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]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/106966

<p dir="ltr">Manganese (Mn) is an essential trace element for humans that functions primarily as a coenzyme in several biological processes such as nerve and brain development, energy metabolism, bone growth and development, as well as cognitive functioning. However, overexposure to environmental Mn via occupational settings or contaminated drinking water can lead to toxic effects on the central nervous systems and cause a Parkinsonian disorder that features symptoms such as fine motor control deficits, dystonia rigidity, speech and mood disturbances, and cognitive deficits summarized under the term “manganism”. Over time, Mn exposure has shifted from acute, high-level instances leading to manganism, to low-level chronic exposure. Considering that Mn exposure is significantly lower than in the past, it is unlikely to expect manganism from chronic Mn exposure under current working conditions. Therefore, there is a need to develop sensitive methods to aid in updating the clinical diagnostic standards for manganism and Mn neurotoxicity as chronic exposure to Mn leads to more subtle symptoms.</p><p><br></p><p dir="ltr">Historically, magnetic resonance imaging (MRI) has been used as a non-invasive tool for detecting excess brain Mn accumulation. Specifically, T1-weighted images show bilateral hyperintensities of the globus pallidus (GP) due to the paramagnetic properties of Mn which increases the MR relaxation rate R1. Although the GP is considered the hallmark of excess brain Mn, this brain area is not necessarily associated with symptoms, exposure, or neuropsychological outcomes. Thus, the focus should not be on the GP only but on the entire brain. With recent advances in quantitative MRI (qMRI), whole brain mapping techniques allow for the direct measurement of relaxation rate changes due to Mn accumulation. The work in this dissertation uses such quantitative techniques and network science to establish novel computational in vivo imaging methods to a) visualize and quantify excess Mn deposition at the group and individual level, and b) characterize the toxicokinetics of excess brain Mn accumulation and the role of different brain regions in the development of neurotoxicity effects.</p><p><br></p><p dir="ltr">First, we developed a novel method for depicting excess Mn accumulation at the group level using high-resolution R1 relaxation maps to identify regional differences using voxel-based quantification (VBQ) and statistical parametric mapping. Second, we departed from a group analysis and developed subject-specific maps of excess brain Mn to gain a better understanding of the relationship between the spatial distribution of Mn and exposure settings. Third, we developed a novel method that combines network science with MRI relaxometry to characterize the storage and propagation of Mn and Fe in the human brain and the role of different brain regions in the development of neurotoxic effects. Lastly, we explore the application of ultra-short echo (UTE) imaging to map Fe content in the brain and compare it against R2* and quantitative susceptibility mapping (QSM).</p><p><br></p><p dir="ltr">Overall, this dissertation is a successful step towards establishing sensitive neuroimaging screening methods to study the effects of occupational Mn exposure. The individual Mn maps offer great potential for evaluating personal risk assessment for Mn neurotoxicity and allow monitoring of temporal changes in an individual, offering valuable information about the toxicokinetics of Mn. The integration of network science provides a holistic analysis to identify subtle changes in the brain’s mediation mechanisms of excess metal depositions and their associations with health outcomes.</p>

本論文所定義之「網路群體犯罪」，不同於組織犯罪般有結構的犯罪團體，亦非為了追求共同利益而合作的共犯夥伴，而是網路使用者自發性互動行為下逐漸浮現的群體近似犯罪行為，並且普遍存在於當今各式各樣的網際網路社群，以各種不同的樣貌與形式展現。本研究以Sutherland（1978）提出之差別接觸理論與Bandura（1977）提出之社會學習理論為基礎，運用理論相關的元素與概念作為食材與食譜，以BDI代理人模式為方法來設計網路群體犯罪之模擬模式，透過動態模擬群體犯罪在不同條件下展現不同之面貌。更運用Watts（2003）主張的網路科學概念與分析方法，來分析犯罪關係網絡之特性，本研究藉由控制網路社群之使用者人數（Size）與初始犯罪率（ICR）來觀察不同組合之下所演化的網路結構差異，並從四個衡量指標：犯罪技能平均數、群聚係數、前10%使用者平均連結度、連結度小於10之比率，標示演化之網路結構的特徵。研究結果發現：1. 犯罪技能擴散的速度受到ICR高低的影響，當ICR越高的時候犯罪技能擴散的速度越快，反之，當ICR較低的時候犯罪技能擴散速度隨之減緩。2. 當ICR超越某一特定臨界值之後，使用者擁有的犯罪技能平均數與所屬社群人數成正向關係。3. ICR的高低對於群聚係數的高低有反向關係，當ICR越高則群聚係數越低，反之，當ICR越低時群聚係數越高。4. 社群使用者人數越多的情況下，群聚係數越低。5. 前10%使用者的平均連結度有隨著演化次數逐漸增加的趨勢。6. 初始犯罪率的高低與前10% 使用者的平均連結度成反比關係。7. 不論演化次數、社群人數多寡與初始犯罪率值之高低，均僅有少數犯罪者擁有高度的連結，絕大多數的使用者或犯罪者其連結度數均不高（符合power law分佈）。 / Collective crime is an emerging phenomenon along with collective intelligence in recent years. It is defined as a form of universally distributed crime originated from spontaneous interaction among community users in this paper. The issues that collective crime addresses focus on deviant or criminal behavior existing in common groups or crowds rather than traditional topics at computer crime or cybercrime. The theories, “differential association” proposed by criminologist Sutherland(1978) and “social learning” proposed by sociologist Bandura(1977), underpin the explanation of collective crime phenomena and the model design of agent-based simulation. The detection function of collective crime consists of the evolving network function based on the micro-simulation and an analysis of the function along with four indicators: average amount of crime skills, average cluster coefficient, average degree of top 10% users, and rate of users with degrees smaller than 10. The research findings are: 1. A community with higher initial crime rate (ICR) results in faster spreading of crime skills. 2. A negative relationship between the community size and the average amounts of crime skills exists, as ICR exceeds a threshold. 3. As ICR gets increasing, the average cluster coefficient gets decreasing, and vice versa. 4. The average cluster coefficient gets decreasing along with increasing community size. 5. The average degree of top 10% users gets increasing along time. 6. A negative relationship exists between ICR and the average degree of the top 10% users. 7. The distribution of the degrees of community users follows the scale-free power law distribution – whatever the network evolution times, community size and ICR are, most of the community users have fewer degrees and only few criminals have pretty high degrees relatively.

