A Dose Relationship Between Brain Functional Connectivity and Cumulative Head Impact Exposure in Collegiate Water Polo Players.

A growing body of evidence suggests that chronic, sport-related head impact exposure can impair brain functional integration and brain structure and function. Evidence of a robust inverse relationship between the frequency and magnitude of repeated head impacts and disturbed brain network function is needed to strengthen an argument for causality. In pursuing such a relationship, we used cap-worn inertial sensors to measure the frequency and magnitude of head impacts sustained by eighteen intercollegiate water polo athletes monitored over a single season of play. Participants were evaluated before and after the season using computerized cognitive tests of inhibitory control and resting electroencephalography. Greater head impact exposure was associated with increased phase synchrony [r (16) > 0.626, p < 0.03 corrected], global efficiency [r (16) > 0.601, p < 0.04 corrected], and mean clustering coefficient [r (16) > 0.625, p < 0.03 corrected] in the functional networks formed by slow-wave (delta, theta) oscillations. Head impact exposure was not associated with changes in performance on the inhibitory control tasks. However, those with the greatest impact exposure showed an association between changes in resting-state connectivity and a dissociation between performance on the tasks after the season [r (16) = 0.481, p = 0.043] that could also be attributed to increased slow-wave synchrony [F (4, 135) = 113.546, p < 0.001]. Collectively, our results suggest that athletes sustaining the greatest head impact exposure exhibited changes in whole-brain functional connectivity that were associated with altered information processing and inhibitory control

Reliability of Graph Measures Derived from Resting-State MEG Data Using Source Space Functional Connectivity Analysis

The reliability of global graph measures derived from neuroimaging data is an important criterion for their use as biomarkers for neurological disorders. This study examined the reliability of the global efficiency (GE), characteristic path length (CPL), transitivity, and synchronizability of functional whole-brain and intra-hemispheric networks based on resting-state magnetoencephalography. Brain sources were reconstructed using atlas-based beamforming, and functional connectivity in six frequency bands was estimated using the debiased weighted phase lag index. An optimal threshold of 100% was chosen based on test-retest reliability of the measures. At this threshold, test-retest reliability of the GE, CPL, and transitivity was mostly fair to excellent except for in the delta band. However, test-retest reliability of the synchronizability was mostly poor to fair. There was no significant effect of gender on any graph measure. Overall, these results indicate that the GE, CPL, and transitivity in most of the frequency bands may be useful biomarkers

Evaluation and implementation of functional cerebral biomarkers in Alzheimer’s disease

The aim of this thesis was to evaluate and implement functional cerebral biomarkers in Alzheimer’s disease (AD) with respect to pathophysiology, disease severity, prognosis and treatment effect in medical trials. We focused on functional cerebral biomarkers that assess synaptic activity and functional connectivity using electroencephalography (EEG), magnetoencephalography (MEG) and 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET). In the different chapters a broad range of challenges associated with this topic was covered. We started by using FDG- PET to observe the effects of the experimental treatment of AD patients with the medical food Souvenaid, followed by EEG as treatment outcome measure in a trial with the drug PQ912. Next to the primary outcomes, the results of these studies revealed that more research was needed to observe which markers could observe reliable, reproducible and valid results and what the factors were that could influence their ability to do this. The EEG markers, rather than the FDG- PET markers, showed promising results. Therefore, we aimed to investigate the effects of sensitivity, reproducibility, heterogeneity of the population and treatment efficacy, while maintaining a well-defined study population and study design, on EEG biomarkers. We first investigated the reproducibility of AD related changes in functional connectivity captured by different measures in electroencephalography (EEG) and magnetoencephalography (MEG). Second, we evaluated the influence of subtypes of AD on various EEG measures and, on the other hand, we used EEG to find heterogeneity and to predict clinical progression

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

Investigation of neural activity in Schizophrenia during resting-state MEG : using non-linear dynamics and machine-learning to shed light on information disruption in the brain

