Cette thèse est motivée par l’analyse des données issues de l’imagerie par résonance magnétique fonctionnelle (IRMf). La nécessité de développer des méthodes capables d’extraire la structure sous-jacente des données d’IRMf constitue un challenge mathématique attractif. A cet égard, nous modélisons les réseaux de connectivité cérébrale par un graphe et nous étudions des procédures permettant d’inférer ce graphe.Plus précisément, nous nous intéressons à l’inférence de la structure d’un modèle graphique non orienté par une procédure de test multiple. Nous considérons deux types de structure, à savoir celle induite par la corrélation et celle induite par la corrélation partielle entre les variables aléatoires. Les statistiques de tests basées sur ces deux dernières mesures sont connues pour présenter une forte dépendance et nous les supposerons être asymptotiquement gaussiennes. Dans ce contexte, nous analysons plusieurs procédures de test multiple permettant un contrôle des arêtes incluses à tort dans le graphe inféré.Dans un premier temps, nous questionnons théoriquement le contrôle du False Discovery Rate (FDR) de la procédure de Benjamini et Hochberg dans un cadre gaussien pour des statistiques de test non nécessairement positivement dépendantes. Nous interrogeons par suite le contrôle du FDR et du Family Wise Error Rate (FWER) dans un cadre gaussien asymptotique. Nous présentons plusieurs procédures de test multiple, adaptées aux tests de corrélations (resp. corrélations partielles), qui contrôlent asymptotiquement le FWER. Nous proposons de plus quelques pistes théoriques relatives au contrôle asymptotique du FDR.Dans un second temps, nous illustrons les propriétés des procédures contrôlant asymptotiquement le FWER à travers une étude sur simulation pour des tests basés sur la corrélation. Nous concluons finalement par l’extraction de réseaux de connectivité cérébrale sur données réelles. / This thesis is motivated by the analysis of the functional magnetic resonance imaging (fMRI). The need for methods to build such structures from fMRI data gives rise to exciting new challenges for mathematics. In this regards, the brain connectivity networks are modelized by a graph and we study some procedures that allow us to infer this graph.More precisely, we investigate the problem of the inference of the structure of an undirected graphical model by a multiple testing procedure. The structure induced by both the correlation and the partial correlation are considered. The statistical tests based on the latter are known to be highly dependent and we assume that they have an asymptotic Gaussian distribution. Within this framework, we study some multiple testing procedures that allow a control of false edges included in the inferred graph.First, we theoretically examine the False Discovery Rate (FDR) control of Benjamini and Hochberg’s procedure in Gaussian setting for non necessary positive dependent statistical tests. Then, we explore both the FDR and the Family Wise Error Rate (FWER) control in asymptotic Gaussian setting. We present some multiple testing procedures, well-suited for correlation (resp. partial correlation) tests, which provide an asymptotic control of the FWER. Furthermore, some first theoretical results regarding asymptotic FDR control are established.Second, the properties of the multiple testing procedures that asymptotically control the FWER are illustrated on a simulation study, for statistical tests based on correlation. We finally conclude with the extraction of cerebral connectivity networks on real data set.