La Science des réseaux est apparue comme un domaine d'étude fondamental pour modéliser un grand nombre de systèmes synthétiques ou du monde réel.La découverte du graphe petit monde et du graphe sans échelle dans ces réseaux a révolutionné la façon d'étudier, d'analyser, de modéliser et de traiter ces réseaux. Dans cette thèse, nous nous intéressons à l'étude des réseaux ayant ces propriétés et souvent qualifiés de réseaux complexes.A notre avis, les recherches menées dans ce domaine peuvent être regroupées en quatre catégories: l'analyse, la structure, le processus/organisation et la visualisation.Nous abordons des problèmes relatifs à chacune de ces catégories tout au long de cette thèse. (...) / Network science has emerged as a fundamental field of study to model many physicaland real world systems around us. The discovery of small world and scale free propertiesof these real world networks has revolutionized the way we study, analyze, model andprocess these networks. In this thesis, we are interested in the study of networks havingthese properties often termed as complex networks. In our opinion, research conducted inthis field can be grouped into four categories, Analysis, Structure, Processes-Organizationand Visualization. We address problems pertaining to each of these categories throughoutthis thesis. (...)

miRNAs are non-coding RNAs of approx. 22 nucleotides in length that inhibit gene expression at the post-transcriptional level. By virtue of this gene regulation mechanism, miRNAs play a critical role in several biological processes and patho-physiological conditions, including cancers. miRNA behavior is a result of a multi-level complex interaction network involving miRNA-mRNA, TF-miRNA-gene, and miRNA-chemical interactions; hence the precise patterns through which a miRNA regulates a certain disease(s) are still elusive. Herein, I have developed an integrative genomics methods/pipeline to (i) build a miRNA regulomics and data analytics repository, (ii) create/model these interactions into networks and use optimization techniques, motif based analyses, network inference strategies and influence diffusion concepts to predict miRNA regulations and its role in diseases, especially related to cancers. By these methods, we are able to determine the regulatory behavior of miRNAs and potential causal miRNAs in specific diseases and potential biomarkers/targets for drug and medicinal therapeutics.

