyvin heikoille tuntoärsykkeille. Havaittiin, että heikko tuntoärsyke havaitaan parhaiten, jos ärsykettä edeltävät aivo-oskillaatiot ovat suuruudeltaan keskisuuria, mutta että jos ärsykettä edeltävät aivo-oskillaatiot ovat voimakkuudeltaan joko suuria tai pieniä, heikko tuntoärsyke havaitaan harvemmin. Väitöskirjan neljännessä osatyössä tutkittiin eri taajuisten aivo-oskillaatioiden välistä synkroniaa levon ja työmuistia vaativan päässälaskutehtävän aikana. Havaittiin, että päässälaskutehtävän aikana alfa ja gamma taajuiset oskillaatiot synkronoituivat enemmän kuin levon aikana. Tämän ehdotettiin mahdollistavan alfa ja gamma oskillaatioiden välisen vuorovaikutuksen eli alfa ja gamma oskillaatioiden toiminnallisten roolien integroinnin. Väitöskirjan viimeisessä osatyössä muodostettiin synteesi erityisesti alfa oskillaatioiden toiminnallisesta roolista tietoisen havainnon aikana. Ehdotettiin, että gamma oskillaatiot heijastavat ärsykkeistä muodostettavia hermostollisia esityksiä, mutta että alfa oskillaatiot heijastavat itse tietoista havaintoa. Ehdotettiin, että alfa ja gamma oskillaatioiden toiminnalliset roolit voidaan integroida taajuuksien välisen synkronian avulla, mikä mahdollistaisi mm. tietoisen havainnon synnyn sekä objekti representaatioiden ylläpidon työmuistin aikana

Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance

Neuronal activity in sensory and fronto-parietal (FP) areas underlies the representation and attentional control, respectively, of sensory information maintained in visual working memory (VWM). Within these regions, beta/gamma phase-synchronization supports the integration of sensory functions, while synchronization in theta/alpha bands supports the regulation of attentional functions. A key challenge is to understand which mechanisms integrate neuronal processing across these distinct frequencies and thereby the sensory and attentional functions. We investigated whether such integration could be achieved by cross-frequency phase synchrony (CFS). Using concurrent magneto- and electroencephalography, we found that CFS was load-dependently enhanced between theta and alpha–gamma and between alpha and beta-gamma oscillations during VWM maintenance among visual, FP, and dorsal attention (DA) systems. CFS also connected the hubs of within-frequency-synchronized networks and its strength predicted individual VWM capacity. We propose that CFS integrates processing among synchronized neuronal networks from theta to gamma frequencies to link sensory and attentional functions

Stimulus detection rate and latency, firing rates and 1–40Hz oscillatory power are modulated by infra-slow fluctuations in a bistable attractor network model

Recordings of membrane and field potentials, firing rates, and oscillation amplitude dynamics show that neuronal activity levels in cortical and subcortical structures exhibit infra-slow fluctuations (ISFs) on time scales from seconds to hundreds of seconds. Similar ISFs are salient also in blood-oxygenation-level dependent (BOLD) signals as well as in psychophysical time series. Functional consequences of ISFs are not fully understood. Here, they were investigated along with dynamical implications of ISFs in large-scale simulations of cortical network activity. For this purpose, a biophysically detailed hierarchical attractor network model displaying bistability and operating in an oscillatory regime was used. ISFs were imposed as slow fluctuations in either the amplitude or frequency of fast synaptic noise. We found that both mechanisms produced an ISF component in the synthetic local field potentials (LFPs) and modulated the power of 1–40 Hz oscillations. Crucially, in a simulated threshold-stimulus detection task (TSDT), these ISFs were strongly correlated with stimulus detection probabilities and latencies. The results thus show that several phenomena observed in many empirical studies emerge concurrently in the model dynamics, which yields mechanistic insight into how infra-slow excitability fluctuations in large-scale neuronal networks may modulate fast oscillations and perceptual processing. The model also makes several novel predictions that can be experimentally tested in future studies

Hyperedge bundling : A practical solution to spurious interactions in MEG/EEG source connectivity analyses