O cérebro humano é considerado uma rede complexa em termos estruturais e funcionais em diferentes escalas. A caracterização da arquitetura desta rede pode ser considerada uma importante ferramenta no auxílio ao estudo de diferentes doenças neurodegenerativas. No presente estudo propusemos um software desenvolvido em JAVA para investigar esta arquitetura com base na correlação estatística de características morfológicas entre diferentes regiões do córtex. Foram utilizados dados de espessura cortical obtidos a partir de imagens de ressonância magnética de 191 indivíduos saudáveis e 93 pacientes com epilepsia. Foi proposto um modelo não linear para considerar o efeito da idade na espessura cortical com identificação de duas etapas: amadurecimento e envelhecimento. Os pacientes, quando comparados aos controles, apresentaram uma redução significativa na espessura cortical fundamentalmente nas regiões para-central, pericalcarina e do pré-cuneo no hemisfério direito. Esta diminuição também se manifestou ao longo da idade, com uma maior taxa de queda na região parahipocampal direita. Considerando a conectividade anatômica aqui calculada, o grupo de pacientes evidenciou alterações em 31\\% das possíveis conexões e de forma difusa. Adicionalmente, nas redes cerebrais dos pacientes houve uma diminuição de 15\\% no comprimento médio do caminho e no coeficiente de agrupamento. Aplicando-se um algoritmo de agrupamento, foram detectadas três comunidades para os indivíduos saudáveis e seis comunidades para os pacientes, confirmando uma quebra de organização estrutural neste ultimo grupo. Com este software esperamos trazer à comunidade mais uma ferramenta para análise das conexões cerebrais e suas modificações em determinadas patologias, contribuindo com seu entendimento e possível diagnóstico. / The human brain can be characterized as a complex network structurally and functionally in different levels. The description of the architecture of this network can be considered an important tool in understanding different neurodegenerative diseases. In the present study, we developed a software in JAVA to investigate this architecture based on statistical correlation of morphological characteristics between different cortex areas. It was used a database of cortical thickness obtained from magnetic resonance images of 191 healthy subjects and 93 patients with epilepsy. It was implemented a non-linear model to consider the effect of age in cortical thickness with identification of 2 stages: maturation and aging. The patients, when compared to healthy subjects, showed a significant reduction in cortical thickness, particularly at the areas precentral, pericalcarine and pré-cuneus of right hemisphere. This decrease also could be noted through the age, with a higher decrease rate at the right parahipocampal area. Considering the anatomical connectivity calculated, the patients group showed diffuse changes in 31\\% of the possible connections. Furthermore, in the patients brain network it was found a decrease of 15\\% in the characteristic path length and clustering coefficient. By applying a clustering algorithm, 3 clusters were detected in the healthy subjects and 6 clusters in the patients, confirming a breakdown of the structural organization in this last group. With our software we hope to bring to the community another tool to improve the brain connectivity analysis and its modifications in some pathologies, contributing with its understanding and possible diagnosis.

Orientador: Gabriela Castellano / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin / Made available in DSpace on 2018-08-30T20:31:19Z (GMT). No. of bitstreams: 1 TapiaHerrera_LuisCarlos1982-_D.pdf: 17368597 bytes, checksum: b04bfdc96a80f7bba2cdca7390a9d09e (MD5) Previous issue date: 2016 / Resumo: Os neurônios são elementos que no cérebro trabalham em grupo e de forma organizada. A técnica de ressonância magnética funcional (fMRI) permite identificar redes corticais e subcorticais do cérebro quando ele desenvolve atividades cognitivas motoras ou perceptivas. No entanto, redes nomeadas de redes em estado de repouso, estão presentes em ausência de tarefas específicas. Alguns estudos modelaram redes funcionais do cérebro com a ajuda da teoria de grafos. Um dos objetivos deste trabalho foi analisar, utilizando teoria de grafos, dados funcionais do cérebro coletados com a técnica de fMRI, de 10 voluntários saudáveis, que participaram de dois protocolos: uma aquisição em estado de repouso e outra durante uma tarefa de produção de palavras. Outro objetivo do trabalho foi testar duas métricas matemáticas (correlação de Pearson e informação mútua), para determinar quais delas conseguem captar melhor diferenças entre as duas condições mencionadas. Também se objetivou comparar parâmetros termodinâmicos das redes de repouso obtidas por meio dos dados reais com os de redes simuladas computacionalmente via modelo de Ising. Finalmente, um último objetivo foi explorar os dados para ver que informação poderia ser obtida a partir dos mesmos, sem uso prévio de modelos sobre as tarefas realizadas. Utilizando a teoria de grafos, achamos