Modelling neuronal interactions using a directed network can be used to provide insight into the activity of the brain during experimental tasks. Magnetoencephalography (MEG) allows for the observation of the fast neuronal dynamics necessary to characterize the activity of sources and their interactions. A network representation of these sources and their connections can be formed by mapping them to nodes and their connection strengths to edge weights. Dynamic Causal Modelling (DCM) presents a Bayesian framework to estimate the parameters of these networks, as well as the ability to test hypotheses on the structure of the network itself using Bayesian model comparison. DCM uses a neurologically-informed representation of the active neural sources, which leads to an underdetermined system and increased complexity in estimating the network parameters. This work shows that inform- ing the MEG DCM source location with prior distributions defined using a MEG source localization algorithm improves model selection accuracy. DCM inversion of a group of candidate models shows an enhanced ability to identify a ground-truth network structure when source-localized prior means are used. / Master of Science

La magnéto-encéphalographie (MEG) mesure l´activité cérébrale avec un excellent décours temporel mais sa localisation sur la surface corticale souffre d´une mauvaise résolution spatiale. Le problème inverse MEG est dit mal-posé et doit de ce fait être régularisé. La parcellisation du cortex en régions de spécificité fonctionnelle proche constitue une régularisation spatiale pertinente du problème inverse MEG. Nous proposons une méthode de parcellisation du cortex entier à partir de la connectivité anatomique cartographiée par imagerie de diffusion. Au sein de chaque aire d´une préparcellisation, la matrice de corrélation entre les profils de connectivité des sources est partitionnée. La parcellisation obtenue est alors mise à jour en testant la similarité des données de diffusion de part et d´autre des frontières de la préparcellisation. C´est à partir de ce résultat que nous contraignons spatialement le problème inverse MEG. Dans ce contexte, deux méthodes sont développées. La première consiste à partitionner l´espace des sources au regard de la parcellisation. L´activité corticale est alors obtenue sur un ensemble de parcelles. Afin de ne pas forcer les sources à avoir exactement la même intensité au sein d´une parcelle, nous développons une méthode alternative introduisant un nouveau terme de régularisation qui, lorsqu´il est minimisé, tend à ce que les sources d´une même parcelle aient des valeurs de reconstruction proches. Nos méthodes de reconstruction sont testées et validées sur des données simulées et réelles. Une application clinique dans le cadre du traitement de données de sujets épileptiques est également réalisée. / Magnetoencephalography (MEG) is a functional non-invasive modality which provides information on the temporal succession of cognitive processes with an excellent time resolution. Unfortunately, spatial resolution is limited due to the ill-posed nature of the MEG inverse problem for estimating source currents from the electromagnetic measurement. Cortex parcellation into regions sharing functional features constitutes a relevant spatial regularization. We propose a whole cortex parcellation method based on the anatomical connectivity mapped by diffusion MRI. Inside areas of a preparcellation, the correlation matrix between connectivity profiles is clustered. The cortex parcellation is then updated testing the similarity of diffusion data on both sides of pre-parcellation boundaries. MEG inverse problem is constrained from this result. Two methods have been developed. The first one is based on the subdivision of source space regarding the parcellation. The cortical activity is obtained on a set of parcels and its analysis is simplified. Not to force sources to have exactly the same value inside a cortical area, we develop an alternative method. We introduce a new regularization term in the MEG inverse problem which constrain sources in a same region to have close values. Our methods are applied on simulated and real subjects. Clinical application is also performed on epileptic data. Each contribution takes part of a pipeline whose each step is detailed to make our works reproducible.

