Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

Cross-frequency coupling (CFC) is thought to represent a basic mechanism of functional integration of neural networks across distant brain regions. In this study, we analyzed CFC profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 30 mild traumatic brain injury (mTBI) patients and 50 controls. We used mutual information (MI) to quantify the phase-to-amplitude coupling (PAC) of activity among the recording sensors in six nonoverlapping frequency bands. After forming the CFC-based functional connectivity graphs, we employed a tensor representation and tensor subspace analysis to identify the optimal set of features for subject classification as mTBI or control. Our results showed that controls formed a dense network of stronger local and global connections indicating higher functional integration compared to mTBI patients. Furthermore, mTBI patients could be separated from controls with more than 90% classification accuracy. These findings indicate that analysis of brain networks computed from resting-state MEG with PAC and tensorial representation of connectivity profiles may provide a valuable biomarker for the diagnosis of mTBI

Communication between Brain Areas Based on Nested Oscillations

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Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods

The human brain can be divided into multiple areas, each responsible for different aspects of behaviour. Healthy brain function relies upon efficient connectivity between these areas and, in recent years, neuroimaging has been revolutionised by an ability to estimate this connectivity. In this paper we discuss measurement of network connectivity using magnetoencephalography (MEG), a technique capable of imaging electrophysiological brain activity with good (~5mm) spatial resolution and excellent (~1ms) temporal resolution. The rich information content of MEG facilitates many disparate measures of connectivity between spatially separate regions and in this paper we discuss a single metric known as power envelope correlation. We review in detail the methodology required to measure power envelope correlation including i) projection of MEG data into source space, ii) removing confounds introduced by the MEG inverse problem and iii) estimation of connectivity itself. In this way, we aim to provide researchers with a description of the key steps required to assess envelope based functional networks, which are thought to represent an intrinsic mode of coupling in the human brain. We highlight the principal findings of the techniques discussed, and furthermore, we show evidence that this method can probe how the brain forms and dissolves multiple transient networks on a rapid timescale in order to support current processing demand. Overall, power envelope correlation offers a unique and verifiable means to gain novel insights into network coordination and is proving to be of significant value in elucidating the neural dynamics of the human connectome in health and disease

Modelling of the switching behavior of functional connectivity microstates (FCμstates) as a novel biomarker for mild cognitive impairment

It is evident the need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI). MCI patients have a high risk of developing Alzheimer’s disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings about the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys)function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings. The activity of different brain rhythms {δ, θ, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine ninety anatomical regions of interest (ROIs). A dynamic functional connectivity graph (DFCG) was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and also cross-frequency coupling pairs (28). We analysed DFCG profiles of neuromagnetic resting state recordings of 18 Mild Cognitive Impairment (MCI) patients and 20 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments that further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. Estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (nμstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation. We succeeded a high classification accuracy on the blind dataset (85 %) which further supports the proposed Markovian modelling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could manipulate properly the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI

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

Dynamics of large-scale electrophysiological networks: a technical review

For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography / electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity

Corrélats neuronaux de la mémoire de travail en magnétoencéphalographie à l’état de repos

Purpose: There are few studies demonstrating the link between neural oscillations in magnetoencephalography (MEG) at rest and cognitive performance. Working memory is one of the most studied cognitive processes and is the ability to manipulate information on items kept in short-term memory. Heister & al. (2013) showed correlation patterns between brain oscillations at rest in MEG and performance in a working memory task (n-back). These authors showed that delta/theta activity in fronto-parietal areas is related to working memory performance. In this study, we use resting state MEG oscillations to validate these correlations with both of verbal (VWM) and spatial (SWM) working memory, and test their specificity in comparison with other cognitive abilities. Methods: We recorded resting state MEG and used clinical neuropsychological tests to assess working memory performance in 18 volunteers (6 males and 12 females). The other neuropsychological tests of the WAIS-IV were used as control tests to assess the specificity of the correlation patterns with working memory. We calculated means of Power Spectrum Density for different frequency bands (delta, 1-4Hz; theta, 4-8Hz; alpha, 8-13Hz; beta, 13-30Hz; gamma1, 30-59Hz; gamma2, 61-90Hz; gamma3, 90-120Hz; large gamma, 30-120Hz) and correlated MEG power normalised for the maximum in each frequency band at the sensor level with working memory performance. We then grouped the sensors showing a significant correlation by using a cluster algorithm. Results: We found positive correlations between both types of working memory performance and clusters in the bilateral posterior and right fronto-temporal regions for the delta band (r2 =0.73), in the fronto-middle line and right temporal regions for the theta band (r2 =0.63) as well as in the parietal regions for the alpha band (r2 =0.78). Verbal working memory and spatial working memory share a common fronto-parietal cluster of sensors but also show specific clusters. These clusters are specific to working memory, as compared to those obtained for other cognitive abilities and right posterior parietal areas, specially in slow frequencies, appear to be specific to working memory process. Conclusions: Slow frequencies (1-13Hz) but more precisely in delta/theta bands (1-8Hz), recorded at rest with magnetoencephalography, predict working memory performance and support the role of a fronto-parietal network in working memory.L’étude et la caractérisation des oscillations cérébrales au repos en magnétoencéphalographie (MEG) en relation avec les performances cognitives ont été peu étudiées. La mémoire de travail permet la manipulation de l’information sur des éléments qui y sont temporairement stockés. Heister et al. (2013) ont étudié l’association entre la mémoire de travail et les oscillations cérébrales au repos. Leurs résultats mettent en évidence un lien entre la performance dans une tâche de mémoire de travail (n-back) et une activité delta/thêta fronto-pariétale droite. Notre projet utilise la MEG au repos pour valider ces corrélations avec des tests standardisés de mémoire de travail verbale et spatiale, et tester leur spécificité en comparaison avec d’autres capacités cognitives. Méthodologie: Nous avons enregistré 18 participants volontaires (6 hommes et 12 femmes) en magnétoencéphalographie de repos. Nous avons évalué les capacités de mémoire de travail verbale et spatiale au moyen de l’Indice de Mémoire de Travail de l’Échelle d'Intelligence de Wechsler pour Adultes - quatrième édition (WAIS-IV), et le sous test d’Addition Spatiale de l’Échelle Clinique de Mémoire de Wechsler - quatrième édition. Nous avons aussi calculé les corrélations avec les autres Indices du WAIS-IV pour évaluer la spécificité des patrons de corrélation observés avec la mémoire de travail. Nous avons moyenné la puissance de la décomposition spectrale pour chaque bande de fréquence (delta, 1-4Hz; thêta, 4-8Hz; alpha, 8-13Hz; beta, 13- 30Hz; gamma1, 30-59Hz; gamma2, 61-90Hz; gamma3, 90-120Hz; large gamma, 30-120Hz) puis nous avons corrélé cette puissance normalisée par le maximum de chaque bande au niveau des capteurs avec la performance dans un test de mémoire de travail. Nous avons ensuite regroupé les capteurs significatifs en cluster d'intérêt. Résultats: Nous avons mis en évidence une corrélation positive entre la performance en mémoire de travail et les régions fronto-pariétale droite pour la bande delta (r2=0,73), fronto-temporale médiale droite pour la bande thêta (r2=0,63), et pariéto-centrale pour la bande alpha (r2=0,78). Les résultats suggèrent que les mémoires de travail verbale et spatiale partagent un même réseau fronto-pariétal. Chaque type de mémoire de travail a aussi des corrélations spécifiques dans des régions différentes pour certaines banques de fréquence. Comparée à d'autres habiletés cognitives, la mémoire de travail est associée à des patrons de corrélations spécifiques avec le MEG au repos et une région pariétale droite qui semble spécialisée dans les basses fréquences à la mémoire de travail. Conclusions: Les basses fréquences (1-13Hz) et plus précisément delta/thêta (1-8Hz), enregistrées au repos en MEG dans les régions frontales et pariétales, permettent de prédire la performance de mémoire de travail ce qui supporte le rôle d’un réseau fronto-pariétal