diferenças entre as redes nas condições de repouso e de produção de palavras para os parâmetros grau médio e coeficiente de cluster. Adicionalmente foram comparadas as redes dos hemisférios direito e esquerdo nas redes geradas na condição de produção de palavras, e achamos que o grau médio das redes pode predizer a lateralização (dominância hemisférica para linguagem), também achada com análises padrões de fMRI. Relativo às métricas matemáticas, a correlação de Pearson e a informação mútua foram comparadas para determinar qual destas métricas captura melhor a similaridade ou sincronia entre duas séries temporais que contêm atividade hemodinâmica do cérebro. Concluímos que a correlação linear é uma medida capaz de caracterizar de forma satisfatória a sincronia entre duas séries desse tipo. Simulações computacionais do modelo de Ising foram desenvolvidas para posteriormente criar redes funcionais em três regimes diferentes: crítico, subcrítico e supercrítico. Esta abordagem do estado de repouso foi examinada em trabalhos prévios, e foi concluído que o cérebro como sistema dinâmico possui uma maior semelhança com o sistema simulado no regime crítico. Finalmente, uma metodologia independente de modelo foi implementada para detectar áreas ativas do cérebro em tarefas dirigidas. Esta metodologia foi testada nos dados na condição de produção de palavras, permitindo identificar as áreas envolvidas na execução da tarefa / Abstract: Neuronal elements in the brain are not isolated, they work together and work in an organized way. The functional magnetic resonance imaging (fMRI) technique allows identifying cortical networks when the brain develops a task. However, resting state brain networks are present in the absence of any task. Some studies have modeled the brain networks architecture with aid of graph theory. One of the main aims of this work was the analysis of resting state and language task fMRI data sets, of ten healthy subjects, using graph theory. In order to study the linear and nonlinear relationships between time series of cortical areas of the brain, two metrics were compared the Pearson correlation and the mutual information. Also, graphs parameters built from resting state data and graph parameters built using simulations of the Ising model were compared. Finally, we developed a methodology to study the time series of differents regions of the brain in order to obtain information of the task without using predefined models of the brain activity. We found differences in the mean degree and the cluster coefficient of the network between the two conditions. In addition, we compared the networks corresponding to the left and right hemispheres during the language task, and found that the mean degree of these networks can predict the language lateralization found with standard fMRI analysis in most cases. The mean degree of the network and the cluster coefficient shows differences for the two conditions. Relative to the comparison between the Pearson correlation and the mutual information, we conclude that the linear correlation is an efficient metric to characterize the synchrony between the haemodynamic time series of the brain. Computational simulations of the Ising model for three different phases were developed: in critical, subcritical and supercritical phases. This comparison was presented in a previous work, and it was concluded that the brain as a dynamical system has remarkable similarities with the computational model in the critical phase. Relatively to the model independent methodology developed, it was possible to identify brain areas engaged with the word production task / Doutorado / Física / Doutor em Ciências / 157356/2011-6 / CNPQ

El cerebro está constituido por numerosos elementos que se encuentran interconectados de forma masiva y organizados en módulos que forman redes jerárquicas. Ciertas patologías cerebrales, como la enfermedad de Alzheimer y el trastorno por consumo de alcohol, se consideran el resultado de efectos en cascada que alteran la conectividad cerebral. La presente tesis tiene como objetivo principal la aplicación de las técnicas de análisis de la ciencia de redes para el estudio de las redes estructurales y funcionales en el cerebro, tanto en un estado control como en un estado patológico. Así, en el primer estudio de la presente tesis se examina la relación entre la conectividad estructural y funcional en la corteza cerebral de la rata. Se lleva a cabo un análisis comparativo entre las conexiones estructurales en la corteza cerebral de la rata y los valores de correlación calculados sobre las mismas regiones. La información acerca de la conectividad estructural se ha obtenido a partir de estudios previos, mientras que la conectividad funcional se ha calculado a partir de imágenes de resonancia magnética funcional. Determinadas propiedades topológicas, y extraídas de la conectividad estructural, se relacionan con la organización modular de las redes funcionales en estado de reposo. Los resultados obtenidos en este primer estudio demuestran que la conectividad estructural y funcional cortical están altamente relacionadas