pr\u00e9sentent<\/p>\n\n\n\n
Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en :<\/p>\n\n\n\n
le mardi 13 d\u00e9cembre 2022 \u00e0 15h00<\/p>\n\n\n\n
Amphi 4
19 rue Marguerite Perey 91120 Palaiseau<\/p>\n\n\n\n
Lien zoom: Membres du jury :<\/strong><\/p>\n\n\n\n M. J\u00e9rome BOUDY<\/strong>, Professeur, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de th\u00e8se L’\u00e9lectroenc\u00e9phalographie (EEG) est encore consid\u00e9r\u00e9e de nos jours comme une technique de neuroimagerie tr\u00e8s utile dans les applications cliniques, adapt\u00e9e aux patients souffrant de troubles cognitifs et physiques, ainsi qu’aux tests \u00e0 grande \u00e9chelle. L’EEG est une technologie non invasive, peu co\u00fbteuse et facilement accessible. Elle se caract\u00e9rise par une haute r\u00e9solution temporelle, ce qui est crucial pour le suivi de la dynamique c\u00e9r\u00e9brale. Plusieurs travaux dans la litt\u00e9rature ont exploit\u00e9 l’EEG pour \u00e9tudier les alt\u00e9rations de l’activit\u00e9 c\u00e9r\u00e9brale li\u00e9es aux maladies neurod\u00e9g\u00e9n\u00e9ratives, notamment la maladie d’Alzheimer (MA). La MA est une maladie neurod\u00e9g\u00e9n\u00e9rative chronique qui entra\u00eene un d\u00e9clin progressif des fonctions cognitives, ainsi que des troubles du comportement et une perte insidieuse d’autonomie au quotidien. En l’absence de traitements curatifs, nous observons un int\u00e9r\u00eat croissant \u00e0 la caract\u00e9risation de l\u2019activit\u00e9 c\u00e9r\u00e9brale aux stades pr\u00e9coces de la maladie. Le stade pr\u00e9clinique de la MA est asymptomatique, mais les l\u00e9sions c\u00e9r\u00e9brales dues \u00e0 la MA sont pr\u00e9sentes. A ce stade, on parle de troubles cognitifs subjectifs (subjective cognitive impairments, SCI). Au stade prodromal, les patients atteints de troubles cognitifs l\u00e9gers (mild cognitive impairment, MCI) pr\u00e9sentent des troubles de la m\u00e9moire mesurables, mais leur capacit\u00e9 fonctionnelle est maintenue. Les patients atteints de troubles subjectifs ou l\u00e9gers pr\u00e9sentent un risque \u00e9lev\u00e9 de d\u00e9velopper la MA. Cette th\u00e8se s\u2019int\u00e9resse au diagnostic pr\u00e9coce de la MA aux stades pr\u00e9clinique et prodromal en utilisant l’EEG au repos, et aborde l’analyse des r\u00e9seaux c\u00e9r\u00e9braux en \u00e9tudiant la connectivit\u00e9 fonctionnelle \u00e0 diff\u00e9rents stades cliniques du d\u00e9clin cognitif (SCI, MCI et MA au stade l\u00e9ger). Pour cela, nous avons men\u00e9 une \u00e9tude r\u00e9trospective en exploitant une base de donn\u00e9es clinique qui contient des signaux EEG enregistr\u00e9s en conditions r\u00e9elles. En premier lieu, nous avons propos\u00e9 d’exploiter une mesure d’entropie, appel\u00e9e \u00ab\u00a0Epoch-based Entropy\u00a0\u00bb (EpEn), pour quantifier la connectivit\u00e9 fonctionnelle. Cette mesure repose sur une mod\u00e9lisation statistique fine des signaux EEG avec des mod\u00e8les de Markov cach\u00e9s. Cette mesure caract\u00e9rise les changements spatio-temporels des signaux EEG en quantifiant le contenu d\u2019information dans les signaux au niveau temporel et spatial. Par la suite, nous avons effectu\u00e9 une analyse topologique du r\u00e9seau c\u00e9r\u00e9bral cortical de mani\u00e8re diff\u00e9rentielle, en exploitant la th\u00e9orie des graphes. La contribution de notre travail est double. En effet, il s’agit du premier travail qui : (i) aborde l’analyse du r\u00e9seau c\u00e9r\u00e9bral chez les patients ayant des troubles subjectifs, des troubles l\u00e9gers et la MA au stade l\u00e9ger, et (ii) combine la mesure d\u2019entropie \u00e0 la th\u00e9orie des graphes puisque nous avons d\u00e9montr\u00e9 son efficacit\u00e9 \u00e0 quantifier les changements spatio-temporels li\u00e9s \u00e0 la MA. Dans cette th\u00e8se, nous avons aussi abord\u00e9 le probl\u00e8me de la grande quantit\u00e9 d’information extraite des signaux EEG, analys\u00e9s sur plusieurs bandes de fr\u00e9quences (delta, theta, alpha, beta), plusieurs \u00e9lectrodes, et plusieurs \u00e9chelles de densit\u00e9 de r\u00e9seau (seuillages multiples des graphes). Par cons\u00e9quent, une autre contribution \u00e0 ce travail de th\u00e8se concerne l’extraction de marqueurs EEG les plus pertinents pour discriminer automatiquement les trois groupes de patients. Ainsi, nous avons propos\u00e9 une m\u00e9thode hi\u00e9rarchique pour l’analyse des signaux EEG, permettant d’identifier les descripteurs les plus pertinents \u00e0 partir d’une grande quantit\u00e9 d’information issue d\u2019une seule mesure de connectivit\u00e9 fonctionnelle. Enfin, nous avons \u00e9valu\u00e9 la corr\u00e9lation entre les marqueurs num\u00e9riques extraits des signaux EEG et les biomarqueurs cliniques \u00e0 notre disposition (MMSE, RL\/RI-16, BREF).