Ruhenetzwerke von Parkinsonpatienten – Effekte der Dopamintherapie

Die Motivation der hier vorliegenden Studie war es, Ruhenetzwerke von Parkinsonpatienten mit der Elektroenzephalographie (EEG) auf einer globalen Hirnnetzwerkebene zu analysieren. Als übergreifendes Ziel der Arbeit galt der vertiefte Einblick in pathophysiologische Mechanismen und Effekte der dopaminergen Therapie. Dabei wurden im Einzelnen folgende Hypothesen untersucht: 1) Die Extraktion von Ruhenetzwerken in einem globalen Analyseansatz ohne a priori Annahmen wurde bislang für magnetenzephalographische (MEG) Daten demonstriert. Es wurde angenommen, dass sich dieser Ansatz auch auf EEG Daten übertragen lässt und robuste Ergebnisse generieren würde. 2) Das Verständnis von Morbus Parkinson geht über eine reine Bewegungsstörung weit hinaus. Als Ausdruck einer solchen globalen Neurodegeneration waren daher pathologische Netzwerkveränderungen im Vergleich von Patienten und Gesunden zu erwarten, die sich nicht nur auf motorische Netzwerke beschränken würden. 3) Die dopaminerge Therapie stellt unverändert den zentralen Baustein der Behandlung der Parkinsonerkrankung dar. Als Ausdruck der resultierenden klinischen Besserung waren auch auf der Netzwerkebene spezifische Therapieeffekte zu erwarten. Inwiefern dies durch Restitution physiologischer Netzwerkmuster oder Etablierung einer alternativen Netzwerkstruktur erfolgen würde, sollte näher untersucht werden. ad 1) In der Literatur mit funktioneller Magnetresonanztomographie (fMRT) gut untersuchte und als etabliert geltende Ruhenetzwerke konnten auch in den EEG Daten identifiziert werden. Dabei wurde die eigentliche Netzwerkextraktion mittels einer Independent Component Analysis (ICA) durch Lösung des inversen Problems im Quellenraum lokalisiert. So konnte neben der im EEG grundsätzlich guten zeitlichen Auflösung auch die räumliche Auflösung optimiert werden. ad 2) Bei den näher untersuchten Ruhenetzwerken ließen sich spezifische räumliche und frequenzbezogene Veränderungen feststellen, welche in die bestehende Forschungsliteratur eingegliedert werden konnten und gleichzeitig das Verständnis dieser Veränderungen erweiterten. Insbesondere für den Bereich von motorischen Arealen zeigte sich ein präzises pathologisches Korrelat im b-Frequenzband, was erneut die Schlüsselrolle von b-Oszillationen betonte. Desweiteren zeigten sich Veränderungen des Default Mode Network (DMN) und des visuellen Netzwerks mit aktuell unklarer klinischer Relevanz. ad 3) Im Bereich der motorischen Kortexareale zeigte das supplementär motorische Areal (SMA) im Sinne einer Restitution auf nahezu physiologische räumliche Netzwerkparameter unmittelbare Effekte der medikamentösen Therapie. Dies war im Einklang mit einer wachsenden Evidenz vor allem aus der fMRT Literatur. Als neuer Aspekt ergab sich nun der offenbar spezifische Effekt im g-Frequenzband