entre sí. Estudios recientes sugieren que el origen de la enfermedad de Alzheimer reside en un mecanismo en el cual depósitos de ovillos neurofibrilares y placas de beta-amiloide se acumulan en ciertas regiones cerebrales, y tienen la capacidad de diseminarse por el cerebro actuando como priones. En el segundo estudio de la presente tesis se investiga si las redes estructurales que se generan con la técnica de resonancia magnética ponderada en difusión podrían ser de utilidad para el diagnóstico de la pre-demencia causada por la enfermedad de Alzheimer. Mediante el uso de imágenes procedentes de la base de datos ADNI, se aplican técnicas de aprendizaje máquina con el fin de identificar medidas de centralidad que se encuentran alteradas en la demencia. En la segunda parte del estudio, se utilizan imágenes procedentes de la base de datos NKI para construir un modelo matemático que simule el proceso de envejecimiento normal, así como otro modelo que simule el proceso de desarrollo de la enfermedad. Con este modelado matemático, se pretende estimar la etapa más temprana que está asociada con la demencia. Los resultados obtenidos de las simulaciones sugieren que en etapas tempranas de la enfermedad de Alzheimer se producen alteraciones estructurales relacionados con la demencia. La cuantificación de la relación estadística entre las señales BOLD de diferentes regiones puede informar sobre el estado funcional cerebral característico de enfermedades neurológicas y psiquiátricas. En el tercer estudio de la presente tesis se estudian las alteraciones en la conectividad funcional que tienen lugar en ratas dependientes del consumo de alcohol cuando se encuentran en estado de reposo. Para ello, se ha aplicado el método NBS. El análisis de este modelo de rata revela diferencias estadísticamente significativas en una subred de regiones cerebrales que están implicadas en comportamientos adictivos. Por lo tanto, estas estructuras cerebrales podrían ser el foco de posibles dianas terapéuticas. La tesis aporta tres innovadoras contribuciones para entender la conectividad cerebral bajo la perspectiva de la ciencia de redes, tanto en un estado control como en un estado patológico. Los resultados destacan que los modelos basados en las redes cerebrales permiten esclarecer la relación entre la estructura y la función en el cerebro. Y quizás más importante, esta perspectiva de red tiene aplicaciones que se podrían trasladar a la práctica clínica. / The brain is composed of massively connected elements arranged into modules that form hierarchical networks. Experimental evidence reveals a well-defined connectivity design, characterized by the presence of strategically connected core nodes that critically contribute to resilience and maintain stability in interacting brain networks. Certain brain pathologies, such as Alzheimer's disease and alcohol use disorder, are thought to be a consequence of cascading maladaptive processes that alter normal connectivity. These findings have greatly contributed to the development of network neuroscience to understand the macroscopic organization of the brain. This thesis focuses on the application of network science tools to investigate structural and functional brain networks in health and disease. To accomplish this goal, three specific studies are conducted using human and rodent data recorded with MRI and tracing technologies. In the first study, we examine the relationship between structural and functional connectivity in the rat cortical network. Using a detailed cortical structural matrix obtained from published histological tracing data, we first compare structural connections in the rat cortex with their corresponding spontaneous correlations extracted empirically from fMRI data. We then show the results of this comparison by relating structural properties of brain connectivity to the functional modularity of resting-state networks. Specifically, we study link reciprocity in both intra- and inter-modular connections as well as the structural motif frequency spectrum within functionally defined modules. Overall, our results provide further evidence that structural connectivity is coupled to and shapes functional connectivity in cortical networks. The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting pahtogenic seeding and subsequent prion-like spreading processes of neurofibrillary tangles and amyloid plaques. In the second study of this thesis, we investigate whether structural brain networks as measured with dMRI could serve as a complementary diagnostic tool in prodromal dementia. Using imaging data from the ADNI database, we first aim to implement machine learning techniques to extract centrality features that are altered in Alzheimer's dementia. We then incorporate data from the NKI database and create dynamical models of normal aging and Alzheimer's disease to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Our model results suggest that changes associated with dementia begin to manifest structurally at early stages. Statistical dependence measures computed between BOLD signals can inform about brain functional states in studies