Inter-areal functional connectivity (FC), neuronal synchronization in particular, is thought to constitute a key systems-level mechanism for coordination of neuronal processing and communication between brain regions. Evidence to support this hypothesis has been gained largely using invasive electrophysiological approaches. In humans, neuronal activity can be non-invasively recorded only with magneto-and electroencephalography (MEG/EEG), which have been used to assess FC networks with high temporal resolution and whole-scalp coverage. However, even in source-reconstructed MEG/EEG data, signal mixing, or "source leakage", is a significant confounder for FC analyses and network localization. Signal mixing leads to two distinct kinds of false-positive observations: artificial interactions (AI) caused directly by mixing and spurious interactions (SI) arising indirectly from the spread of signals from true interacting sources to nearby false loci. To date, several interaction metrics have been developed to solve the AI problem, but the SI problem has remained largely intractable in MEG/EEG all-to-all source connectivity studies. Here, we advance a novel approach for correcting SIs in FC analyses using source-reconstructed MEG/EEG data. Our approach is to bundle observed FC connections into hyperedges by their adjacency in signal mixing. Using realistic simulations, we show here that bundling yields hyperedges with good separability of true positives and little loss in the true positive rate. Hyperedge bundling thus significantly decreases graph noise by minimizing the false-positive to true-positive ratio. Finally, we demonstrate the advantage of edge bundling in the visualization of large-scale cortical networks with real MEG data. We propose that hypergraphs yielded by bundling represent well the set of true cortical interactions that are detectable and dissociable in MEG/EEG connectivity analysis.Peer reviewe

Comparison of methods to identify modules in noisy or incomplete brain networks

open6siCommunity structure, or "modularity," is a fundamentally important aspect in the organization of structural and functional brain networks, but their identification with community detection methods is confounded by noisy or missing connections. Although several methods have been used to account for missing data, the performance of these methods has not been compared quantitatively so far. In this study, we compared four different approaches to account for missing connections when identifying modules in binary and weighted networks using both Louvain and Infomap community detection algorithms. The four methods are "zeros," "row-column mean," "common neighbors," and "consensus clustering." Using Lancichinetti-Fortunato-Radicchi benchmark-simulated binary and weighted networks, we find that "zeros," "row-column mean," and "common neighbors" approaches perform well with both Louvain and Infomap, whereas "consensus clustering" performs well with Louvain but not Infomap. A similar pattern of results was observed with empirical networks from stereotactical electroencephalography data, except that "consensus clustering" outperforms other approaches on weighted networks with Louvain. Based on these results, we recommend any of the four methods when using Louvain on binary networks, whereas "consensus clustering" is superior with Louvain clustering of weighted networks. When using Infomap, "zeros" or "common neighbors" should be used for both binary and weighted networks. These findings provide a basis to accounting for noisy or missing connections when identifying modules in brain networks.openWilliams N.; Arnulfo G.; Wang S.H.; Nobili L.; Palva S.; Palva J.M.Williams, N.; Arnulfo, G.; Wang, S. H.; Nobili, L.; Palva, S.; Palva, J. M

Dynamic large-scale network synchronization from perception to action

Sensory-guided actions entail the processing of sensory information, generation of perceptual decisions, and the generation of appropriate actions. Neuronal activity underlying these processes is distributed into sensory, fronto-parietal, and motor brain areas, respectively. How the neuronal processing is coordinated across these brain areas to support functions from perception to action remains unknown. We investigated whether phase synchronization in large-scale networks coordinate these processes. We recorded human cortical activity with magnetoencephalography (MEG) during a task in which weak somatosensory stimuli remained unperceived or were perceived. We then assessed dynamic evolution of phase synchronization in large-scale networks from source-reconstructed MEG data by using advanced analysis approaches combined with graph theory. Here we show that perceiving and reporting of weak somatosensory stimuli is correlated with sustained strengthening of large-scale synchrony concurrently in delta/theta (3-7 Hz) and gamma (40-60 Hz) frequency bands. In a data-driven network localization, we found this synchronization to dynamically connect the task-relevant, that is, the fronto-parietal, sensory, and motor systems. The strength and temporal pattern of interareal synchronization were also correlated with the response times. These data thus show that key brain areas underlying perception, decision-making, and actions are transiently connected by large-scale dynamic phase synchronization in the delta/theta and gamma bands.Peer reviewe