Environ 25% de la population mondiale est atteinte de troubles psychiatriques qui sont typiquement associés à des problèmes comportementaux, fonctionnels et/ou cognitifs et dont les corrélats neurophysiologiques sont encore très mal compris. Non seulement ces dysfonctionnements réduisent la qualité de vie des individus touchés, mais ils peuvent aussi devenir un fardeau pour les proches et peser lourd dans l’économie d’une société. Cibler les mécanismes responsables du fonctionnement atypique du cerveau en identifiant des biomarqueurs plus robustes permettrait le développement de traitements plus efficaces. Ainsi, le premier objectif de cette thèse est de contribuer à une meilleure caractérisation des changements dynamiques cérébraux impliqués dans les troubles mentaux, plus précisément dans la schizophrénie et les troubles d’humeur. Pour ce faire, les premiers chapitres de cette thèse présentent, en intégral, deux revues de littératures systématiques que nous avons menées sur les altérations de connectivité cérébrale, au repos, chez les patients schizophrènes, dépressifs et bipolaires. Ces revues révèlent que, malgré des avancées scientifiques considérables dans l’étude de l’altération de la connectivité cérébrale fonctionnelle, la dimension temporelle des mécanismes cérébraux à l’origine de l’atteinte de l’intégration de l’information dans ces maladies, particulièrement de la schizophrénie, est encore mal comprise. Par conséquent, le deuxième objectif de cette thèse est de caractériser les changements cérébraux associés à la schizophrénie dans le domaine temporel. Nous présentons deux études dans lesquelles nous testons l’hypothèse que la « disconnectivité temporelle » serait un biomarqueur important en schizophrénie. Ces études explorent les déficits d’intégration temporelle en schizophrénie, en quantifiant les changements de la dynamique neuronale dite invariante d’échelle à partir des données magnétoencéphalographiques (MEG) enregistrés au repos chez des patients et des sujets contrôles. En particulier, nous utilisons (1) la LRTCs (long-range temporal correlation, ou corrélation temporelle à longue-distance) calculée à partir des oscillations neuronales et (2) des analyses multifractales pour caractériser des modifications de l’activité cérébrale arythmique. Par ailleurs, nous développons des modèles de classification (en apprentissage-machine supervisé) pour mieux cerner les attributs corticaux et sous-corticaux permettant une distinction robuste entre les patients et les sujets sains. Vu que ces études se basent sur des données MEG spontanées enregistrées au repos soit avec les yeux ouvert, ou les yeux fermées, nous nous sommes par la suite intéressés à la possibilité de trouver un marqueur qui combinerait ces enregistrements. La troisième étude originale explore donc l’utilité des modulations de l’amplitude spectrale entre yeux ouverts et fermées comme prédicteur de schizophrénie. Les résultats de ces études démontrent des changements cérébraux importants chez les patients schizophrènes au niveau de la dynamique d’invariance d’échelle. Elles suggèrent une dégradation du traitement temporel de l’information chez les patients, qui pourrait être liée à leurs symptômes cognitifs et comportementaux. L’approche multimodale de cette thèse, combinant la magétoencéphalographie, analyses non-linéaires et apprentissage machine, permet de mieux caractériser l’organisation spatio-temporelle du signal cérébrale au repos chez les patients atteints de schizophrénie et chez des individus sains. Les résultats fournissent de nouvelles preuves supportant l’hypothèse d’une « disconnectivité temporelle » en schizophrénie, et étendent les recherches antérieures, en explorant la contribution des structures cérébrales profondes et en employant des mesures non-linéaires avancées encore sous-exploitées dans ce domaine. L’ensemble des résultats de cette thèse apporte une contribution significative à la quête de nouveaux biomarqueurs de la schizophrénie et démontre l’importance d’élucider les altérations des propriétés temporelles de l’activité cérébrales intrinsèque en psychiatrie. Les études présentées offrent également un cadre méthodologique pouvant être étendu à d’autres psychopathologie, telles que la dépression.Psychiatric disorders affect nearly a quarter of the world’s population. These typically bring about debilitating behavioural, functional and/or cognitive problems, for which the underlying neural mechanisms are poorly understood. These symptoms can significantly reduce the quality of life of affected individuals, impact those close to them, and bring on an economic burden on society. Hence, targeting the baseline neurophysiology associated with psychopathologies, by identifying more robust biomarkers, would improve the development of effective treatments. The first goal of this thesis is thus to contribute to a better characterization of neural dynamic alterations in mental health illnesses, specifically in schizophrenia and mood disorders. Accordingly, the first chapter of this thesis presents two systematic literature reviews, which investigate the resting-state changes in brain connectivity in schizophrenia, depression and bipolar disorder patients. Great strides have been made in neuroimaging research in identifying alterations in functional connectivity. However, these two reviews reveal a gap in the knowledge about the temporal basis of the neural mechanisms involved in the disruption of information integration in these pathologies, particularly in schizophrenia. Therefore, the second goal of this thesis is to characterize the baseline temporal neural alterations of schizophrenia. We present two studies for which we hypothesize that the resting temporal dysconnectivity could serve as a key biomarker in schizophrenia. These studies explore temporal integration deficits in schizophrenia by quantifying neural alterations of scale-free dynamics using resting-state magnetoencephalography (MEG) data. Specifically, we use (1) long-range temporal correlation (LRTC) analysis on oscillatory activity and (2) multifractal analysis on arrhythmic brain activity. In addition, we develop classification models (based on supervised machine-learning) to detect the cortical and sub-cortical features that allow for a robust division of patients and healthy controls. Given that these studies are based on MEG spontaneous brain activity, recorded at rest with either eyes-open or eyes-closed, we then explored the possibility of finding a distinctive feature that would combine both types of resting-state recordings. Thus, the third study investigates whether alterations in spectral amplitude between eyes-open and eyes-closed conditions can be used as a possible marker for schizophrenia. Overall, the three studies show changes in the scale-free dynamics of schizophrenia patients at rest that suggest a deterioration of the temporal processing of information in patients, which might relate to their cognitive and behavioural symptoms. The multimodal approach of this thesis, combining MEG, non-linear analyses and machine-learning, improves the characterization of the resting spatiotemporal neural organization of schizophrenia patients and healthy controls. Our findings provide new evidence for the temporal dysconnectivity hypothesis in schizophrenia. The results extend on previous studies by characterizing scale-free properties of deep brain structures and applying advanced non-linear metrics that are underused in the field of psychiatry. The results of this thesis contribute significantly to the identification of novel biomarkers in schizophrenia and show the importance of clarifying the temporal properties of altered intrinsic neural dynamics. Moreover, the presented studies offer a methodological framework that can be extended to other psychopathologies, such as depression