of neurological and psychiatric disorders. Furthermore, its non-invasive nature allows comparable measurements between clinical and animal studies, providing excellent translational capabilities. In the last study, we apply the NBS method to investigate alterations in the resting-state functional connectivity of the rat brain in a PD state, an established animal model of clinical relevant features in alcoholism. The analysis reveal statistically significant differences in a connected subnetwork of structures with known relevance for addictive behaviors, hence suggesting potential targets for therapy. This thesis provides three novel contributions to understand the healthy and pathological brain connectivity under the perspective of network science. The results obtained in this thesis underscore that brain network models offer further insights into the structure-function coupling in the brain. More importantly, this network perspective provides potential applications for the diagnosis and treatment of neurological and psychiatric disorders. / El cervell està constituït per nombrosos elements que es troben interconnectats de forma massiva i organitzats en mòduls que formen xarxes jeràrquiques. Certes patologies cerebrals, com la malaltia d'Alzheimer i el trastorn per consum d'alcohol, es consideren el resultat d'efectes en cascada que alteren la connectivitat cerebral. La present tesi té com a objectiu principal l'aplicació de les tècniques d'anàlisi de la ciència de xarxes per a l'estudi de les xarxes estructurals i funcionals en el cervell, tant en un estat control com en un estat patològic. Així, en el primer estudi de la present tesi s'examina la relació entre la connectivitat estructural i funcional en l'escorça cerebral de la rata. Es du a terme una anàlisi comparativa entre les connexions estructurals en l'escorça cerebral de la rata i els valors de correlació calculats sobre les mateixes regions. La informació sobre la connectivitat estructural s'ha obtingut a partir d'estudis previs, mentre que la connectivitat funcional s'ha calculat a partir d'imatges de ressonància magnètica funcional. Determinades propietats topològiques, i extretes de la connectivitat estructural, es relacionen amb l'organització modular de les xarxes funcionals en estat de repòs. Els resultats obtinguts en este primer estudi demostren que la connectivitat estructural i funcional cortical estan altament relacionades entre si. Estudis recents suggereixen que l'origen de la malaltia d'Alzheimer resideix en un mecanisme en el qual depòsits d'ovulets neurofibrilars i plaques de beta- miloide s'acumulen en certes regions cerebrals, i tenen la capacitat de disseminar-se pel cervell actuant com a prions. En el segon estudi de la present tesi s'investiga si les xarxes estructurals que es generen amb la tècnica de la imatge per ressonància magnètica ponderada en difusió podrien ser d'utilitat per al diagnòstic de la predemència causada per la malaltia d'Alzheimer. Per mitjà de l'ús d'imatges procedents de la base de dades ADNI, s'apliquen tècniques d'aprenentatge màquina a fi d'identificar mesures de centralitat que es troben alterades en la demència. En la segona part de l'estudi, s'utilitzen imatges procedents de la base de dades NKI per a construir un model matemàtic que simule el procés d'envelliment normal, així com un altre model que simule el procés de desenrotllament de la malaltia. Amb este modelatge matemàtic, es pretén estimar l'etapa més primerenca que està associada amb la demència. Els resultats obtinguts de les simulacions suggereixen que en etapes primerenques de la malaltia d'Alzheimer es produeixen alteracions estructurals relacionats amb la demència. La quantificació de la relació estadística entre els senyals BOLD de diferents regions pot informar sobre l'estat funcional cerebral característic de malalties neurològiques i psiquiàtriques. A més, a causa de la seua naturalesa no invasiva, és possible comparar els resultats obtinguts entre estudis clínics i estudis amb animals d'experimentació. En el tercer estudi de la present tesi s'estudien les alteracions en la connectivitat funcional que tenen lloc en rates dependents del consum d'alcohol quan es troben en estat de repòs. Per a realitzar-ho, s'ha aplicat el mètode NBS. L'anàlisi d'aquest model de rata revela diferències estadísticament significatives en una subxarxa de regions cerebrals que estan implicades en comportaments addictius. Per tant, estes estructures cerebrals podrien ser el focus de possibles dianes terapèutiques. La tesi aporta tres innovadores contribucions per a entendre la connectivitat cerebral davall la perspectiva de la ciència de xarxes, tant en un estat control com en un estat patològic. Els resultats destaquen que els models basats en les xarxes cerebrals permeten aclarir la relació entre l'estructura i la funció en el cervell. I potser més important, esta perspectiva de xarxa té aplicacions que es podrien traslladar a la pràcti / Díaz Parra, A. (2018). A network science approach of the macroscopic organization of the brain: analysis of structural and functional brain networks in health and disease [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/106966 / TESIS