<\/p>\n\n\n\n Electroencephalography (EEG) is still considered nowadays as a convenient neuroimaging technique in clinical applications, suitable for cognitively and physically disabled patients, as well as for serial tests. In fact, EEG is a non-invasive, cost-effective, and mobile technology. It is characterized by a high temporal resolution, which is crucial for the analysis of fast brain functional dynamics. There is a rich literature addressing the use of EEG to investigate brain activity alterations due to neurodegenerative diseases, especially Alzheimer’s disease (AD). AD is a chronic neurodegenerative disease that leads to progressive decline of cognitive functions along with behavioral disorders and insidious loss of autonomy in daily living activities. We observe a growing interest in the earlier stages of the disease since curative treatments are still lacking. The preclinical stage of AD is asymptomatic, but the brain lesions due to AD are present. At this phase, the term of subjective cognitive impairment (SCI) has been recently defined. In the prodromal stage, mild cognitive impairment (MCI) patients show measurable memory impairments but their functional capacity is maintained. SCI and MCI patients are at high risk of developing AD. This thesis investigates the early diagnosis of AD at preclinical and prodromal stages using resting-state EEG, and addresses brain network analysis by studying the functional connectivity over several clinical stages of cognitive decline (SCI, MCI and Mild AD). To this end, we conduct a retrospective study using a clinical database that contains EEG signals recorded in real-life conditions. We first propose to exploit an entropy measure, termed \u201cepoch-based entropy\u201d (EpEn), as a measure of functional connectivity, that relies on a refined statistical modeling of EEG signals based on Hidden Markov Models. This measure characterizes the spatiotemporal changes in EEG signals by quantifying the information content of EEG signals, both at the time and spatial levels. Furthermore, we conduct a topological brain network analysis over the three stages of cognitive decline by employing the Graph Theory. The novelty of our work is twofold. Actually, this is the first work that: (i) addresses EEG brain network analysis over SCI, MCI and Mild AD stages simultaneously, and (ii) combines EpEn to Graph Theory since we have shown its effectiveness in quantifying the complete spatiotemporal alteration due to AD. In this thesis, we decided to invest the largest amount of EEG information for brain network analysis, by exploiting several frequency ranges (delta, theta, alpha, beta), several electrodes locations (instead of regions), and several network density scales (multiple graph thresholding). Therefore, another issue tackled in this thesis concerns the identification of relevant EEG markers to discriminate automatically between SCI, MCI and AD patients in the context of graph analysis framework. To this end, we propose an automatic hierarchical method for EEG analysis, which allows the extraction of relevant markers from large amount of information based on a single EEG connectivity measure. Finally, we also assess the correlation between the relevant EEG markers and the clinical biomarkers at our disposal (MMSE, RL\/RI-16, BREF).<\/p>\n","protected":false},"excerpt":{"rendered":" L’Ecole doctorale : Ecole Doctorale de l’Institut Polytechnique de Pariset le Laboratoire de recherche SAMOVAR – Services r\u00e9partis, Architectures, MOd\u00e9lisation, Validation, Administration des R\u00e9seaux pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Monsieur MAJD ABAZID Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : Math\u00e9matiques 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https:\/\/telecom-paris.zoom.us\/j\/98403568641?pwd=STUwSndobUNDSTg5bFFsbWUvYS85dz09<\/a>
ID de r\u00e9union : 984 0356 8641<\/a>
Code secret : 495693<\/p>\n\n\n\n
M. Sylvain CHEVALLIER<\/strong>, Professeur des universit\u00e9s, Universit\u00e9 Paris-Saclay, FRANCE – Rapporteur
M. Jordi SOL\u00e9-CASALS<\/strong>, Professor, Universit\u00e9 de Vic, ESPAGNE – Rapporteur
Mme Nesma HOUMANI<\/strong>, Ma\u00eetresse de conf\u00e9rences, Telecom SudParis, FRANCE – Co-encadrante de th\u00e8se
Mme Kiyoka KINUGAWA BOURRON<\/strong>, Professeure des universit\u00e9s – praticienne hospitali\u00e8re, Sorbonne Universit\u00e9\/CNRS, UMR 8256 Biological Adaptation and Aging, FRANCE – Examinatrice
M. G\u00e9rard DRAY<\/strong>, Professeur, IMT Mines Ales, FRANCE – Examinateur<\/p>\n\n\n\n
R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\n
Abstract : \u00ab\u00a0Topological study of the brain functional organization at the early stages of Alzheimer’s disease using electroencephalography\u00a0\u00bb<\/strong><\/p>\n\n\n\n