Mutations in the SPAST gene causing hereditary spastic paraplegia arerelated to global topological alterations in brain functional networks

Aim: Our aim was to describe the rearrangements of the brain activity related to genetic mutations in the SPAST gene. Methods: Ten SPG4 patients and ten controls underwent a 5 min resting state magnetoencephalography recording and neurological examination. A beamformer algorithm reconstructed the activity of 90 brain areas. The phase lag index was used to estimate synchrony between brain areas. The minimum spanning tree was used to estimate topological metrics such as the leaf fraction (a measure of network integration) and the degree divergence (a measure of the resilience of the network against pathological events). The betweenness centrality (a measure to estimate the centrality of the brain areas) was used to estimate the centrality of each brain area. Results: Our results showed topological rearrangements in the beta band. Specifically, the degree divergence was lower in patients as compared to controls and this parameter related to clinical disability. No differences appeared in leaf fraction nor in betweenness centrality. Conclusion: Mutations in the SPAST gene are related to a reorganization of the brain topology

The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease

Current biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease. However, in spite of its added value they are not part of the standard of care in clinical practice in dementia. In this paper we review what these neurophysiological techniques can add to the early diagnosis of AD, whether results in both modalities are related to each other or not, as well as the need of its validation against current biomarkers. We discuss their potential implications for the better understanding of the pathophysiological mechanisms of the disease as well as the need of performing simultaneous M/EEG recordings to better understand discrepancies between these two techniques. Finally, more studies are needed studying M/EEG with amyloid and Tau biomarkers

Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders

Healthy brain functioning depends on efficient communication of information between brain regions, forming complex networks. By quantifying synchronisation between brain regions, a functionally connected brain network can be articulated. In neurodevelopmental disorders, where diagnosis is based on measures of behaviour and tasks, a measure of the underlying biological mechanisms holds promise as a potential clinical tool. Graph theory provides a tool for investigating the neural correlates of neuropsychiatric disorders, where there is disruption of efficient communication within and between brain networks. This research aimed to use recent conceptualisation of graph theory, along with measures of behaviour and cognitive functioning, to increase understanding of the neurobiological risk factors of atypical development. Using magnetoencephalography to investigate frequency-specific temporal dynamics at rest, the research aimed to identify potential biological markers derived from sensor-level whole-brain functional connectivity. Whilst graph theory has proved valuable for insight into network efficiency, its application is hampered by two limitations. First, its measures have hardly been validated in MEG studies, and second, graph measures have been shown to depend on methodological assumptions that restrict direct network comparisons. The first experimental study (Chapter 3) addressed the first limitation by examining the reproducibility of graph-based functional connectivity and network parameters in healthy adult volunteers. Subsequent chapters addressed the second limitation through adapted minimum spanning tree (a network analysis approach that allows for unbiased group comparisons) along with graph network tools that had been shown in Chapter 3 to be highly reproducible. Network topologies were modelled in healthy development (Chapter 4), and atypical neurodevelopment (Chapters 5 and 6). The results provided support to the proposition that measures of network organisation, derived from sensor-space MEG data, offer insights helping to unravel the biological basis of typical brain maturation and neurodevelopmental conditions, with the possibility of future clinical utility