El balance del cerebro se altera a nivel estructural y funcional por el consumo de alcohol y puede causar trastornos por consumo de alcohol (TCA). El objetivo de esta Tesis Doctoral fue investigar los efectos del consumo crónico y excesivo de alcohol en el cerebro desde una perspectiva funcional y estructural, mediante análisis de imágenes multimodales de resonancia magnética (RM). Realizamos tres estudios con objetivos específicos: i) Para entender cómo las neuroadaptaciones desencadenadas por el consumo de alcohol se ven reflejadas en la conectividad cerebral funcional entre redes cerebrales, así como en la actividad cerebral, realizamos estudios en ratas msP en condiciones de control y tras un mes con acceso a alcohol. Para cada sujeto se obtuvieron las señales específicas de sus redes cerebrales tras aplicar análisis probabilístico de componentes independientes y regresión espacial a las imágenes funcionales de RM en estado de reposo (RMf-er). Después, estimamos la conectividad cerebral en estado de reposo mediante correlación parcial regularizada. Para una lectura de la actividad neuronal realizamos un experimento con imágenes de RM realzadas con manganeso. En la condición de alcohol encontramos hipoconectividades entre la red visual y las redes estriatal y sensorial; todas con incrementos en actividad. Por el contrario, hubo hiperconectividades entre tres pares de redes cerebrales: 1) red prefrontal cingulada media y red estriatal, 2) red sensorial y red parietal de asociación y 3) red motora-retroesplenial y red sensorial, siendo la red parietal de asociación la única red sin incremento de actividad. Estos resultados indican que las redes cerebrales ya se alteran desde una fase temprana de consumo continuo y prolongado de alcohol, disminuyendo el control ejecutivo y la flexibilidad comportamental. ii) Para comparar el volumen de materia gris (MG) cortical entre 34 controles sanos y 35 pacientes con dependencia al alcohol, desintoxicados y en abstinencia de 1 a 5 semanas, realizamos un análisis de morfometría basado en vóxel. Las principales estructuras cuyo volumen de MG disminuyó en los sujetos en abstinencia fueron el giro precentral (GPreC), el giro postcentral (GPostC), la corteza motora suplementaria (CMS), el giro frontal medio (GFM), el precúneo (PCUN) y el lóbulo parietal superior (LPS). Disminuciones de MG en el volumen de esas áreas pueden dar lugar a cambios en el control de los movimientos (GPreC y CMS), en el procesamiento de información táctil y propioceptiva (GPostC), personalidad, previsión (GFM), reconocimiento sensorial, entendimiento del lenguaje, orientación (PCUN) y reconocimiento de objetos a través de su forma (LPS). iii) Caracterizar estados cerebrales dinámicos en señales de RMf mediante una metodología basada en un modelo oculto de Markov (HMM en inglés)-Gaussiano en un paradigma con diseño de bloques, junto con distintas señales temporales de múltiples redes: componentes independientes y modos funcionales probabilísticos (PFMs en inglés) en 14 sujetos sanos. Cuatro condiciones experimentales formaron el paradigma de bloques: reposo, visual, motora y visual-motora. Mediante la aplicación de HMM-Gaussiano a los PFMs pudimos caracterizar cuatro estados cerebrales a partir de la actividad media de cada PFM. Los cuatro mapas espaciales obtenidos fueron llamados HMM-reposo, HMM-visual, HMM-motor y HMM-RND (red neuronal por defecto). HMM-RND apareció una vez el estado de tarea se había estabilizado. En un futuro cercano se espera obtener estados cerebrales en nuestros datos de RMf-er en ratas, para comparar dinámicamente el comportamiento de las redes cerebrales como un biomarcador de TCA. En conclusión, las técnicas de neuroimagen aplicadas en imagen de RM multimodal para estimar la conectividad cerebral en estado de reposo, la actividad cerebral y el volumen de materia gris han permitido avanzar en el entendimiento de los mecanismos homeostático / La ingesta d'alcohol altera el balanç del cervell a nivell estructural i funcional i pot causar trastorns per consum d' alcohol (TCA). L'objectiu d'aquesta Tesi Doctoral fou estudiar els efectes en el cervell del consum crònic i excessiu d'alcohol, des d'un punt de vista funcional i estructural i per mitjà d'anàlisi d'imatges de ressonància magnètica (RM). Vam realitzar tres anàlisis amb objectius específics: i) Per a entendre com les neuroadaptacions desencadenades pel consum d'alcohol es veuen reflectides en la connectivitat cerebral funcional entre xarxes cerebrals, així com en l'activitat cerebral, vam realitzar estudis en rates msP en les condicions de control i després d'un mes amb accés a alcohol. Per a cada subjecte vam obtindre els senyals de les xarxes cerebrals tras aplicar a les imatges funcionals de RM en estat de repòs una anàlisi probabilística de components independents i regressió espacial. Després, estimàrem la connectivitat cerebral en estat de repòs per mitjà de correlació parcial regularitzada. Per a una lectura de l'activitat cerebral vam adquirir imatges de RM realçades amb manganés. En la