Spielt Ihr Gehirn Ihre letzten Lebenserfahrungen ab, während Sie sich ausruhen? Eine offene Frage in den Neurowissenschaften ist, welche Ereignisse unser Gehirn wiederholt und gibt es eine Korrelation zwischen der Wiederholung und der Dauer des Ereignisses? In dieser Studie habe ich versucht, dieser Frage nachzugehen, indem ich Magnetenzephalographie-Daten aus einem Experiment zum aktiven Hören verwendet habe. Die Magnetenzephalographie (MEG) ist ein nicht-invasives Neuroimaging-Verfahren, das verwendet wird, um die Gehirnaktivität zu untersuchen und die Gehirndynamik bei Wahrnehmungs- und kognitiven Aufgaben insbesondere in den Bereichen Sprache und Hören zu verstehen. Es zeichnet das in unserem Gehirn erzeugte Magnetfeld auf, um die Gehirnaktivität zu erkennen. Ich baue eine Pipeline für maschinelles Lernen, die einen Teil der Experimentdaten verwendet, um die Klangmuster zu lernen und dann das Vorhandensein von Geräuschen im späteren Teil der Aufnahmen vorhersagt, in denen die Teilnehmer untätig sitzen mussten und kein Ton zugeführt wurde. Das Ziel der Untersuchung der Testwiedergabe von gelernten Klangsequenzen in der Nachhörphase. Ich habe ein Klassifikationsschema verwendet, um Muster zu identifizieren, wenn MEG auf verschiedene Tonsequenzen in der Zeit nach der Aufgabe reagiert. Die Studie kam zu dem Schluss, dass die Lautfolgen über dem theoretischen Zufallsniveau identifiziert und unterschieden werden können und bewies damit die Gültigkeit unseres Klassifikators. Darüber hinaus könnte der Klassifikator die Geräuschsequenzen in der Nachhörzeit mit sehr hoher Wahrscheinlichkeit vorhersagen, aber um die Modellergebnisse über die Nachhörzeit zu validieren, sind mehr Beweise erforderlich. / Does your brain replay your recent life experiences while you are resting? An open question in neuroscience is which events does our brain replay and is there any correlation between the replay and duration of the event? In this study I tried to investigate this question by using Magnetoencephalography data from an active listening experiment. Magnetoencephalography (MEG) is a non-invasive neuroimaging technique used to study the brain activity and understand brain dynamics in perception and cognitive tasks particularly in the fields of speech and hearing. It records the magnetic field generated in our brains to detect the brain activity. I build a machine learning pipeline which uses part of the experiment data to learn the sound patterns and then predicts the presence of sound in the later part of the recordings in which the participants were made to sit idle and no sound was fed. The aim of the study of test replay of learned sound sequences in the post listening period. I have used classification scheme to identify patterns if MEG responses to different sound sequences in the post task period. The study concluded that the sound sequences can be identified and distinguished above theoretical chance level and hence proved the validity of our classifier. Further, the classifier could predict the sound sequences in the post-listening period with very high probability but in order to validate the model results on post listening period, more evidence is needed.

Les oscillations jouent un rôle de premier plan dans la mise en place des réseaux cérébraux sains et pathologiques. En particulier, au niveau clinique, les activités oscillatoires sont d'une grande importance diagnostique en épilepsie. Par ailleurs, les méthodes non-invasives d'électrophysiologie sont particulièrement adaptées pour la compréhension des réseaux cérébraux à grande échelle. Cependant, la majorité des études en épilepsie a été dirigée vers les pointes intercritiques, qui sont des activités transitoires. Une question qui reste donc en suspens est le lien entre les pointes épileptiques et les activités oscillatoires épileptiques. Cette thèse a visé à résoudre deux problématiques complémentaires autour de cette question. La première problématique est la séparation adéquate entre les activités oscillatoires et transitoires. Il s'agit d'une tâche difficile surtout lors d'un grand chevauchement temporel, qui peut résulter en la contamination d'une activité par l'autre. Nous avons évaluée trois méthodes de filtrage : le filtre FIR (méthode classique), la transformé d'ondelette stationnaire et le filtrage parcimonieux par matching pursuit (MP, basé sur un dictionnaire). Sur des simulations, la SWT a donné de très bons résultats pour la reconstruction des transitoires et le MP pour les oscillations ; de plus, les deux méthodes ont donné un faible taux de faux positifs en détection automatique des oscillations. La SWT et le FIR ont donné les meilleurs résultats de filtrage sur les signaux réels, en particulier lors de la localisation de source. / The Oscillatory activities play a leading role in the development of healthy and pathological brain networks. In particular, at the clinical level, the oscillatory activities are of great importance in the diagnostic of epilepsy. In addition, the non-invasive electrophysiology methods are particularly