Die Covid-19 Pandemie hat gezeigt, wie stark die Ausbreitung von Infektionskrankheiten von der Dynamik der menschlichen Mobilität bestimmt wird. Gleichzeitig eröffnet die anhaltende Explosion an verfügbaren Mobilitätsdaten im 21. Jahrhundert einen viel genaueren Blick auf die menschliche Mobilität. In dieser Arbeit untersuchen wir verschiedene Ansätze, wie moderne Mobilitätsdaten zusammen mit Modellierung ein tieferes Verständnis des Zusammenspiels von menschlicher Mobilität und der Ausbreitung von Infektionskrankheiten ermöglichen. Wir verwenden Mobilitätsdaten um zu zeigen, dass landesweite Mobilitätsmuster während der Covid-19 Pandemie in Deutschland komplexe strukturelle Veränderungen durchlaufen haben. Wir stellen einen räumlich heterogenen Rückgang der Mobilität während Lockdown-Phasen fest. Vor allem beobachten wir, dass ein deutlicher Rückgang der Fernreisen während der Pandemie zu einem lokaleren Netzwerk und einer Abschwächung des “Small-World”-Effekts führt. Wir zeigen, dass diese strukturellen Veränderungen einen erheblichen Einfluss auf die Ausbreitungsdynamik von Epidemien haben, indem sie die epidemische Kurve abflachen und die Ausbreitung in geografisch weit entfernte Regionen verzögern. Des Weiteren entwickeln wir eine neue Methode zur Bestimmung des Ausbruchsursprungs anhand von hochaufgelösten geografischen Bewegungsdaten. Abschließend untersuchen wir, wie repräsentativ Mobilitätsdatensätze für das tatsächliche Reiseverhalten einer Bevölkerung sind. Wir identifizieren verschieden Arten von Verzerrungen, zeigen ihre Spuren in empirischen Datensätzen, und entwickeln einen mathematischen Rahmen um diese Verzerrungen abzuschwächen. Wir hoffen, dass unsere Studien in dieser Arbeit sich als hilfreiche Bausteine erweisen für ein einheitliches Verständnis von menschlicher Mobilität und der Dynamik von Infektionskrankheiten. / The Covid-19 pandemic demonstrated how strongly infectious disease spread is driven by the dynamics of human mobility. At the same time, the ongoing explosion of available mobility data in the 21st century opens up a much finer view of human mobility. In this thesis, we investigate several ways in which modern mobility data sources and modeling enable a deeper understanding of the interplay of human mobility and infectious disease spread. We use large-scale mobility data captured from mobile phones to show that country-wide mobility patterns undergo complex structural changes during the Covid-19 pandemic in Germany. Most prominently, we observe that a distinct reduction in long-distance travel during the pandemic leads to a more local, clustered network and a moderation of the “small-world” effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by “flattening” the epidemic curve and delaying the spread to geographically distant regions. Further, we show that high-resolution mobility data can be used for early outbreak detection. We develop a novel method to determine outbreak origins from geolocated movement data of individuals affected by the outbreak. We also present several practical applications that have been developed based on the above research. To further explore the question of applicability, we examine how representative mobility datasets are of the actual travel behavior of a population. We develop a mathematical framework to mitigate these biases, and use it to show that biases can severely impact outcomes of dynamic processes such as epidemic simulations, where biased data incorrectly estimates the severity and speed of disease transmission. We hope that our studies in this thesis will prove as helpful building blocks to assemble the emerging, unified understanding of mobility and infectious disease dynamics.

La problématique de ce doctorat est : comment la structure des réseaux constitue une nouvelle matrice pour la production d’oeuvres artistiques. Pour répondre à cette question, nous commencerons en étudiant l’évolution de la notion de « réseau » de l’antiquité jusqu’à aujourd’hui ; les théories des réseaux concernant leur structure et/ou leur dynamique. Ensuite nous présenterons les applications –de la notion ou des théories de réseaux– tant dans les sciences que dans l’art. Nous listerons et nous analyserons huit types de réseaux et puis nous mentionnerons des oeuvres artistiques qui ont été inspirées par ces types de réseaux et/ou qui utilisent certains (types de) réseaux comme matrice pour leur création. Nous proposerons le projet Lignes Imaginaires, un modèle 3D qui se fonde sur la conception d’un métro de lignes imaginaires, voire de lignes dynamiques et/ou paradoxales qui sont en mouvement, apparaissent/disparaissent, créent de l’infrastructure supplémentaire. L’analyse du métro Lignes Imaginaires dévoile l'importance de la géographie et de la spatialité des réseaux, tandis que leur représentation graphique topologique reste insuffisante pour la représentation précise et pour la compréhension de leur structure (paradoxale). En outre, l’innovation du métro Lignes Imaginaires est que son infrastructure est dynamique et auto-organisée, contrairement aux métros traditionnels où les lignes et leurs itinéraires sont fixes. L’objectif du projet artistique Lignes Imaginaires est de visualiser un concept en créant un métro hors du commun qui pourrait aussi proposer des formes alternatives des réseaux de transports dans le contexte urbain. / This thesis examines and shows ways in which the structure of networks can provide a new matrix for the production of artworks. In order to answer this question we start by studying: the evolution of the term ‘network’ from the ancient times up to nowadays; the theories that refer to network structure or network dynamics. Then we present the applications of these theories into both art and science. We list and analyze eight different types of networks and then we feature artworks which have been inspired by these network types or have used the network structure of a certain type as a matrix for art making. We propose the Imaginary Lines project, a three-dimensional network model which is based on the concept of a metro composed of imaginary lines. More precisely Imaginary Lines metro network encompasses seven paradoxical lines which move, (dis)appear and produce supplementary infrastructure. The Imaginary Lines metro unveils the importance of geography and spatiality, in contrast with topological network graphic representations, which remain insufficient, in terms of utmost accuracy in representation and comprehension of network structure. Additionally, the Imaginary Lines network innovation lays in its infrastructure dynamics as well as in its self-organisation. The objective of the Imaginary Lines artistic project is to visualise a concept by creating an unusual metro, which goes beyond traditional fixed-route transport networks and can support alternative forms of urban transport development.