condició d'alcohol vam trobar hipoconnectivitats entre la xarxa visual i les xarxes estriatal i sensorial, totes amb increments en activitat. Al contrari, va haver-hi hiperconnectivitats entre tres parells de xarxes cerebrals: 1) xarxa prefrontal cingulada mitja i xarxa estriatal, 2) xarxa sensorial i xarxa parietal d'associació i 3) xarxa motora-retroesplenial i xarxa sensorial, sent la xarxa parietal d'associació l'única xarxa sense increment d'activitat. Aquests resultats indiquen que les xarxes cerebrals ja s'alteren des d'una fase primerenca caracteritzada per consum continu i prolongat d'alcohol, disminuint el control executiu i la flexibilitat comportamental. ii) Per a comparar el volum de MG cortical entre 34 controls sans i 35 pacients amb dependència a l'alcohol, desintoxicats i en abstinència de 1 a 5 setmanes vam emprar anàlisi de morfometria basada en vòxel. Les principals estructures on el volum de MG va disminuir en els subjectes en abstinència van ser el gir precentral (GPreC), el gir postcentral (GPostC), la corteça motora suplementària (CMS), el gir frontal mig (GFM), el precuni (PCUN) i el lòbul parietal superior (LPS). Les disminucions de MG en eixes àrees poden donar lloc a canvis en el control dels moviments (GPreC i CMS), en el processament d'informació tàctil i propioceptiva (GPostC), personalitat, previsió (GFM), reconeixement sensorial, enteniment del llenguatge, orientació (PCUN) i reconeixement d'objectes a través de la seua forma (LPS). iii) Caracterització de les dinàmiques temporals del cervell com a diferents estats cerebrals, en senyals de RMf mitjançant una metodologia basada en un model ocult de Markov (HMM en anglès)-Gaussià en imatges de RMf, junt amb dos tipus de senyals temporals de múltiples xarxes cerebrals: components independents i modes funcionals probabilístics (PFMs en anglès) en 14 subjectes sans. Quatre condicions experimentals van formar el paradigma de blocs: repòs, visual, motora i visual-motora. HMM-Gaussià aplicat als PFMs (senyals de RM funcional de xarxes cerebrals) va permetre la millor caracterització dels quatre estats cerebrals a partir de l'activitat mitjana de cada PFM. Els quatre mapes espacials obtinguts van ser anomenats HMM-repòs, HMM-visual, HMM-motor i HMM-XND (xarxa neuronal per defecte). HMM-XND va aparèixer una vegada una tasca estava estabilitzada. En un futur pròxim s'espera obtindre estats cerebrals en les nostres dades de RMf-er en rates, per a comparar dinàmicament el comportament de les xarxes cerebrals com a biomarcador de TCA. En conclusió, s'han aplicat tècniques de neuroimatge per a estimar la connectivitat cerebral en estat de repòs, l'activitat cerebral i el volum de MG, aplicades a imatges multimodals de RM i s'han obtés resultats que han permés avançar en l'enteniment dels m / Alcohol intake alters brain balance, affecting its structure and function, and it may cause Alcohol Use Disorders (AUDs). We aimed to study the effects of chronic, excessive alcohol consumption on the brain from a functional and structural point of view, via analysis of multimodal magnetic resonance (MR) images. We conducted three studies with specific aims: i) To understand how the neuroadaptations triggered by alcohol intake are reflected in between-network resting-state functional connectivity (rs-FC) and brain activity in the onset of alcohol dependence, we performed studies in msP rats in control and alcohol conditions. Group probabilistic independent component analysis (group-PICA) and spatial regression were applied to resting-state functional magnetic resonance imaging (rs-fMRI) images to obtain subject-specific time courses of seven resting-state networks (RSNs). Then, we estimated rs-FC via L2-regularized partial correlation. We performed a manganese-enhanced (MEMRI) experiment as a readout of neuronal activity. In alcohol condition, we found hypoconnectivities between the visual network (VN), and striatal (StrN) and sensory-cortex (SCN) networks, all with increased brain activity. On the contrary, hyperconnectivities were found between three pairs of RSNs: 1) medial prefrontal-cingulate (mPRN) and StrN, 2) SCN and parietal association (PAN) and 3) motor-retrosplenial (MRN) and SCN networks, being PAN the only network without brain activity rise. Interestingly, the hypoconnectivities could be explained as control to alcohol transitions from direct to indirect connectivity, whereas the hyperconnectivities reflected an indirect to an even more indirect connection. These findings indicate that RSNs are early altered by prolonged and moderate alcohol exposure, diminishing the executive control and behavioral flexibility. ii) To compare cortical gray matter (GM) volume between 34 healthy controls and 35 alcohol-dependent patients who were detoxified and remained abstinent for 1-5 weeks before MRI acquisition, we performed a voxel-based morphometry analysis. The main structures whose GM volume decreased in abstinent subjects compared to controls were precentral gyrus (PreCG), postcentral gyrus (PostCG), supplementary motor cortex (SMC), middle frontal gyrus (MFG), precuneus (PCUN) and superior parietal lobule (SPL). Decreases in GM volume in these areas may lead to changes in control of movement (PreCG and SMC), in processing tactile and proprioceptive information (PostCG), personality, insight, prevision (MFG), sensory appreciation, language understanding, orientation (PCUN) and the recognition of objects by touch and shapes (SPL). iii) To characterize dynamic brain states in functional MRI (fMRI) signals by means of an approach based on the Hidden Markov model (HMM). Several parameter configurations of HMM-Gaussian in a block-design paradigm were considered, together with different time series: independent components (ICs) and probabilistic functional modes (PFMs) on 14 healthy subjects. The block-design fMRI paradigm consisted of four experimental conditions: rest, visual, motor and visual-motor. Characterizing brain states' dynamics in fMRI data was possible applying the HMM-Gaussian approach to PFMs, with mean activity driving the states. The four spatial maps obtained were named HMM-rest, HMM-visual, HMM-motor and HMM-DMN (default mode network). HMM-DMN appeared once a task state had stabilized. The ultimate goal will be to obtain brain states in our rs-fMRI rat data, to dynamically compare the behavior of brain RSNs as a biomarker of AUD. In conclusion, neuroimaging techniques to estimate rs-FC, brain activity and GM volume can be successfully applied to multimodal MRI in the advance of the understanding of brain homeostasis in AUDs. These functional and structural alterations are a biomarker of chronic alcoholism to explain impairments in executive control, reward evaluation and visuospatial processing. / Pérez Ramírez, MÚ. (2018). Characterizing functional and structural brain alterations driven by chronic alcohol drinking: a resting-state fMRI connectivity and voxel-based morphometry analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/113164 / TESIS

Tato práce se zabývá využitím EEG dat pro výpočet mozkové konektivity a vytvořením klasifikátoru mentální zátěže. Nejdříve je popsán teoretický základ EEG, následně jsou rozebrány některé metody pro určení mozkové konektivity. Pro výpočet klasifikačních příznaků byla použita data nasnímaná během experimentu, který manipuloval s mentální zátěží ve dvou stupních. V práci je popsán průběh experimentu, zpracování a redukce nasnímaných dat, stejně jako extrakce příznaků z nasnímaných EEG dat pomocí několika metod měření konektivity (korelační funkce, kovariance, koherence a míra fázové soudržnosti) a následná automatická klasifikace třemi způsoby (na základě vzdálenosti od vzoru tvořeného průměrem, metoda nejbližšího souseda a diskriminační alanýza). Dosažené výsledky jsou detailně popsány a diskutovány. Nejlepšího výsledku (úspěšnost 60,64%) bylo dosaženo při použití kovarianční matice určené z dat získaných ze 4 elektrod z různých mozkových oblastí (beta pásmo EEG) při klasifikaci založené na lineární diskriminační funkci.

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Approximately 20% of the body’s energy consumption is ongoingly consumed by the brain, where the main part is due to the neural activity, which is only increased slightly when doing a demanding task. This ongoingly neural activity are studied with the so called resting-state fMRI, which mean that the neural activity in the brain is measured for participants with no specific task. These studies have been useful to understand the neural function and how the neural networks are constructed and cooperate. This have also been helpful in several clinical research, for example have differences been identified between bipolar disorder and major depressive disorder. Recent research has focused on temporal properties of the ongoing activity and it is well known that neural activity occurs in bursts. In this study, resting-state fMRI data and temporal graph theory is used to develop a point based method (PBM) to quantify these bursts at a nodal level. By doing this, the bursty pattern can be further investigated and the nodes showing the most bursty pattern (i.e hubs) can be identified. The method developed shows a robustness regarding several different aspects. In the method is two different variance threshold algorithms suggested. One local variance threshold (LVT) based on the individual variance of the edge time-series and one global variance threshold (GVT) based on the variance of all edges time-series, where the GVT shows the highest robustness. However, the choice of threshold needs to be adapted for the aims of the current study. Finally, this method ends up in a new measure to quantify this bursty pattern named bursty centrality. The derived temporal graph theoretical measure was correlated with traditional static graph properties used in resting state and showed a low but significant correlation. By applying this method on resting-state fMRI data for 32 young adults was it possible to identify regions of the brain that showed the most dynamic properties, these regions differed between the two thresholding algorithms

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning.

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning.