The role of multi-scale phase synchronization and cross-frequency interactions in cognitive integration

Neuronal processing is distributed into anatomically distinct, largely specialized, neuronal populations. These populations undergo rhythmic fluctuations in excitability, which are commonly known as neuronal oscillations. Electrophysiological studies of neuronal activity have shown that phase synchronization of oscillations within frequencies characterizes both resting state and task execution and that its strength is correlated with task performance. Therefore phase-synchronization within frequencies is thought to support communication between oscillating neuronal populations and thereby integration and coordination of anatomically distributed processing in cognitive functions. However, it has remained open if and how phase synchronization is associated with directional flow of information. Furthermore, oscillations and synchronization are observed concurrently in multiple frequencies, which are thought to underlie distinct computational functions. Little is known how oscillations and synchronized networks of different frequencies in the human brain are integrated and enable unified cognitive function and experience. In the first study of this thesis, we developed a measure of directed connectivity in networks of coupled oscillators, called Phase Transfer Entropy (Phase TE) and tested if Phase TE could detect directional flow in simulated data in the presence of noise and signal mixing. Results showed that Phase TE indeed reliably detected information flow under these conditions and was computationally efficient. In the other three studies, we investigated if two different forms of inter-areal cross-frequency coupling (CFC), namely cross-frequency phase synchrony (CFS) and phase-amplitude coupling (PAC), could support integration and coordination of neuronal processing distributed across frequency bands in the human brain. In the second study, we analyzed source-reconstructed magneto- and electroencephalographic (M/EEG) data to investigate whether inter-areal CFS could be observed between within-frequency synchronized networks and thereby support the coordination of spectrally distributed processing in visual working memory (VWM). The results showed that CFS was increased during VWM maintenance among theta to gamma frequency bands and the strength of CFS networks predicted individual VWM capacity. Spectral patterns of CFS were found to be different from PAC, indicating complementary roles for both mechanisms. In the third study, we analyzed source-reconstructed M/EEG data to investigate whether inter-areal CFS and PAC could be observed during two multi-object visual tracking tasks and thereby support visual attention. PAC was found to be significantly correlated with object load in both tasks, and CFS in one task. Further, patterns of CFS and PAC differed significantly between subjects with high and low capacity for visual attention. In the fourth study, we analyzed intracerebral stereo-electroencephalographic data (SEEG) and source-reconstructed MEG data to investigate whether CFS and PAC are present also in resting state. Further, in order to address concerns about observations of CFC being spurious and caused by non-sinusoidal or non-zero mean signal waveforms, we introduced a new approach to identify true inter-areal CFC connections and discard potentially spurious ones. We observed both inter-areal CFS and PAC, and showed that a significant part of connections was unambiguously true and non-spurious. Spatial profiles differed between CFS and PAC, but were consistent across datasets. Together, the results from studies II-IV provide evidence that inter-areal CFS and PAC, in complementary ways, connect frequency-specific phase-synchronized networks that involve functionally specialized regions across the cortex to support complex functions such as VWM and attention, and also characterize the resting state. Inter-areal CFC thus may be crucial for the coordination and integration of spectrally distributed processing and the emergence of introspectively coherent cognitive function.Keskeinen kysymys aivotutkimuksessa on, kuinka ajattelu ja kognitio syntyvät ihmisaivojen 10^15 hermosolussa. Informaation käsittely aivoissa tapahtuu suurissa hermosolupopulaatioissa, jotka ovat toiminnallisesti erikoistuneita ja anatomisesti eroteltuja eri aivoalueille. Niiden aktivaatiorakenteiden jaksollisia muutoksia kutsutaan aivorytmeiksi eli oskillaatioiksi. Hermosolupopulaatioiden välistä viestintää edesauttaa niiden toiminnan samantahtisuus eli synkronoituminen. Sähköfysiologisissa tutkimuksissa on havaittu aivorytmien synkronoituvan sekä lepomittausten että tehtävien suorituksen aikana siten että tämä synkronoituminen ennustaa kognitiivissa tehtävissä suoriutumista. Oskillaatioiden vaihesynkronia ei kuitenkaan kerro niiden välisen vuorovaikutuksen suunnasta. Tämän lisäksi oskillaatioita ja niiden välistä synkroniaa havaitaan yhtäaikaisesti lukuisilla eri taajuuksilla, joiden ajatellaan olevan vastuussa erillisistä laskennallisista ja kognitiivisista toiminnoista. Toistaiseksi on kuitenkin jäänyt kartoittamatta, miten informaation käsittely eri taajuuksilla yhdistetään yhtenäisiksi kognitiivisiksi toiminnoiksi, ja havaitaanko myös eri taajuisten oskillaatioverkkojen välillä synkroniaa. Väitöskirjan ensimmäisessä osatyössä on kehitetty uusi tapata mitata oskillaattoriverkkojen vuorovaikutusten suuntia, jonka toimivuus todennettiin simuloimalla synkronoituneita hermosolupopulaatioita. Väitöskirjan muissa osatöissä on tutkittu havaitaanko ihmisaivoissa eri taajuisten oskillaatioiden välistä synkronoitumista. Erityisesti tutkittiin kahta erilaista synkronian muotoa, joista ensimmäinen (’cross- frequency phase synchrony’,CFS) mittaa kahden oskillaation välistä vaihesuhdetta ja toinen (’phase-amplitude coupling’, PAC) vaiheen ja amplitudin suhdetta. Väitöskirjan toisessa osassa tutkittiin, selittääkö CFS koehenkilöiden suoriutumista näkötyömuistitehtävässä. Tutkimukseen osallistuneilta koehenkilöiltä mitattiin aivosähkökäyrä (EEG) ja aivomagneettikäyrä (MEG), joiden avulla selvitettiin havaitaanko aivoalueiden välistä synkroniaa (CFS). Tutkimustulokset osoittivat, että koehenkilöiden CFS oli korkeampi näkötyömuistitehtävän mielessä pitämisen aikana theta-taajuuksista gamma-taajuuksiin asti ja että CFS-verkkojen vahvuus ennusti yksilöllistä työmuistikapasiteettia. Kolmannessa tutkimuksessa analysoitiin MEG- ja EEG-aivokuvantamislaitteita käyttäen onko aivoalueiden välillä CFS:ä ja PAC:a kahdessa näkötarkkaavaisuustehtävässä. PAC lisääntyi tilastollisesti merkitsevästi tehtävän vaikeuden mukaan kummassakin tehtävässä, kun taas CFS lisääntyi yhdessä tehtävässä. Lisäksi CFS ja PAC taajuusparit olivat erilaisia hyvin suoriutuvien koehenkilöiden sekä heikosti suoriutuvien koehenkilöiden välillä. Neljännessä tutkimuksessa tutkittiin havaitaanko CFS:ä ja PAC:a aivojen lepotilassa. Aivokuoren aktiivisuutta mitattiin MEG:llä sekä epilepsiapotilailta aivoihin kirurgisesti asetetuilla elektrodeilla. CFS:ä sekä PAC:a havaittiin kummallakin menetelmällä. Lisäksi kehitimme menetelmän joka vähentää väärien havaintojen todennäköisyyttä ja lisää aitojen CFS ja PAC yhteyksien havaitsemista. Tulokset osoittavat, että merkittävä osuus yhteyksistä aivoalueiden välillä on aitoja. CFS- ja PAC-profiilit erosivat toisistaan, mutta olivat samanlaisia eri menetelmillä tutkittaessa. Yhdistettynä tulokset tutkimuksista II–IV viittaavat siihen, että CFS ja PAC yhdistävät eri taajuuksille ja aivoalueille hajautettua informaation käsittelyä. CFS:sää ja PAC:ia havaittiin aivojen lepotilassa mutta myös tarkkaavaisuus- ja näkötyömuistitehtävän aikana. CFS ja PAC saattavat mahdollistaa eri taajuisten aivorytmien ja hajautettujen prosessien koordinaation ja yhdistämisen

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

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

Measuring information-transfer delays

In complex networks such as gene networks, traffic systems or brain circuits it is important to understand how long it takes for the different parts of the network to effectively influence one another. In the brain, for example, axonal delays between brain areas can amount to several tens of milliseconds, adding an intrinsic component to any timing-based processing of information. Inferring neural interaction delays is thus needed to interpret the information transfer revealed by any analysis of directed interactions across brain structures. However, a robust estimation of interaction delays from neural activity faces several challenges if modeling assumptions on interaction mechanisms are wrong or cannot be made. Here, we propose a robust estimator for neuronal interaction delays rooted in an information-theoretic framework, which allows a model-free exploration of interactions. In particular, we extend transfer entropy to account for delayed source-target interactions, while crucially retaining the conditioning on the embedded target state at the immediately previous time step. We prove that this particular extension is indeed guaranteed to identify interaction delays between two coupled systems and is the only relevant option in keeping with Wiener’s principle of causality. We demonstrate the performance of our approach in detecting interaction delays on finite data by numerical simulations of stochastic and deterministic processes, as well as on local field potential recordings. We also show the ability of the extended transfer entropy to detect the presence of multiple delays, as well as feedback loops. While evaluated on neuroscience data, we expect the estimator to be useful in other fields dealing with network dynamics

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

cited By 0Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase-amplitude coupling (PAC) or by n:m-cross-frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.Peer reviewe

Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations

Neuronal oscillations, their inter-areal synchronization, and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. We hypothesized that this variability could be partially explained by genetic polymorphisms in neuromodulatory genes. We recorded resting-state magnetoencephalography (MEG) from 82 healthy participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in catechol- -methyltransferase ( ) Val Met and brain-derived neurotrophic factor ( ) Val Met. Both and polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only polymorphism affected the strength of large-scale synchronization. Our findings demonstrate that and genetic polymorphisms contribute to inter-individual variability in neuronal oscillation dynamics. Comparison of these results to computational modeling of near-critical synchronization dynamics further suggested that and polymorphisms influenced local oscillations by modulating the excitation-inhibition balance according to the brain criticality framework

Data from: 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