suitable for understanding the large-scale brain networks. However, most studies in epilepsy have been directed to the interictal spikes, which are transitional activities. One issue that remains unresolved is the relationship between epileptic spikes and epileptic oscillatory activities. This thesis resolves two complementary problems. The first one is the suitable separation between the oscillatory and transitory activity, which is quite sensitive to the presence of the overlap in the time-frequency domain. This can lead to a contamination between the activities. We did evaluate three filtering methods: the FIR (classic methods), the stationary wavelet SWT and the parsimonious filter with the matching pursuit MP. The SWT gave good results in the reconstruction of transient activity and the MP in the reconstruction of oscillatory activity both for simulated data; also they provide a low false positive in automatic detection of oscillatory activity. The SWT and FIR gave the best results on real signals especially for source localization. In the simulated data, the MP is optimal since the atoms of the dictionary resembles to the simulated signals, which isn't guaranteed for real signals. The second problem is the comparison between network connectivity of transient and oscillatory activity, as measured in surface recordings (MEG) and invasive recordings SEEG.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2016-08-09T15:19:07Z No. of bitstreams: 2 Dissertação - Geronimo CArlos de Meira Detoni - 2013.pdf: 2178811 bytes, checksum: 38c96596d85db8bf692e4715f8dc4f4f (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-08-10T12:12:58Z (GMT) No. of bitstreams: 2 Dissertação - Geronimo CArlos de Meira Detoni - 2013.pdf: 2178811 bytes, checksum: 38c96596d85db8bf692e4715f8dc4f4f (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-08-10T12:12:58Z (GMT). No. of bitstreams: 2 Dissertação - Geronimo CArlos de Meira Detoni - 2013.pdf: 2178811 bytes, checksum: 38c96596d85db8bf692e4715f8dc4f4f (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2013-09-02 / The unbundling of the Brazilian electric sector in 2004 significantly impacted the sale of power in the country. Since then, a number of measures were progressively implemented, making the market for buying and selling energy competitive and challenging. The generation companies and energy traders had to adapt to new market segmentation, while seeking to retain old customers and attract new contracts. In this context, Eletrobras Eletronorte, a public energy generation and transmission company, defined excellence in management as its strategy by aligning its processes to MEG and to have continuous participation in the PNQ, which evaluates and ranks the best companies in Brazil. The company's relationship with customers and the market is made by means of the criterion "Customers". In the energy market, this criterion expresses the degree of excellence of the marketing process. It is therefore hoped that its score is supposed to represent the measure of success in the pursuit of customer retention and maintenance of the organization. Starting from the historic score obtained by the company in editions of PNQ to the criterion "Customers", the results of satisfaction surveys of the area and financial results recorded during the study period, this work intends to evaluate the impact of having MEG used in the process of commercialization of Eletrobras Eletronorte, considering that the organization was the winner of “World Class” prize from FNQ. / A desverticalização do setor elétrico brasileiro em 2004 impactou a comercialização de energia no país de maneira significativa. Desde então, uma série de medidas foi implementada progressivamente, tornando o mercado de compra e venda de energia competitivo e desafiador. Fez-se necessário que as geradoras e comercializadoras de energia se adaptassem a uma nova segmentação do mercado, buscando ao mesmo tempo a retenção dos clientes antigos e a captação de novos contratos. Nesse contexto, a Eletrobras Eletronorte, empresa pública de geração e transmissão de energia, definiu como estratégia a excelência na gestão, por meio do alinhamento de seus processos ao MEG e da contínua participação no PNQ, que avalia e classifica as melhores empresas do país. A relação da empresa com clientes e mercado é feita por meio do critério “Clientes”. No mercado de energia, esse critério expressa o grau de excelência do processo de comercialização. Espera-se, portanto que sua pontuação represente a medida do sucesso na busca da retenção e manutenção dos clientes da organização. Partindo do histórico da pontuação obtida pela empresa nas edições do PNQ para o critério “Clientes”, dos resultados das pesquisas de satisfação internas da área e dos resultados econômico-financeiros verificados no período do estudo, este trabalho pretende avaliar o impacto da utilização do MEG no processo de Comercialização da Eletrobras Eletronorte, haja vista o cenário de obtenção, pela organização, do prêmio “Classe Mundial” conferido pela FNQ.

Είναι γεγονός ότι τόσο το ευθύ όσο & το αντίστροφο EEG & MEG πρόβλημα έχει αποτελέσει αντικείμενο μελέτης ερευνητών διαφόρων ειδικοτήτων (των Μαθηματικών, της Πληροφορικής, των Φυσικών, των Ηλεκτρολόγων Μηχανικών & φυσικά της Ιατρικής), ήδη από τις δεκαετίες του 1950 & 1960, που αναζητούσαν τρόπους υπολογισμού του ηλεκτρικού & του μαγνητικού πεδίου που παράγουν στο εξωτερικό του εγκεφάλου δεδομένες πηγές που βρίσκονται στο εσωτερικό του (ευθέα προβλήματα EEG - MEG), είτε τρόπους προσδιορισμού των πηγών από μετρήσεις των πεδίων αυτών εξωτερικά του εγκεφάλου (αντίστροφα προβλήματα EEG - MEG). Όλα τα νευρικά σήματα του εγκεφάλου διαδίδονται μέσω μικρών ηλεκτρικών ρευμάτων, τα οποία παράγουν ηλεκτρικό & μαγνητικό πεδίο εντός & εκτός του εγκεφάλου λόγω του συζευγμένου χαρακτήρα του ηλεκτρομαγνητισμού για τα χρονικώς μεταβαλλόμενα φαινόμενα. Τα ηλεκτρικά πεδία & τα μαγνητικά πεδία που παράγονται καταγράφονται από το Ηλεκτροεγκεφαλογράφημα (EEG) & το Μαγνητοεγκεφαλογράφημα (MEG) αντίστοιχα. Στην παρούσα εργασία παρουσιάζουμε τα αποτελέσματα των ερευνών που έχουν καταγραφεί τα τελευταία χρόνια & έχουν δώσει χρήσιμες σχέσεις για το ηλεκτρικό & μαγνητικό πεδίο για τα διάφορα πρότυπα του εγκεφάλου & συγκεκριμένα του σφαιρικού, του σφαιροειδούς & του ελλειψοειδούς προτύπου. Επίσης αναλύεται η αναγωγή προς το σφαιρικό πρότυπο από τα άλλα δύο πρότυπα για να αποδειχθεί ότι τελικά η σφαιρική συμπεριφορά αποκαθίσταται & αναφέρονται ποιες είναι οι σιωπηλές πηγές που δεν συνεισφέρουν στην δημιουργία του μαγνητικού πεδίου στα διάφορα πρότυπα. Τέλος γίνεται αναφορά στο αντίστροφο πρόβλημα του ΗΕΓ που δεν έχει μοναδική λύση & αναφέρουμε πως μπορεί να επιλυθεί αρκεί από τα δεδομένα που έχουμε για το ηλεκτρικό πεδίο να έχει εξαλειφθεί ο θόρυβος με κάποιες μεθόδους που εξηγούνται. Αντικείμενο της εργασίας είναι αρχικά να περιγράψει τη μεθοδολογία για την ανάλυση του ηλεκτρικού πεδίου σε καρτεσιανές συντεταγμένες όσον αφορά το ελλειψοειδές πρότυπο του εγκεφάλου, που είναι & το βέλτιστο δεδομένου ότι ο μέσος εγκέφαλος είναι ελλειψοειδής με άξονες 9, 6.5, 6 cm. Στη συνέχεια δημιουργήσαμε ένα πρόγραμμα σε matlab με σκοπό την καταγραφή των πειραματικών αποτελεσμάτων (που είναι ο προσδιορισμός της πηγής & της θέσης της) & την τρισδιάστατη γραφική παράστασή τους από την παραμετρική ανάλυσή του ηλεκτρικού πεδίου. Στόχος μας ήταν η εύρεση & η σύγκριση των πηγών & των θέσεων που αυτές εντοπίζονται στο εσωτερικό του εγκεφάλου υπό διάφορες συνθήκες που καθορίζονται από την μεταβολή διαφόρων παραμέτρων όπως το πεδίο τιμών των μετρήσεων & των σφαλμάτων τους. / The fact is that both the EEG & MEG problems and the EEG & MEG inverse problems have been studied by researchers of various disciplines (Mathematics, Informatics, Physics, Electrical Engineering & Medicine), since 1950 and 1960, that have been seeking for ways of calculating the electrical and the magnetic field that are produced outside the brain by given sources located within the brain (EEG – MEG problems), or for methods of determining the sources from the measurement of these fields outside the brain (EEG – MEG inverse problems). All the nerve signals of the brain propagate via small electric currents, which produce electric and magnetic fields within and outside the brain due to the coupled nature of electromagnetism for the time-varying phenomena. Electric and magnetic fields, that are generated, are recorded by EEG recording (EEG) & the MEG recording (MEG), respectively. In this work we present the results of surveys in the recent years that have given useful relations for the electric and magnetic field for various models of the brain and in particular the spherical, the spheroid and the ellipsoid model. We also analyzed the reduction in the spherical model from the other two models in order to demonstrate that finally the spherical behavior is restored. Moreover, we listed the silent sources that do not contribute to the creation of the magnetic field in the various models. Finally, we refer to the EEG inverse problem that has not a unique solution and we refer how it can be solved provided that the noise is removed from the data we have - regarding the electric field- with some methods that are explained. The scope of this work is to describe at first the methodology for the analysis of the electric field for the ellipsoid model of the brain (that is the optimal model since the average brain is ellipsoid with axes 9, 6.5, 6 cm) in cartesian coordinates. Then we create a program in matlab in order to record the results, as far as the source and the position of the source are concerned, and their three-dimensional graphics. Our goal is to find and compare the vectors of the sources and the vectors of the positions that these sources are located within the brain, under various conditions determined by changing the various parameters such as the measurement parameters and the errors of the measurement parameters.