Dissertations / Theses on the topic 'Connectome'

The corticospinal tract (CST), which carries commands from the cerebral cortex to the spinal cord, is vital to fine motor control. Spinal cord injury (SCI) often damages CST axons, causing loss of motor function, most notably in the hands and legs. Our preliminary work in rats suggests that CST circuitry is complex: neurons whose axons project to the lower cervical spinal cord, which directly controls hand function, also send axon collaterals to other locations in the nervous system and may engage parallel motor systems. To inform research into repair of SCI, we therefore aimed to map the entire projection pattern, or “connectome,” of such cervically-projecting CST axons. In this study, we mapped the corticospinal connectome of the Rhesus macaque - an animal model more similar to humans, and therefore more clinically relevant for examining SCI. Comparison of the Rhesus macaque and rat CST connectome, and extrapolation to the human CST connectome, may improve targeting of treatments and rehabilitation after human SCI. To selectively trace cervically-projecting CST motor axons, a virus encoding a Cre-recombinase-dependent tracer (AAV-DIO-gCOMET) was injected into the hand motor cortex, and a virus encoding Cre-recombinase (AAV-Cre) was injected into the C8 level of the spinal cord. In this intersectional approach, the gCOMET virus infects many neurons in the cortex, but gCOMET expression is not turned on unless the nucleus also contains Cre-recombinase, which must be retrogradely transported from axon terminals in the C8 spinal cord. Thus, gCOMET is only expressed in neurons that project to the C8 spinal cord, and it proceeds to fill the entire neuron, including all axon collaterals. Any gCOMET-labeled axon segments observed in other regions of the nervous system are therefore collaterals of cervically-projecting axons. gCOMET-positive axons were immunohistochemically labeled, and axon density was quantified using a fluorescence microscope and Fiji/ImageJ software. Specific regions of interest were chosen for analysis because of their known relevance in motor function in humans, and for comparison to results of a similar study in rats. Results in the first monkey have revealed both similarities and differences between the monkey and rodent CST connectome. Analyses of additional monkeys are ongoing. The final results will provide detailed information about differences between rodent and primate CST, will serve as a baseline for examining changes in the CST connectome after SCI, and will provide guidance for studies targeting treatment and functional recovery after SCI.

Fine-grained descriptions of brain connectivity are required to understand how neural information is processed and relayed across spatial scales. Prior investigations of the mouse brain connectome have employed discrete anatomical parcellations, limiting spatial resolution and potentially concealing network attributes critical to connectome organization. In this work, we provide a voxel-level description of the network and hierarchical structure of the directed mouse connectome, unconstrained by regional partitioning. We found that hub regions and core network components of the voxel-wise mouse connectome exhibit a rich topography encompassing key cortical and subcortical relay regions. We also typified regional substrates based on their directional topology into sink or source regions, and reported a previously unappreciated role of modulatory nuclei as critical effectors of inter-modular and network communicability. Finally, we demonstrated a close spatial correspondence between the mesoscale topography of the mouse connectome and its functional macroscale organization, showing that, like in primates and humans, the mouse cortical connectome is organized along two major topographical axes that can be linked to hierarchical patterns of laminar connectivity, and shape the topography of fMRI dynamic states, respectively. This investigation was paralleled by further studies aimed to more closely relate structural connectome features to the corresponding large scale functional networks of the mouse brain. We first focused on the mouse default mode network (DMN), describing its axonal substrates with sublaminar precision and cell-type specificity. We found that regions of the mouse DMN are predominantly located within the isocortex and exhibit preferential connectivity. Dedicated tract tracing experiments carried out by the Allen Brain Institute revealed that layer 2/3 DMN neurons projected mostly in the DMN, whereas layer 5 neurons project both in and out. Further analyses revealed the presence of separate in-DMN and out-DMN-projecting cell types with distinct genetic profiles. Lastly, we carried out a fine-grained comparison of functional topography and dynamic organization of large-scale fMRI networks in wakeful and anesthetized mice, relating the corresponding functional networks to the underlying architecture of structural connectivity. Recapitulating prior observations in conscious primates, we found that the awake mouse brain is subjected to a profound topological reconfiguration such to maximize cross-talk between cortical and subcortical neural systems, departing from the underlying structure of the axonal connectome. Taken together, these results advance our understanding of the foundational wiring principles of the mammalian connectome, and create opportunities for identifying targets of interventions to modulate brain function and its network structure in a physiologically-accessible species.

Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February 2016.
"December 2015." Cataloged from PDF version of thesis.
Includes bibliographical references (pages 83-88).
By investigating neural connections, neuroscientists try to understand the brain and reconstruct its connectome. Automated connectome reconstruction from high resolution electron miscroscopy is a challenging problem, as all neurons and synapses in a volume have to be detected. A mm3 of a high-resolution brain tissue takes roughly a petabyte of space that the state-of-the-art pipelines are unable to process to date. A high-performance, fully automated image processing pipeline is proposed. Using a combination of image processing and machine learning algorithms (convolutional neural networks and random forests), the pipeline constructs a 3-dimensional connectome from 2-dimensional cross-sections of a mammal's brain. The proposed system achieves a low error rate (comparable with the state-of-the-art) and is capable of processing volumes of 100's of gigabytes in size. The main contributions of this thesis are multiple algorithmic techniques for 2- dimensional pixel classification of varying accuracy and speed trade-off, as well as a fast object segmentation algorithm. The majority of the system is parallelized for multi-core machines, and with minor additional modification is expected to work in a distributed setting.
by Wiktor Jakubiuk.
M. Eng. in Computer Science and Engineering

Traumatic Brain Injury (TBI) is a leading cause of death and disability globally, with survivors often experiencing ongoing and debilitating cognitive impairments (e.g., slowed processing speed, poor attention, and executive functioning deficits). These impairments are often linked to focal lesions in regions of the cerebral cortex thought to uphold each cognitive function. However, the spectrum of impairments experienced by individual patients are not fully explained by focal lesions of the grey matter; instead, emerging theories suggest that many cognitive burdens result from disconnections in the white matter of the brain. With the advent of diffusion MRI (dMRI), new techniques are available to study how TBI disrupts the white matter pathways that connect brain regions (structural connectomics). Structural connectomics allows the quantification of network disruption in TBI patients using graph theoretical analyses, with studies reporting alterations in brain network integration and segregation. These studies suggest that graph metrics may be used as a ‘biomarker’ for TBI patients’ cognitive impairments, by linking changes in brain derived graph metrics to cognitive symptoms. However, challenges remain in ascribing behavioural relevance to graph metrics in this newly emerging field. This thesis critically evaluates the use of graph theoretical measures of the structural connectome in moderate-severe TBI, and their use at a single-subject level. First, a meta-analysis of studies comparing healthy controls and TBI patients using graph metrics is used to demonstrate that communication metrics are most robustly linked to brain injury. This review also highlights issues with the over-interpretation of the relationship between graph metrics such as path-length and the efficiency of cognitive processes. Second, a study in healthy adults shows that communication metrics are related to processing speed. This relationship between cognitive performance and measures of network alteration is underpinned by biologically plausible models of cognition and brain structure. Third, a profile of graph theoretical properties and alterations in six TBI patients is explored using a personalised connectomics approach. Spiderplots are used to represent graph metric alterations in each patient compared to healthy controls. Profiling individual patients in this way provides new insights into how graph metrics relate to lesion characteristics and TBI subtypes. Taken together, this thesis explores 1) how structural network topology is altered in patients with TBI, 2) how graph metrics can be interpreted, 3) how a personalised connectomics approach to TBI can be implemented, and 4) the methodological considerations for studying TBI using graph theory. The collective results of thesis indicate that graph metrics display potential for characterising network alterations in patients with brain injury; specifically, a profiling approach can account for heterogeneity in the TBI population, informing clinical decision making.

Les neurones corticaux sont générés sur de longues périodes embryonnaires et post-natales. Des travaux précédents ont montré que les neurones générés à des stades embryonnaires précoces jouent un rôle essentiel dans la coordination de l'activité neuronale nécessaire à la maturation des réseaux de neurones corticaux. La première partie de mon travail a consisté à caractériser le connectome structural des neurones glutamatergiques et GABAergiques en utilisant la méthode du « fate mapping » permettant l’expression de protéines fluorescentes en fonction de la date de genèse des neurones. En utilisant la microscopie à feuillet de lumière sur des cerveaux transparisés, j’ai pu quantifier la distribution de différentes populations neuronales dans le cerveau entier.La deuxième partie de mon travail a été consacrée à caractériser le connectome fonctionnel des neurones GABAergiques et à démontrer la présence de neurones « hubs » dans le cortex en baril en développement. En utilisant des lignées de souris transgéniques exprimant l’indicateur de calcium GCaMP6s, nous avons suivi la maturation et la dynamique fonctionnelle du réseau neuronal au cours des deux premières semaines postnatales en utilisant l’imagerie à deux photons in vivo. La distribution des liens fonctionnels entre neurones suit une loi de probabilité à queue lourde suggérant la présence de neurones « hubs ». En utilisant l’imagerie calcique à deux photons et une stimulation « optogénétique-holographique », nous avons démontré le rôle « hub » d’une sous-population de neurones GABAergiques dans la synchronisation de l’activité du réseau dans le cortex en baril au cours du développement
Cortical neurons are generated throughout an extended embryonic period. Recent studies indicate that the cells originating from the earliest stages of neurogenesis are critically involved in coordinating neuronal activity, instructing network maturation throughout large cortical areas. The first part of my work was building and mining brain cell atlases and connectomes. I first characterized the brain-wide structural connectome of early-born glutamatergic and GABAergic neurons, fluorescently labeled according to their date of birth (genetic fate-mapping approach). Using light-sheet microscopy on cleared brains, I quantify the distribution of both populations in the whole brain to create an Atlas.The second part of my work was the characterization of GABAergic neurons functional connectome and the characterization of hub cells in the developing barrel cortex in vivo. By using transgenic mice lines expressing the calcium indicator GCaMP6s, we follow the maturation and the functional dynamics of the network during the two first postnatal weeks using two-photon imaging. The characteristically heavy-tailed distribution of functional connections between neurons that we observed, strongly suggest the presence of hub neurons. Using two-photon calcium imaging and holographic-optogenetic stimulation we entangle the necessary and sufficient conditions of how GABAergic neurons contribute to and synchronize network activity as acting as hub neuron in the barrel cortex

Globally, preterm birth (defined as birth at < 37 weeks of gestation) affects around 11% of deliveries and it is closely associated with cerebral palsy, cognitive impairments and neuropsychiatric diseases in later life. Magnetic Resonance Imaging (MRI) has utility for measuring different properties of the brain during the lifespan. Specially, diffusion MRI has been used in the neonatal period to quantify the effect of preterm birth on white matter structure, which enables inference about brain development and injury. By combining information from both structural and diffusion MRI, is it possible to calculate structural connectivity of the brain. This involves calculating a model of the brain as a network to extract features of interest. The process starts by defining a series of nodes (anatomical regions) and edges (connections between two anatomical regions). Once the network is created, different types of analysis can be performed to find features of interest, thereby allowing group wise comparisons. The main frameworks/tools designed to construct the brain connectome have been developed and tested in the adult human brain. There are several differences between the adult and the neonatal brain: marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes in the T1 weighted image. All of these issues make the standard processes to construct the brain connectome very challenging to apply in the neonatal population. Several groups have studied the neonatal structural connectivity proposing several alternatives to overcome these limitations. The aim of this thesis was to optimise the different steps involved in connectome analysis for neonatal data. First, to provide accurate parcellation of the cortex a new atlas was created based on a control population of term infants; this was achieved by propagating the atlas from an adult atlas through intermediate childhood spatio-temporal atlases using image registration. After this the advanced anatomically-constrained tractography framework was adapted for the neonatal population, refined using software tools for skull-stripping, tissue segmentation and parcellation specially designed and tested for the neonatal brain. Finally, the method was used to test the effect of early nutrition, specifically breast milk exposure, on structural connectivity in preterm infants. We found that infants with higher exposure to breastmilk in the weeks after preterm birth had improved structural connectivity of developing networks and greater fractional anisotropy in major white matter fasciculi. These data also show that the benefits are dose dependent with higher exposure correlating with increased white matter connectivity. In conclusion, structural connectivity is a robust method to investigate the developing human brain. We propose an optimised framework for the neonatal brain, designed for our data and using tools developed for the neonatal brain, and apply it to test the effect of breastmilk exposure on preterm infants.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Title as it appears in MIT Commencement Exercises program, June 5, 2015: Connectomics project : performance engineering neural image segmentation. Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 77-79).
Segmentation of images, the process of grouping together pixels of the same object, is one of the major challenges in connectome extraction. Since connectomics data consist of large quantity of digital information generated by the electron microscope, there is a necessity for a highly scalable system that performs segmentation. To date, the state-of-the-art segmentation libraries such as GALA and NeuroProof lack parallel capability to be run on multicore machines in a distributed setting in order to achieve the scalability desired. Employing many performance engineering techniques, I parallelize a pipeline that uses the existing segmentation algorithms as building blocks to perform segmentation on EM grayscale images. For an input image stack of dimensions 1024 x 1024 x 100, the parallel segmentation program achieves a speedup of 5.3 counting I/O and 9.4 not counting I/O running on an 18-core machine. The program has become I/O bound, which is a better fit to run on a distributed computing framework. In this thesis, the contribution includes coming up with parallel algorithms for constructing a regional adjacency graph from labeled pixels and agglomerating an over-segmentation to obtain the final segmentation. The agglomeration process in particular is challenging to parallelize because most graph-based segmentation libraries entail very complex dependency. This has led many people to believe that the process is inherently sequential. However, I found a way to get good speedup by sacrificing some segmentation quality. It turns out that one could trade o a negligible amount in quality for a large gain in parallelism.
by Quan Nguyen.
M. Eng.

La connectomique est l’étude des cartes de connectivité du cerveau (animal ou humain), qu’on nomme connectomes. À l’aide des outils développés par la science des réseaux complexes, la connectomique tente de décrire la complexité fonctionnelle et structurelle du cerveau. L’organisation des connexions du connectome, particulièrement la hiérarchie sous-jacente, joue un rôle majeur. Jusqu’à présent, les modèles hiérarchiques utilisés en connectomique sont pauvres en propriétés émergentes et présentent des structures régulières. Or, la complexité et la richesse hiérarchique du connectome et de réseaux réels ne sont pas saisies par ces modèles. Nous introduisons un nouveau modèle de croissance de réseaux hiérarchiques basé sur l’attachement préférentiel (HPA - Hierarchical preferential attachment). La calibration du modèle sur les propriétés structurelles de réseaux hiérarchiques réels permet de reproduire plusieurs propriétés émergentes telles que la navigabilité, la fractalité et l’agrégation. Le modèle permet entre autres de contrôler la structure hiérarchique et apporte un support supplémentaire quant à l’influence de la structure sur les propriétés émergentes. Puisque le cerveau est continuellement en activité, nous nous intéressons également aux propriétés dynamiques sur des structures hiérarchiques produites par HPA. L’existence d’états dynamiques d’activité soutenue, analogues à l’état minimal de l’activité cérébrale, est étudiée en imposant une dynamique neuronale binaire. Bien que l’organisation hiérarchique favorise la présence d’un état d’activité minimal, l’activité persistante émerge du contrôle de la propagation par la structure du réseau.
Connectomics is the study of the brain connectivity maps (animal or human), described as complex networks and named connectomes. The organization of the connections, including the network’s hidden hierarchy, plays a major role in our understanding of the functional and structural complexity of the brain. Until now, the hierarchical models in connectomics have exhibited few emergent properties and have proposed regular structures whereas conectomes and real networks show complex structures. We introduce a new growth model of hierarchical networks based on preferential attachment (HPA - hierarchical preferential attachment). The structure can be controlled by a small set of parameters to fit real networks. We show how functional properties emerge from the projection of the hierarchical organization. Furthermore, we use HPA to investigate the minimum level of activity of the brain. The network response under binary dynamics shows evidence of persistent activity, similar to the resting-state of the brain. Even though hierarchical organization is beneficial for sustained activity, we show that persistent activity emerges from the control of the structure over the dynamics.

During the past decade, there has been a great interest in creating mathematical models to describe the properties of connectivity in the human brain. One of the established tools to describe these interactions among regions of the brain is graph theory. However, graph theoretical methods were mainly designed for the analysis of single network which is problematic for neuroscientists wishing to study groups of subjects. Specifically, studies using the Rich Club (RC) graph measure require cumbersome methods to make statistical inferences. In the first part of this work, we propose a framework to analyse the inter-subject variability in Rich Club organisation. The proposed framework is used to identify the changes in RC coefficient and RC organisation in patients with schizophrenia relative to healthy control. We follow this work by proposing a novel method, named Rich Block (RB), which is a combination of the tradition Rich Club and Stochastic Block Models (SBM). We show that using RBs can not only facilitate an inter-subject statistical inference, it can also account for differences in profile of connectivity, and control for subject-level covariates. We validate the Rich Block approach by simulating networks of different size and structure. We find that RB accurately estimates RC coefficients and RC organisations, specifically, in network with large number of nodes and blocks. With real data we use RB to identify changes in coefficient and organisation of highly connected sub-graphs of hub blocks in schizophrenia. In the final portion of this work, we examine the methods used to define each edge in networks formed from resting-state functional magnetic resonance imaging (rs-fMRI). The standard approach in rs-fMRI is to divide the brain into regions, extract time series, and compute the temporal correlation between each region. These correlations are assumed to follow standard results, when in fact serial autocorrelation in the time series can corrupt these results. While some authors have proposed corrections to account for autocorrelation, they are poorly documented and always assume homogeneity of autocorrelation over brain regions. Thus we propose a method to account for bias in interregion correlation estimates due to autocorrelation. We develop an exact method and an approximate, more computationally efficient method that adjusts for the sampling variability in the correlation coefficient. We use inter-subject scrambled real-data to validate the proposed methods under a null setting, and intact real-data to examine the impact of our method on graph theoretical measures. We find that the standard methods fail to practically correct the sensitivity and specificity level due to over-simplifying the temporal structure of BOLD time series, while even our approximate method is substantially more accurate.

Consciousness and loss of consciousness is something we encounter in our everyday lives. Despite its commonplace in everyday life, scientists are still trying to understand and find reliable markers for it. In this work we use a data-driven K-means clustering approach to uncover the different functional patterns associated with different consciousness levels. We pursue this study using a high resolution optogenetic voltage image of the mouse brain waking up from anesthesia. The main questions we addressed in this study are: Can we identify signatures of conscious and unconsciousness from functional connectivity patterns? What is the nature of the different patterns that correspond to wakefulness and anesthesia? What is the nature of dynamics between these functional patterns in wakefulness and anesthesia? How does the anatomical connectivity support the observed functional patterns in wakefulness and anesthesia? Our results show that during anesthesia, the brain is characterized by a single dominant brain pattern with short range connections. Furthermore, we observed from our results that during anaesthesia the brain is characterized by minimal temporal exploration of the different brain configurations. Conversely, in awake state we observed the opposite. The brain pattern with long range connections are frequent in wakefulness. In addition, wakefulness is characterized by somewhat frequent temporal exploration of brain states. Our results show that analysis of functional connectivity patterns can be a useful tool for identifying specific and generalizable fingerprints of wakefulness and anaesthesia

Cartographier le connectome structurel humain est un des enjeux majeurs de la neuroimagerie. Face à l'immensité des connexions neuronales, les méthodes d'imagerie n'ont cessé d'évoluer pour gagner en détails. Des dissections de Klingler à l'IRM de diffusion et aux méthodes de microscopie avancées, ces modalités complémentaires améliorent notre compréhension de l'architecture de la matière blanche à différentes échelles. La tractographie a été introduite comme méthode computationnelle visant la reconstruction virtuelle de fibres axonales à partir de données d'IRM pondérées en diffusion. Parmi ces méthodes, la méthode de tractographie globale repose sur l'utilisation de verres de spin, de petites portions de fibres axonales, dont les positions, orientations et connexions sont des variables optimisées conjointement par le biais d'une fonction de coût. Contrairement aux méthodes conventionnelles, qui estiment ces variables individuellement pour chaque fibre, cette approche identifie un optimum global tendant vers les configurations de fibres les plus plausibles compte tenu des directions axonales modélisées grâce l'IRM de diffusion. Le processus d'optimisation de Metropolis-Hastings sous-jacent demeure très coûteux computationnellement, empêchant tout usage à large échelle. Dans cette optique, ExaTract a été développé au sein de cette thèse pour considérablement accélérer les calculs tirant parti de l'émergence des architectures HPC. ExaTract s'applique aussi bien sur des jeux de données d'IRM de diffusion in vivo conventionnels que sur des jeux de données de très hautes résolutions issus de l'IRM à haut champ ou de la microscopie 3D-PLI. Là où les approches dites de phénotypage large cherchent à acquérir des données sur de larges cohortes de sujets pour en extraire des patterns, le phénotypage profond vise une cartographie précise et exhaustive d'un nombre réduit de spécimens. C'est dans cette approche qu'a été initié le projet Chenonceau: en repoussant les limites de l'IRM à haut champ, un cerveau human post-mortem a été imagé sur deux campagnes d'acquisition étalées sur près de 2 ans et menées conjointement sur les IRMs précliniques à 7 et à 11.7 Tesla de NeuroSpin, atteignant jusqu'à 100 µm de résolution. Ce jeu de données d'IRM multi-modale unique au monde rassemble 48 champs de vue couvrant le cerveau entier et fournit des caractéristiques cyto- et myélo-architectonique et de connectivité structurale à l'échelle mésoscopique. L'apport de cette thèse au sein du projet Chenonceau porte sur le traitement des données et la reconstruction de ce jeu de données massif à l'échelle du cerveau complet, permettant ainsi le partage à la communauté scientifique. Dans un troisième volet, ExaTract a été appliqué sur le jeu de données Chenonceau pour établir le premier atlas de connectivité du cerveau humain à l'échelle mésoscopique. Bénéficiant de méthodes de classification non-supervisée, l'atlas produit regroupe les faisceaux de matière blanche profonds, mais également les fibres courtes, alors observées à une échelle jamais atteinte auparavant. Bien que très peu étudiées dans la littérature, ces fibres ont été trouvées en très grand nombre au sein du cerveau Chenonceau
Mapping the human structural connectome is one of the major challenges of neuroimaging. Faced with the complexity of neuronal connections, imaging methods are constantly evolving to reveal ever-finer details. From Klingler dissections to diffusion MRI and advanced microscopy methods, these complementary modalities enhance our understanding of white matter architecture at multiple scales. Tractography has been introduced as a computational method for the virtual reconstruction of axonal fibres from diffusion-weighted MRI data. Among these methods, the global tractography method is based on the use of spin glasses, small portions of axonal fibres, whose positions, orientations and connections are variables jointly optimised by means of a cost function. Unlike conventional methods, which estimate these variables individually for each fibre, this approach identifies a global optimum tending towards the most plausible fibre configurations given the axonal directions derived from diffusion MRI. The underlying Metropolis-Hastings optimisation process remains computationally very expensive, preventing any large-scale use. To this end, ExaTract was developed as part of this thesis to considerably speed up computations, taking advantage of the emergence of HPC architectures. ExaTract can be applied both to conventional in vivo diffusion MRI datasets and to very high-resolution datasets from high-field MRI or 3D-PLI microscopy. Whereas wide phenotyping approaches aim to acquire data on large cohorts of subjects to extract patterns, deep phenotyping focuses on precise and exhaustive mapping of a small number of specimens. The Chenonceau project took this approach: by pushing the boundaries of high-field MRI, a post-mortem human brain was acquired over two campaigns spread over nearly 2 years and carried out jointly on NeuroSpin's 7 and 11.7 Tesla preclinical MRIs, achieving up to 100 µm resolutions. This unique in-the-world multi-modal MRI dataset gathers 48 fields of view covering the whole brain and provides cyto- and myelo-architectonic features and structural connectivity at the mesoscopic scale. The contribution of this thesis to the Chenonceau project concerns data processing and the reconstruction of this massive dataset at a whole-brain scale, allowing it to be shared with the scientific community. In a third aspect, ExaTract was applied to the Chenonceau dataset to establish the first atlas of human brain connectivity on a mesoscopic scale. Using unsupervised clustering methods, the built atlas includes not only deep white matter bundles but also short fibres, which were observed on a scale never met before. Although poorly studied in the literature, these fibres were found in very large numbers in the Chenonceau brain

Episode dépressif caractérisé (EDC) devient de plus en plus fréquent dans le monde entier. Les imageries fonctionnelles et volumétriques ont trouvé des activations anormales et des réductions de la substance grise cérébrale des patients d’EDC. Pourtant, le pattern des connexions cérébrales (le connectome structurel) des patients en EDC en imagerie de diffusion est peu connu et incomplet. L’objectif de ce travail est d’étudier le connectome structurel des patients d’EDC. Pendant 3 ans du 03/2014 au 03/2017, 56 patients d’EDC et 31 sujets sains de contrôles ont inclus dans l’étude. Tous ces patients ont reçu le même traitement de dépression de venlafaxine et ont été suivi 3 mois. Ils ont reçu l’évaluation clinique et d’IRM anatomique et de la diffusion cérébrale à l’inclusion et à 3 mois.Les contrôles ne sont évalués qu’à l’inclusion. A 3 mois, 37 patients sur 56 ont fini toutes les évaluations.On a trouvé que l’ancienne usage de l’antidépresseur (AD) et l’ancien épisode de dépression lient respectivement à l’augmentation et à la diminution de l’anisotropie cérébrale des patients déprimés. Aucune différence de l’anisotropie cérébrale entre les patients et les sujets sains à l’inclusion et à 3 mois du traitement n’a été détectée. La réponse à l’AD ne lie pas à l’anisotropie cérébrale des patients à l’inclusion et à 3 mois. La topographie des connexions semble modifiée mais pas significative. Ce résultat a mis en évidence pour la première fois 2 affections opposites de l’AD et de la dépression sur le connectome structurel cérébral à long terme
Major depressive disorder (MDD) is expanding on worldwide. Functional and volumetric imaging found abnormal activities and reductions in cerebral gray matter in MDD patients. However, the pattern of brain connections (structural connectome) of MDD patients in diffusion imaging remains unclear. The objective of this work is to study the structural connectome of MDD patients. For 3 years from 03/2014 to 03/2017, 56 MDD patients and 31 healthy controls (HC) were included in the study. All of these patients received the same venlafaxine depression treatment and were followed for 3 months. They received clinical evaluation and anatomical MRI and cerebral diffusion at baseline and at 3 months. HC are evaluated once at inclusion. At 3 months, 37 out of 56 patients completed all assessments. The old use of the antidepressant drugs (AD) and the previous episode of depression have been found to be related to the increased and decreased of cerebral anisotropy in depressed patients, respectively. No differences in cerebral anisotropy between patients and HC at baseline and at 3 months of treatment were detected. The response to AD is not related to patients’ cerebral anisotropy at baseline and at 3 months. The topography of the connections seems modified but not significant. This result showed for the first time 2 opposing affections of AD and depression on the cerebral structural connectome in long term

The term cerebral palsy (CP) encompasses a group of disorders of movement and/or posture and of motor function due to a non-progressive injury, interference or abnormality in an immature or developing brain. These motor disorders are often accompanied by disturbances of sensation, perception, cognition, communication, behaviour or epilepsy, and it has been shown that these deficits may have a greater impact on the quality of life of these people than the motor impairment itself. Taking into account the classification of CP according to the type of motor involvement, dyskinetic CP is identified as a rare subtype (between 3% and 15% of all CP cases) characterized by abnormal patterns of posture and/or movement, accompanied by involuntary, uncontrolled, recurrent and, occasionally, stereotyped movements. This type of CP has been mainly associated with a perinatal hypoxic-ischemic event in children born at term or near term and in neonates with kernicterus. It has been shown that dyskinetic CP is one of the most disabling forms of CP, not only because of its association with greater motor impairment but also because of a greater presence and severity of associated deficits, such as speech and communication impairments. It should be noted that studies analysing this type of CP are still scarce, probably due to their lower frequency and greater severity. Cognitive and neuroimaging studies are specially rare, probably because most neuropsychological evaluations require manual dexterity and/or verbal responses, and neuroimaging studies involve the acquisition of MRI sequences in which the person must remain still. Therefore, the main goals of this thesis are: 1) to identify an objective measure of intelligence adequate for the great heterogeneity that exists in CP, 2) to describe the cognitive profile of a relatively large sample of people with dyskinetic CP, and 3) analyse the state of the cerebral white matter of this population through a connectome approach. To this end, a total of 52 subjects with dyskinetic CP and 52 typically-developing controls were included in the study and were administered a neuropsychological battery that included general cognitive performance (using the Raven’s progressive matrices - colour version, the Peabody picture vocabulary test and the Wechsler's nonverbal scale) and five specific cognitive domains (attention, visual perception, language, learning and memory and executive functioning). Finally, an structural MRI was acquired.
La parálisis cerebral (PC) se define como un grupo de trastornos del movimiento y/o la postura y de la función motora debidos a una lesión, interferencia o anomalía no progresiva en un cerebro inmaduro o en desarrollo. Estos trastornos motores a menudo se acompañan de alteraciones sensoriales, de la percepción, de la cognición, de la comunicación, del comportamiento o de epilepsia, y se ha evidenciado que estos déficits pueden tener un mayor impacto sobre la calidad de vida que el propio déficit motor. Teniendo en cuenta la clasificación de la PC según el tipo de afectación motora se identifica que la PC discinética es un subtipo poco frecuente (entre el 3% y el 15% de todos los casos) que se caracteriza por patrones anormales de postura y/o movimiento, acompañados de movimientos involuntarios, descontrolados, recurrentes y, ocasionalmente, estereotipados. Este tipo de PC se ha asociado principalmente a procesos de hipoxia-isquemia en bebés nacidos a término o casi a término, y a la presencia de ictericia grave. Se ha evidenciado como la PC discinètica es más incapacitante que otros tipos de PC, no sólo por su asociación con una mayor afectación motriz sino también por una mayor presencia y gravedad de déficits asociados, como una mayor afectación del habla y de la comunicación. Cabe destacar que los estudios que han analizado este tipo de PC son todavía escasos, probablemente debido a su menor frecuencia y mayor gravedad. Especialmente escasos son los estudios cognitivos y de neuroimagen, ya que las pruebas neuropsicólogicas requieren de una destreza manual y/o de respuestas verbales y los estudios de neuroimagen implican la adquisición de secuencias de resonancia magnética en dónde la persona debe permanecer quieta. Es por ello que esta tesis tiene como objetivos principales: 1) identificar una medida objetiva de inteligencia adecuada para la gran heterogeneidad que existe en la PC, 2) describir el perfil cognitivo de una muestra relativamente amplia de personas con PC discinética, y 3) analizar el estado de la sustancia blanca cerebral de esta población mediante la técnica del conectoma. Para ello se incluyeron en el estudio un total de 52 sujetos con PC discinética y 52 sujetos control, a quienes se les administró una batería neuropsicólogica que evaluaba el rendimiento cognitivo general (mediante las Matrices progresivas de Raven - versión color, el Test de vocabulario en imágenes de Peabody - III y la Escala no verbal de aptitud intelectual de Wechsler) y cinco dominios cognitivos específicos (atención, visuopercepción, lenguaje, aprendizaje y memoria y función ejecutiva) y se les realizó un estudio de resonancia magnética estructural.

Le système visuel humain est complexe par son organisation anatomique et par son fonctionnement incomplètement élucidé. Il est fonctionnellement divisé en deux systèmes. Le premier système est destiné à la vision consciente communément appelée voie visuelle principale ou en anglais « image forming visual pathways ». Le second, appelé système secondaire ou accessoire, n’apporte pas d’information visuelle consciente, il est dit « non image forming visual pathway ». Ce dernier apporte à notre cerveau une information sur l’environnement telle que la sensation jour/nuit. Ses fonctions sont sous-tendues par l’afflux d’informations rétiniennes non visuelles sur des structures de l’hypothalamus comme le noyau supra-chiasmatique. Les deux systèmes visuels ont un substratum anatomique complexe faisant intervenir de nombreuses structures anatomiques au sein des différents étages du cerveau cortical et sous-cortical comme les noyaux gris centraux dits « Basal Ganglias » (BG). Le système visuel secondaire intervient aussi comme une structure de contrôle des mouvements oculomoteurs tels que la poursuite ou les saccades nécessaires pour explorer notre environnement. Ainsi les saccades oculomotrices sont sous le contrôle modulateur des BG. De ce fait l’étude des saccades apparait comme un très bon modèle pour explorer le fonctionnement du système extrapyramidal au cours des maladies neuro-dégénératives. Les connaissances actuelles sur ce système de contrôle des saccades proviennent essentiellement des études sur le primate non humain et sur des observations cliniques chez l’homme au cours de pathologies dégénératives ou toxiques des BG. L’observation des structures anatomiques, en particulier du réseau de la substance blanche cérébrale qui supporte les connections axonales, n’est pas accessible à l’imagerie clinique de routine. Pour décrire et étudier ces réseaux de connections, la notion de connectomique a été introduite il y a un dizaine d’années. Dans ce travail, nous nous sommes donné l’objectif de décrire le connectome des saccades oculomotrices sur un plan structurel. Nous avons exploré les structures sous-corticales intervenant dans le contrôle des saccades comme les BG, le colliculus supérieur et le pulvinar. Pour ce faire, nous avons utilisé l’imagerie IRM structurelle en diffuseur de tension (DTI) chez deux groupes de patients présentant une maladie neuro-dégénérative : un groupe souffrant de maladie de Parkinson chez qui une atteinte des BG et une dysfonction des saccades sont reconnues, et un groupe de trembleurs essentiels reconnu pour ne pas présenter de dysfonction des saccades et chez qui les BG sont épargnés. Le résultat de ce travail a permis pour la première fois une description in vivo du connectome des saccades chez l’Homme. Il a de plus montré des différences dans la structure du connectome dans les deux groupes de patients. Une meilleure connaissance de ce connectome pourrait permettre de mieux comprendre certains troubles oculomoteurs et aussi de suivre l’évolution de certaines maladies neurodegeneratives
Visual system is complex by its anatomy and its function. Neuro-anatomists have been interested in understanding the link between the visual pathways and the brain for centuries. Classical brain fixation and dissection methods were used to describe the visual pathways identifiable macroscopically. Non–image visual pathway, particularly the part involves in saccadic eye movements network in human is still not mastered. Our current knowledge in SCM is based on animal studies, anatomic dissection and brain histopathology examination of specimens from patients with clinical basal ganglia (BG) disorders. Saccadic eye movements (SCM) are under the control of the basal ganglia (BG) and SCM circuitry within the BG represents a good model for studying pathology in the extra-pyramidal system. The diagnosis of Parkinson’s disease (PD), which affects SEM and its distinction from non-dopaminergic, essential tremor (ET) where SEM are not impaired can be challenging and still relies on clinical observations. Diffusion tensor imaging and fiber tractography (DTI-FT), a new MRI technology, can be used to evaluate the presence and integrity of white matter tracts using directional diffusion patterns of water. The purpose of this study is to use DTI-FT to analyse SEM networks within BG and compare the SEM neural pathways or connectome of patients clinically diagnosed with PD and ET. To date, there are no studies, using DTI-FT for the extensive exploration of non-image visual pathways and SCM circuits, notably the deep brain connections. For this goal, we introduced the concept of SCM connectomes, derived from the general concept of connectome. Our study used structural MRI to identify nuclei and fascicles of the SCM connectome in PD and ET patients; imageries were acquired in routine clinical conditions fitted for DBS surgery. We found a reduction of the fiber number in two fascicles of the connectome in PDcompared to ET group

L’imagerie par résonance magnétique pondérée en diffusion est une modalité d’imagerie médicale non invasive qui permet de mesurer les déplacements microscopiques des molécules d’eau dans les tissus biologiques. Il est possible d’utiliser cette information pour inférer la structure du cerveau. Les techniques de modélisation locale de la diffusion permettent de calculer l’orientation et la géométrie des tissus de la matière blanche. Cette thèse s’intéresse à l’optimisation des métaparamètres utilisés par les modèles locaux. Nous dérivons des paramètres optimaux qui améliorent la qualité des métriques de diffusion locale, de la tractographie de la matière blanche et de la connectivité globale. L’échantillonnage de l’espace-q est un des paramètres principaux qui limitent les types de modèle et d’inférence applicable sur des données acquises en clinique. Dans cette thèse, nous développons une technique d’échantillonnage de l’espace-q permettant d’utiliser l’acquisition comprimée pour réduire le temps d’acquisition nécessaire.

L’IRM de diffusion est une modalité d’imagerie médicale qui suscite un intérêt croissant dans larecherche en neuro-imagerie. Elle permet de caractériser in vivo l’organisation neuronale et apportepar conséquent de nouvelles informations sur les fibres de la matière blanche. En outre, il a étémontré que chaque région corticale a une signature spécifique pouvant être décrite par des mesuresde connectivité. Notre travail de recherche a ainsi porté sur la conception d’une méthode deparcellisation du cortex entier à partir de ces métriques. En se basant sur de précédents travaux dudomaine (thèse de P. Roca 2011), ce travail propose une nouvelle analyse de groupe permettantl’obtention d’une segmentation individuelle ou moyennée sur la population d'étude. Il s’agit d’unproblème difficile en raison de la variabilité interindividuelle présente dans les données. Laméthode a été testée et évaluée sur les 80 sujets de la base ARCHI. Des aspects multimodaux ontété abordés pour comparer nos parcellisations structurelles avec d’autres parcellisations ou descaractéristiques morphologiques calculées à partir des modalités présentes dans la base de données.Une correspondance avec la variabilité de l’anatomie corticale, ainsi qu’avec des parcellisations dedonnées d’IRM fonctionnelle, a pu être montrée, apportant une première validationneuroscientifique
Résumé anglais :Diffusion MRI is a medical imaging modality of great interest in neuroimaging research. Thismodality enables the characterization in vivo of neuronal organization and thus providinginformation on the white matter fibers. In addition, each cortical region has been shown to have aspecific signature, which can be described by connectivity measures. Our research has focused onthe design of a whole cortex parcellation method driven by these metrics. Based on the previouswork of P. Roca 2011, a new group analysis is proposed to achieve an individual or populationaveraged segmentation. This is a difficult problem due to the interindividual variability present inthe data. The method was tested and evaluated on the 80 subjects of the ARCHI database.Multimodal aspects were investigated to compare the proposed structural parcelliations with otherparcellations or morphological characteristics derived from the modalities present in the database. Aconnection between the variability of cortical anatomy and parcellations of the functional MRI datawas demonstrated, providing a first neuroscientist validation

The current thesis addresses some of the unresolved predictions of recent models of the semantic brain system, such as the hub-and-spokes model. In particular, we tackle different aspects of the hypothesis that a widespread network of interacting heteromodal (hub(s)) and unimodal (spokes) cortices underlie semantic cognition. For this purpose, we use connectivity analyses, measures of graph theory and permutation-based statistics with source reconstructed Electro-/MagnetoEncephaloGraphy (EEG/MEG) data in order to track dynamic modulations of activity and connectivity within the semantic networks while a concept unfolds in the brain. Moreover, in order to obtain more accurate connectivity estimates of the semantic networks, we propose novel methods for some of the challenges associated with EEG/MEG connectivity analysis in source space. We utilised data-driven analyses of EEG/MEG recordings of visual word recognition paradigms and found that: 1) Bilateral Anterior Temporal Lobes (ATLs) acted as potential processor hubs for higher-level abstract representation of concepts. This was reflected in modulations of activity by multiple contrasts of semantic variables; 2) ATL and Angular Gyrus (AG) acted as potential integrator hubs for integration of information produced in distributed semantic areas. This was observed using Dynamic Causal Modelling of connectivity among the main left-hemispheric candidate hubs and modulations of functional connectivity of ATL and AG to semantic spokes by word concreteness. Furthermore, examining whole-brain connectomes using measures of graph theory revealed modules in the right ATL and parietal cortex as global hubs; 3) Brain oscillations associated with perception and action in low-level cortices, in particular Alpha and Gamma rhythms, were modulated in response to words with those sensory-motor attributes in the corresponding spokes, shedding light on the mechanism of semantic representations in spokes; 4) Three types of hub-hub, hub-spoke and spoke-spoke connectivity were found to underlie dynamic semantic graphs. Importantly, these results were obtained using novel approaches proposed to address two challenges associated with EEG/MEG connectivity. Firstly, in order to find the most suitable of several connectivity metrics, we utilised principal component analysis (PCA) to find commonalities and differences of those methods when applied to a dataset and identified the most suitable metric based on the maximum explained variance. Secondly, reconstruction of EEG/MEG connectomes using anatomical or fMRI-based parcellations can be significantly contaminated by spurious leakage-induced connections in source space. We, therefore, utilised cross-talk functions in order to optimise the number, size and locations of cortical parcels, obtaining EEG/MEG-adaptive parcellations. In summary, this thesis proposes approaches for optimising EEG/MEG connectivity analyses and applies them to provide the first empirical evidence regarding some of the core predictions of the hub-and-spokes model. The key findings support the general framework of the hub(s)-and-spokes, but also suggest modifications to the model, particularly regarding the definition of semantic hub(s).

Le cortex cingulaire antérieur (CCA) est une région préfrontale située au centre d’un réseau permettant l’échange d’informations cognitives, motrices, limbiques et viscérales, la plaçant ainsi comme un sujet incontournable dans l’étude de pathologies complexes telles que les troubles anxio-dépressifs. Afin de pouvoir aborder ces pathologies chez la souris, nous avons établi par traçage neuronal le connectome complet des différentes aires composant le CCA. Nous avons ainsi montré qu’une grande majorité des structures de ce connectome communique de manière réciproque avec cette région et que, selon les aires cingulaires, des spécificités de densité d'innervation et de topographie peuvent exister. Ceci suggère des fonctions partagées mais également des rôles plus spécifiques à chaque aire. A partir de ce connectome, nous avons ensuite montré, par une approche optogénétique associée à des tests comportementaux, que l'activation répétée de la projection de l’amygdale au CCA est susceptible d'induire des comportements de type anxio-dépressif chez des souris naïves. Ce travail met donc en évidence le rôle d'une partie du connectome du CCA dans l'établissement des troubles de l'humeur
The anterior cingulate cortex (ACC) is a prefrontal region located at the center of a network allowing the sharing of cognitive, motor, limbic and visceral information, placing it as an interesting target for the study of complex pathologies like mood disorders. To investigate these diseases in mice, we provided the complete connectome of each ACC areas by a tract-tracing approach. We demonstrated that the majority of structures constituting this connectome are reciprocally connected with the ACC and that some density and topographical connection specificities were observed among cingulate areas. These results potentially suggest some shared functions between cingulate areas, also completed by specific roles inherent to each area. Using this connectome, we demonstrated that the repeated activation of the amygdala projection to the ACC was able to induce anxiodepressive-like behaviors in naïve mice, by using optogenetics combined with behavioral tests. This study highlights for the first time the implication of a portion of the ACC connectome in the establishment of mood disorders

Ce travail vise au développement d’une méthode d’inférence des fibres de la substance blanche cérébrale fondée sur l’utilisation d’une approche globale de type « verres de spins » sous contraintes anatomiques. Contrairement aux méthodes classiques reconstituant les fibres indépendamment les unes des autres, cette approche markovienne reconstruit l’ensemble des fibres dans un unique processus de minimisation d’une énergie globale dépendant de la configuration des spins (position, orientation, longueur et connexion(s)) et de leur adéquation avec le modèle local du processus de diffusion, afin d'améliorer la robustesse et la réalité anatomique des fibres reconstruites. Le travail mené dans le cadre de cette thèse a donc consisté, en plus du développement de l’algorithme de tractographie, à étudier la possibilité de le contraindre à l’aide d’a priori anatomiques provenant de l’imagerie anatomique pondérée en T1 et des nouvelles approches de microscopie par IRM de diffusion fournissant des informations de nature micro-structurelle sur le tissu. En particulier, l’algorithme a été conçu pour autoriser une forte courbure des fibres à l’approche du ruban cortical et permettre leur connexion au sommet des gyri, mais également sur leurs flancs. Le modèle NODDI (Neurite Orientation Dispersion and Density Imaging) a gagné en popularité au cours des dernières années grâce à sa compatibilité avec une utilisation en routine clinique et permet de quantifier la densité neuritique et la dispersion angulaire des axones. Une forte dispersion traduit l’existence de populations de fibres d’orientations différentes ou une forte courbure d’un même faisceau de fibres au sein d'un voxel. Elle est donc exploitée pour relâcher la contrainte de faible courbure à proximité du cortex cérébral dans notre approche de tractographie globale lorsque cette dispersion angulaire est forte, permettant aux fibres de s'orienter par rapport à la normale locale au cortex. Cette contrainte est en revanche supprimée si la dispersion angulaire reste faible, indiquant une trajectoire à plus faible courbure, à l’instar des fibres se projetant dans le fond du gyrus ou des fibres en U. Les performances de cette nouvelle approche de tractographie sous contraintes anatomiques ont été évaluées à partir de données simulées, et ont été testées sur des données IRM post-mortem de très haute résolution et sur des données IRM in vivo de résolution millimétrique. En parallèle de ce développement méthodologique, une étude des corrélats locaux-régionaux de la densité neuritique et de l’activation cérébrale à la surface du cortex a été réalisée. L'étude a été menée sur la cohorte de sujets sains scannés dans le cadre du projet européen CONNECT dotée de données anatomiques, de diffusion et fonctionnelles reposant sur l’utilisation de paradigmes explorant en particulier les réseaux de la motricité, du langage et de la vision. Les données anatomiques ont permis d’extraire la surface piale et une parcellisation surfacique du cortex de chaque individu, les données de diffusion ont permis l’évaluation des cartographies individuelles de la densité neuritique au sein du ruban cortical et les données fonctionnelles du phénomène BOLD (Blood Oxygen Level Dependent) ont permis le calcul des cartographies individuelles des z-scores du modèle linéaire général pour différents contrastes. Une colocalisation des maxima de la densité neuritique et des pics d'activation a été observée, pouvant être interprétée comme une augmentation de la densité neuritique au sein des réseaux fonctionnels afin d'en améliorer l'efficacité. L’étude a également corroboré la latéralisation du réseau fonctionnel du langage et de la motricité, en accord avec la latéralisation de la population scannée tandis qu'une augmentation de la densité neuritique dans le cortex visuel droit a été observée pouvant être corrélée à des résultats d’étude de l’attention visuo-spatiale reportée dans la littérature chez le primate non-humain
This work aims at developing a method inferring white matter fibers reconstructed using a global spin-glass approach constrained by anatomical prior knowledge. Unlike usual methods building fibers independently from one another, our markovian approach reconstructs the whole tractogram in an unique process by minimizing the global energy depending on the spin glass configuration (position, orientation, length and connection(s)) and the match with the local diffusion process in order to increase the robustness and the accuracy of the algorithm and the anatomical reliability of the reconstructed fibers. Thus, the work done during this PhD, along with the development of the global tractography algorithm, consisted in studying the feasibility of the anatomical prior knowledge integration stemming from the T1 weighted MRI and from new diffusion MRI microstructure approaches providing microstructural information of the surrounding tissue. In particular, the algorithm was built to allow a high fiber curvature when getting closer to the cortical ribbon and thus enabling the connection not only at the end of the gyri but also on their sides. The NODDI (Neurite Orientation Dispersion and Density Imaging) model has become more and more popular during the past years thanks to its capability to be used in clinical routine and allows to quantify neurite density and axons angular dispersion. A high dispersion means the existence of different fibers population or a high curvature of a fascicle within a voxel. Thus, the orientation dispersion has been used in our global tractography framework to release the curvature constraint near the cerebral cortex when the angular dispersion is high, allowing fibers to orientate collinear to the local normal to the cortical surface. However, this constraint is removed if the angular dispersion stays low, meaning a low curvature fiber trajectory following the example of the fibers projecting to the end of a gyrus or the U-fibers. The performances of this new tractography approach constrained by anatomical prior knowledge have been evaluated on simulated data, and tested on high resolution post-mortem MRI acquisitions and millimetric resolution in vivo MRI acquisitions. In parallel of this methodological development, a study about local-regional correlations between neurite density and cerebral activation on the cortical surface has been made. This study has been conducted on the healthy volunteers cohort scanned in the frame of the European CONNECT project including anatomical, diffusion and functional data. The anatomical data has been used to extract the pial surface and an individual parcellation on the cortical surface for each volunteer, the diffusion data has been used to evaluate the individual maps of neurite density within the cortical ribbon and the functional data from the BOLD (Blood Oxygen Level Dependent) effect has been used to calculate the individual z-scores of the general linear model for specific contrasts investigating the motor, language and visual networks. A co-localization of neurite density and activation peaks has been observed, which might indicate an increase of the neurite density within functional networks in order to increase its efficiency. This study also corroborates the lateralization of the language functional network and the motor one, in good agreement with the population lateralization, while an increase of the neurite density in the visual cortex has been observed which might be correlated to the results of visuo-spatial attention studies described in the literature on the non-human primate

Capire come modellare l'attività del cervello a riposo, resting state, è il primo passo necessario per avvicinarsi a una reale comprensione della dinamica cerebrale. Sperimentalmente si osserva che, quando il cervello non è soggetto a stimoli esterni, particolari reti di regioni cerebrali presentano un'attività neuronale superiore alla media. Nonostante gli sforzi dei ricercatori, non è ancora chiara la relazione che sussiste tra le connessioni strutturali e le connessioni funzionali del sistema cerebrale a riposo, organizzate nella matrice di connettività funzionale. Recenti studi sperimentali mostrano la natura non stazionaria della connettività funzionale in disaccordo con i modelli in letteratura. Il modello implementato nella presente tesi per simulare l'evoluzione temporale del network permette di riprodurre il comportamento dinamico della connettività funzionale. Per la prima volta in questa tesi, secondo i lavori a noi noti, un modello di resting state è implementato nel cervello di un topo. Poco è noto, infatti, riguardo all'architettura funzionale su larga scala del cervello dei topi, nonostante il largo utilizzo di tale sistema nella modellizzazione dei disturbi neurologici. Le connessioni strutturali utilizzate per definire la topologia della rete neurale sono quelle ottenute dall'Allen Institute for Brain Science. Tale strumento fornisce una straordinaria opportunità per riprodurre simulazioni realistiche, poiché, come affermato nell'articolo che presenta tale lavoro, questo connettoma è il più esauriente disponibile, ad oggi, in ogni specie vertebrata. I parametri liberi del modello sono stati scelti in modo da inizializzare il sistema nel range dinamico ottimale per riprodurre il comportamento dinamico della connettività funzionale. Diverse considerazioni e misure sono state effettuate sul segnale BOLD simulato per meglio comprenderne la natura. L'accordo soddisfacente fra i centri funzionali calcolati nel network cerebrale simulato e quelli ottenuti tramite l'indagine sperimentale di Mechling et al., 2014 comprovano la bontà del modello e dei metodi utilizzati per analizzare il segnale simulato.

Le cerveau humain est constitué d'un grand nombre de neurones inter-connectés formant des faisceaux de fibres de matière blanche permettant de transmettre des influx nerveux entre différentes régions. Dans cette thèse, divers aspects de la connectivité anatomique cérébrale ont été étudiés en utilisant l'Imagerie par Résonance Magnétique (IRM) et la tractographie. La tractographie est aujourd'hui la seule méthode permettant de reconstruire, en partie, les faisceaux de fibres de matière blanche in vivo et de manière non-invasive. (1) Une première étude visait à caractériser de manière quantitative les faisceaux d'association courts fronto-pariétaux reconstruits par tractographie dans la région centrale chez vingt sujets sains. (2) Une deuxième étude visait à définir une nouvelle méthode de parcellisation (i.e., subdiviser le cerveau en différentes régions macroscopiques) en utilisant comme critère structurel de base des motifs de connectivité reconstruits par tractographie. (3) Enfin, une troisième étude avait pour objectif de créer une ontologie neuroanatomique afin de représenter des régions de matière grise macroscopiques connectées par des faisceaux de fibres reconstruits par tractographie et d'annoter automatiquement des données de la connectomique humaine. L'utilisation de raisonneurs DL (Description Logic) usuels permettait de générer automatiquement des inférences relatives aux relations partie-tout, de connectivité ou enfin de voisinage spatial
Human brain contains a great number of neurons interconnected forming white matter fiber bundles that can transmit information between different regions. In this thesis, different aspects of anatomical connectivity were studied using Magnetic Resonance Imaging (MRI) and tractography. Tractography is currently the only tool that allow to reconstruct white matter fiber bundles in the living human brain and in a non invasive way. (1) A first study aimed to characterize quantitatively the white matter fiber bundles reconstructed by tractography between the precentral and postcentral gyri in twenty healthy subjects. (2) A second study aimed to define a new parcellation scheme (i.e., subdivide the brain into different macroscopic regions) using connectivity patterns reconstructed by tractography as the main structural criteria. (3) Lastly, a third study aimed to create a new ontology in order to represent gray matter regions connected by white matter fiber bundles reconstructed by tractography and to annotate automatically connectomics datasets. The use of common DL (Description Logic) reasoners allowed to infer automatically some new axioms concerning especially part-whole, connectivity or spatial relationships

On a longtemps pensé que la communication intercellulaire était essentiellement régie par les signalisations juxta-, endo- et paracrine, les gap junctions et, plus récemment, les exosomes. Cependant, les travaux de plusieurs groupes dont le nôtre ont révélé que les Tunneling Nanotubes (TNT), des protrusions membranaires riches en actine qui relient le cytoplasme de cellules distantes et permettent le transport intercellulaire dynamique de leur contenu biologique, fournissent également l'infrastructure et les machines pour une communication efficace entre cellules. Malgré des progrès significatifs, la caractérisation de ces nouveaux organites a été limitée par le manque d'informations moléculaires et structurelles. Combler ces lacunes à l'aide d'une série d'outils de pointe et d'approches novatrices est devenu l'objectif principal de ma thèse. Plus précisément, j'ai exploré le rôle des complexes régulateurs de l’actine dans la formation des TNT reliant les cellules neuronales. Mes analyses montrent que les voies moléculaires connues pour être impliquées dans la formation d'autres protrusions membranaires régulent différemment la génération des TNT. En utilisant la microscopie par imagerie en direct, la microscopie électronique cryocorrélative et la tomographie, j'ai également étudié la nano-architecture des TNT neuronaux. Mes découvertes ont démontré que les TNT des cellules neuronales sont composés de plusieurs TNT individuels permettant le passage de vésicules et de mitochondries. En raison des difficultés d'identification des TNT in vivo, mes travaux ont également porté sur la mise en œuvre d'une approche « Connectomic » structurelle pour détecter les TNT dans les tissus sans avoir besoin d'un marqueur spécifique de TNT. Mes résultats indiquent que des structures de type TNT relient les cellules granulaires cérébelleuses migratrices des souris nouveau-nées, ce qui suggère que la communication intercellulaire pendant des événements migratoires dans le cerveau pourrait être médiée par des processus mettant en jeu des TNT. La squelettisation des structures identifiées fournit des informations géométriques qui corroborent les observations faites dans des expériences de couplage de colorants. L'ensemble de mes travaux de thèse fait la lumière sur la formation et la structure des TNT neuronaux in vitro et sur de nouvelles approches pour l'identification des TNT in vivo
Inter-cellular communication has long been thought to be governed by juxta-, endo-, and paracrine signaling, tight junctions, and more recently, exosomes. However, large efforts from our and other groups revealed that Tunneling Nanotubes (TNTs), actin-rich membranous protrusions that connect the cytoplasm of distant cells and allow the dynamic inter-cellular transport of biological cargo, also provide the infrastructure and machinery for effective cell-to-cell communication. Despite significant progress made to unveil TNT-mediated cell communication, the characterization of these novel organelles has been limited by unanswered questions that hail from the lack of both molecular and structural information. Exploring these gaps in the field using a series of state-of-the-art tools and novel approaches became the main focus of my dissertation. Specifically, I explored the specific role of actin-regulator complexes in the formation of TNTs connecting neuronal cells. My analyses show that molecular pathways known to be involved in the formation of other membranous protrusions behave differently in the generation of TNTs. By employing live imaging microscopy, cryo-correlative electron microscopy and tomography approaches, I also studied the nano- architecture of neuronal TNTs. My findings demonstrated that TNTs of neuronal cells are comprised of multiple individual TNTs capable of transporting vesicles and mitochondria. Owing to the difficulties of identifying TNTs in vivo, my work also focused on the implementation of a structural Connectomic approach to detect TNTs in tissue without the need for a TNT-specific marker. My findings indicate that TNT-like structures connect migratory cerebellar granule cells of neonate mice, suggesting that inter-cellular communication during migratory events in the brain could be mediated by TNT-like processes. Skeletonization of the structures identified provide my findings with geometrical information that can be compared with observations made by corroborative dye-coupling experiments. Taken together, my dissertation work sheds light on the formation and structure of neuronal TNTs in vitro, and novel approaches for the identification of TNTs in vivo

Bien qu'outil de référence pour le diagnostic et le suivi de la SEP, l'IRM conventionnelle ne reste que modérément corrélée à l'état clinique du patient. Afin de mieux caractériser les altérations pathologiques, nous employons dans ce travail les techniques d'IRM dites non conventionnelles que sont la spectroscopie par résonance magnétique (SRM) et l'IRM de diffusion. Un premier suivi hebdomadaire, a permis de mettre en évidence la sensibilité des métriques de diffusion et la spécificité de la SRM pour détecter les processus initiaux de la formation d'une lésion.Un second suivi a permis de mettre en évidence des modifications de la diffusivité dans plusieurs faisceaux de substance blanche, avec notamment une diminution de la fraction d'anisotropie et une augmentation de diffusivité radiale, s'aggravant avec l'avancée de la maladie et plus marquée dans les formes progressives.Enfin, l'application de la théorie des graphes a permis de caractériser la connectivité cérébrale dans les quatre formes cliniques et d'étudier leur évolution. Cette étude a permis de mettre en évidence des altérations dans tous les phénotypes cliniques, avec notamment une diminution de la densité du réseau cérébral, plus importante dans les formes progressives de la maladie et tendant à s'accentuer avec la progression de la maladie.Ce travail montre la sensibilité des techniques avancées d'IRM pour la caractérisation des altérations pathologiques et de leur évolution dans la SEP
While conventional MRI is the reference tool for the diagnosis and monitoring of MS, it remains only moderately correlated with the patient’s clinical status. In order to better characterize pathological alterations occurring in MS, we use in this work non-conventional MRI techniques, namely magnetic resonance spectroscopy (MRS) and diffusion MRI.A first weekly follow-up revealed the sensitivity of the diffusion metrics and the specificity of the SRM to detect the initial processes of lesion formation.A second follow-up revealed changes in diffusivity in several white matter fiber bundles, including a decrease in fraction of anisotropy and an increase in radial diffusivity, worsening with advancing disease and more marked in the progressive forms.Finally, the application of graph theory allowed to characterize the brain connectivity in the four clinical forms and to study their evolution. This study allowed us to highlight alterations in all the four clinical phenotypes, including a decrease in the cerebral network density, more marked in the progressive forms of the disease and tending to increase with its progression.This work shows the sensitivity of advanced MRI techniques for the characterization of pathological alterations and their evolution in MS

Les entrées des projections de dLGN et FB des aires corticales à L1 du V1 de la souris sont discontinues. Elles correspondent à un motif d'expression M2AChR. Ce motif est aussi observé chez le rat et le singe. Les neurones en L2/3 alignés avec les zones M2+ ont une grande acuité spatiale, tandis que dans les zones M2- ont une grande acuité temporelle. Ensemble, les zones M2 + et M2- forment des domaines constants. Ils codent des sous-régions du RF, de sorte que plusieurs domaines contribuent à l'image d'un point du champ visuel.En utilisant des traceurs rétrogrades, nous montrons un principe d'organisation générale fondée sur une règle de la distance exponentielle (EDR) et la géométrie corticale. Nous trouvons des invariants de réseau, mais aussi des différences significatives, telles que des connexions de longue distance beaucoup plus faibles chez le macaque. Une EDR est aussi présente à l'échelle locale, à moins de 1,5 mm, ce qui indique qu'elle pourrait être une propriété universellement applicable à toutes les échelles et chez toutes les mammifères.41 injections avec des traceurs rétrogrades ont été faites dans 22 des 45 régions du néocortex de la souris. Nous avons aplati le cortex et utilisé des critères histologiques et génétiques pour la répartition des neurones marqués dans les aires corticales. Pour chaque connexion, un poids a été déterminé. La cohérence entre les animaux est influencée par le poids moyen et la taille de l'injection. La distribution lognormale des connexions à une aire corticale couvre 5 ordres de grandeur et constitue un profil de connectivité qui est caractéristique de chaque aire. La matrice cortico-corticale présente une densité de 96%
We find that inputs to the non-columnar mouse V1 from the dLGN and FB projections from cortical areas to L1 are patchy. The patches are matched to a pattern of M2AChR expression at ?xed locations of mouse, rat, and monkey V1. Neurons in L2/3 aligned with M2-rich patches have high spatial acuity, whereas cells in M2-poor zones have high temporal acuity. Together M2+ and M2-zones form constant-size domains that are repeated across V1. Domains map subregions of the RF, such that multiple copies are contained within the point image. Using tract tracing data from macaque and mouse, we show a general organizational principle based on an exponential distance rule (EDR) and cortical geometry. We find network invariants between mouse and macaque, but also significant differences, such as fractionally smaller and much weaker long distance connections in the macaque than in mouse. An EDR holds at local scales as well (within 1.5 mm), indicating that it might be a universally valid property across all scales and across the mammalian class.41 injections with retrograde tracers were made in 22 of the 40 areas of the mouse neocortex. Flat mounts of the cortex complete with comprehensive histological and genetic criteria enabled allocation of counts of labeled neurons to individual cortical areas. A weight was determined for each connection. Consistency across animals was systematically influenced by mean weight and injection size. The lognormal distribution of connections to a cortical area spanned 5 orders of magnitude and constituted a connectivity profile that was highly characteristic for each area. The resulting matrix showed that 96% of connections that can exist do exist

Il est possible d’aborder l'organisation fonctionnelle du cerveau en modélisant le cerveau comme un système dynamique, ce qui permet d'étudier comment l'architecture fonctionnelle dépend du squelette structurel sous-jacent. En combinant approches expérimentales et théoriques chez la souris, nous avons étudié de façon systématique comment le connectome structurel contraint le connectome fonctionnel.Dans une première partie nous avons généralisé à la souris le logiciel open source The Virtual Brain (Sanz-Leon et al., 2013, Melozzi et al., 2017).En utilisant les données d'IRM de diffusion (IRMd) de 19 souris, nous avons virtualisé leur cerveau pour générer un signal BOLD in silico que nous avons comparé aux données d'IRM fonctionnelle enregistrées chez les mêmes souris pendant la veille passive. Nous montrons que les prédictions du modèle basé sur le connectome dépendent strictement de la structure du réseau (Melozzi et al., en révision). Nous démontrons que les variations individuelles définissent une empreinte structurelle spécifique ayant un impact direct sur l'organisation fonctionnelle des cerveaux individuels. Ces résultats démontrent l’existence d’un lien causal entre le connectome structurel et le connectome fonctionnel.Finalement, nous confirmons certaines de nos conclusions en utilisant l’approche inverse: nous avons étudié s’il était possible de déduire le connectome structurel à partir du connectome fonctionnel en utilisant la méthode d'inférence Bayésienne (Melozzi et al., en préparation).Nos résultats aux futures études testant la causalité entre structure et fonction, au niveau du cerveau entier individuel, en conditions physiologique et pathologique
The connectome-based model approach aims to understand the functional organization of the brain by modeling the brain as a dynamical system and then studying how the functional architecture rises from the underlying structural skeleton. In this thesis, taking advantage of mice studies, we investigated the informative content of different structural features in explaining the functional ones.First, we extended the open-source software TVB (Leon et al., 2013), originally designed for humans, to accommodate the connectome-based model approach in mice (Melozzi et al., 2017).Using diffusionMRI (dMRI) data from 19 mice, we virtualised their brains to generate in silico fMRI that we compared to functional MRI data recorded in the same mice during passive wakefulness. We show that the predictions of the connectome-based model strictly depend on the structure of the underlying network (Melozzi et al., under review). We demonstrate that individual variations define a specific structural fingerprint with a direct impact upon the functional organization of individual brains. Comparing the predictive power of the tracer-based and the dMRI-based connectome we identify how the limitations of the dMRI method restrict our comprehension of the structural-functional relation. Together, these results strongly support the existence of a causal link between the structural and the functional connectomes.Finally, we infer the connectome form resting state dynamics by inferring the structural connectome using the Bayesian inference (Melozzi et al., in prep).Our results pave the way to future studies focusing on the causal link between structure and function at the individual brain level

Orientador: Gabriela Castellano
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas
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Resumo: O conectoma cerebral refere-se ao mapeamento dos circuitos neurais com os objetivos de 1) identificar regiões que dão suporte às atividades mentais e comportamentais, e 2) detectar alterações nesses circuitos que levam a distúrbios de ordem psiquiátrica e neurológica. Na prática, os estudos de conectoma cerebral consistem na integração de técnicas multimodais de imageamento como ressonância magnética (RM), eletroencefalograma (EEG) e magnetoencefalograma (MEG) com o intuito de estimar os tipos e os níveis de conexão entre regiões cerebrais remotas. Essa "conectividade" entre regiões cerebrais é geralmente classificada em três tipos: anatômica, funcional e efetiva. No presente trabalho, as técnicas de conectividade, usando dados de MR, foram aplicadas na comparação de grupos saudáveis e patológicos. Pela técnica de conectividade anatômica observou-se anomalias na substância branca de pacientes com mutação no gene SPG11. Essa anomalias foram detectadas através da redução da anisotropia fracional (FA) e aumento da difusividade média (MD), difusividade radial (RD) e difusividade axial (AD) em regiões subcorticais dos lobos temporal e frontal, bem como no giro do cíngulo, cuneus striatum, corpo caloso e tronco cerebral. Tais achados indicam que o dano neuronal é mais difuso do que indicava a literatura. Um segundo estudo de conectividade anatômica demonstrou que esses índices de difusividade não foram robustos para diferenciar idosos com e sem diagnóstico de depressão indicando a necessidade de avanços na formulação de novos índices com maior sensibilidade. A técnica de conectividade funcional foi empregada em três estudos. No primeiro, observou-se que pacientes com epilepsia de lobo temporal medial unilateral apresentam redução da conectividade funcional durante a execução de tarefas de memória verbal e visual. Essa redução foi predominantemente ipslateral à lesão e associada ao material-específico utilizado no teste de memória. No segundo estudo, verificou-se uma redução dos padrões de conectividade funcional hipotalâmica em sujeitos obesos e a sua parcial elevação após a cirurgia bariátrica concomitantemente à redução de indicadores bioquímicos de inflamação. No terceiro estudo, observou-se que pacientes com doença de Alzheimer apresentaram elevação dos níveis de conectividade funcional na rede saliente (Salience Network) e redução na rede de modo padrão (Default-mode network). Adicionalmente, verificou-se nos pacientes a correlação positiva da síndrome hiperativa com os níveis de conectividade funcional no cíngulo anterior e em áreas da ínsula direita. O conjunto desses resultados ilustra um possível significado clínico para futuro diagnóstico e tratamento da doença de Alzheimer. Pela técnica de conectividade efetiva observou-se que em função do envelhecimento sadio há uma mudança dos parâmetros de conectividade durante a codificação de palavras com conteúdo emocional. A influência do hipocampo sobre a amígdala ipslateral é reduzida nos sujeitos mais velhos enquanto a influência da amígdala direita sobre o hipocampo direito é elevada. Tais achados reforçam a tese da ininterrupta plasticidade etária e da dinâmica cerebral normal. Essa mesma técnica foi também empregada para demonstrar os diferentes padrões de influência entre os lobos frontal e temporal de pacientes com ELTM esquerda e sujeitos controle. Encontrou-se alteração nos padrões de conectividade efetiva dos pacientes, indicando que estes podem ser potenciais biomarcadores para a epilepsia
Abstract: Connectome refers to the neural circuitry mapping aiming to identify brain regions that support mental and behavioral functions as well as to detect circuit changes that are linked to psychiatric or neurologic disorders. In practice, connectome studies link several neuroimaging approaches such as MRI, EEG and MEG by means of the estimation of connections among remote brain regions. This "connectivity" among brain regions is usually classified as anatomic, functional or effective. In this work, the technique of connectivity, using MR data, was applied to compare healthy and pathological groups. By means of the anatomical connectivity abnormalities in the white matter of patients with SPG11 mutation were observed. These abnormalities were expressed as the reduction of the levels of fractional anisotropy (FA) and the increase in mean (MD) and radial diffusivities (RD) in sub-cortical regions of temporal and frontal lobe as well as in cingulated gyrus, cuneus, striatum, corpus callosum and brainstem. These findings suggest that neuronal damage/dysfunction is more widespread than previously recognized in this condition. Another anatomical connectivity study showed that such indices of diffusivity were not robust to statistically differentiate between old subjects with and without depression. This lacking on finding differences between both groups indicates that new indices of diffusivity have to emerge in order to provide complementary information about brain subtle microstructures. Functional connectivity was applied to three studies. In the first study, it was observed that patients with unilateral medial temporal lobe epilepsy presented lower levels of functional connectivity during visual or verbal memory tasks. Such reduction was ipsilateral to the side of the lesion and associated to the specific-material used in the memory task. In the second work, the levels of functional connectivity were reduced in hypothalamic regions of obese patients but a partial reversibility of hypothalamic dysfunction was observed after bariatric surgery. In the third, patients with Alzheimer disease presented higher values of functional connectivity in the salience network and a reduction of connectivity values in the default-mode network. Also in these patients, significant correlations between the levels of hyperactivity syndrome and the salience network were observed in the anterior cingulate cortex and right insula areas. These results indicate the potential clinical significance of resting state alterations in future diagnosis and therapy of Alzheimer disease. The effective connectivity approaches demonstrated that old and young subjects have significant differences when encoding words with emotional contents. The influence of the hippocampus on the ipsilateral amygdale was lower for older subjects whereas the influence of the right amygdale on the right hippocampus was increased for these subjects. These findings suggest that brain plasticity also happens as function of age. The same approach was used to estimate the influence from frontal to temporal lobes in patients with left MTLE compared to healthy subjects. The patterns of effective connectivity were changed in patients and may be potentially considered as biomarkers for epilepsy
Doutorado
Neurociencias
Doutor em Ciências

La parcellisation du cerveau humain en aires fonctionnelles est un problème complexe mais majeur pour la compréhension du fonctionnement du cerveau et pourrait avoir des applications médicales importantes en neurochirurgie par exemple pour mieux identifier les zones fonctionnelles à sauvegarder. Cet objectif va de pair avec l'idée de construire le connectome cérébral humain, qui n'est autre que le réseau de ses connexions.Pour définir un tel réseau, il faut en effet définir les éléments de ce réseau de connexions : c'est-à-dire avoir un découpage du cerveau en régions. Il existe de multiples manières et critères pour identifier ces régions et à ce jour il n'y a pas de parcellisation universelle du cortex. Dans cette thèse nous étudierons la possibilité d'effectuer cette parcellisation en fonction des données de connectivité anatomique, issues de l'imagerie par résonance magnétique de diffusion, qui est une technique d'acquisition permettant de reconstruire les faisceaux de neurones cérébraux de manière non invasive. Nous nous placerons dans un cadre surfacique en étudiant seulement la surface corticale et les connexions anatomiques sous-jacentes. Dans ce contexte nous présenterons un ensemble de nouveaux outils pour construire, visualiser et simuler le connectome cérébral humain, dans un cadre surfacique et à partir des données de connectivité anatomique reconstruites par IRM, et ceci pour un groupe de sujets. A partir de ces outils nous présenterons des méthodes de réduction de dimension des données de connectivité, que nous appliquerons pour parcelliser le cortex entier de quelques sujets. Nous proposons aussi une nouvelle manière de décomposer les données de connectivité au niveau d'un groupe de sujets en tenant compte de la variabilité inter-individuelle. Cette méthode sera testée et comparée à d'autres méthodes sur des données simulées et des données réelles. Les enjeux de ce travail sont multiples, tant au niveau méthodologique (comparaison de différents algorithmes de tractographie par exemple) que clinique (étude du lien entre altérations des connexions et pathologie).

L'idée que l'intelligence s’appuie non seulement sur des régions spécifiques du cerveau, mais également sur des réseaux cérébraux efficaces s’est récemment affirmée. En effet, on pense que l'organisation du cerveau humain repose sur des réseaux complexes et dynamiques dans lesquels la communication entre les régions cérébrales garantit un transfert efficace d'informations. Ces concepts nous ont amené à explorer les bases neurales de l'intelligence en combinant des techniques avancées d'IRM et la théorie des graphes. D'un côté, les techniques avancées d'IRM, telles que l'IRM fonctionnelle au repos (IRMf-rs) et l'IRM par diffusion (IRMd), permettent d'explorer respectivement la connectivité cérébrale fonctionnelle et structurale, tandis que la théorie des graphes permettent la caractérisation des propriétés des réseaux à différentes échelles, grâce à des métriques globales et locales. L'objectif de cette thèse est de caractériser la topologie des réseaux cérébraux fonctionnels et structurels chez les enfants et les adultes avec un quotient intellectuel supérieur (HIQ) par rapport aux sujets de niveau standard (SIQ). Premièrement, nous avons concentré notre attention sur une population d’enfants présentant différentes caractéristiques cognitives. Deux profils HIQ, à savoir homogène (Hom-HIQ) et hétérogène HIQ (Het-HIQ), ont été définis sur la base d'observations cliniques et de sous-tests du quotient intellectuel (QI). En utilisant des techniques d’IRMf-rs, nous avons examiné la topologie du réseau fonctionnel par « l’indice de rupture de nœud ». Nous avons trouvé des différences topologiques significatives dans les propriétés d'intégration et de ségrégation des réseaux chez les enfants HIQ par rapport aux enfants SIQ, pour le graphe cérébral entier, pour chaque graphe hémisphérique et pour la connectivité homotopique. De plus, ces changements de topologie étaient plus prononcés dans le sous-groupe Het-HIQ. Enfin, nous avons trouvé des corrélations significatives entre les changements des métriques de graphes et le QI total et d’autres indices du QI. Ces résultats ont démontré pour la première fois que les deux profils HIQ sont liés à une organisation différente du substrat neuronal. Ensuite, la connectivité structurale du réseau cérébral, mesurée par IRMd chez l’ensemble des enfants HIQ, est significativement différente de celle des enfants SIQ. Nous avons également aussi de fortes corrélations entre la densité des réseaux cérébraux des enfants et leurs scores d'intelligence. De plus, plusieurs corrélations ont été trouvées entre les métriques de graphe d'intégration suggérant que les performances de l'intelligence peuvent être liées à une organisation homogène des réseaux. Ces résultats ont démontré que le substrat neuronal de l'intelligence repose sur une microarchitecture de la substance blanche de forte densité et sur une organisation homogène des réseaux. Cette population a finalement été étudiée par IRMf avec une tâche de mémorisation de mots. Des changements significatifs ont été observés entre les groupes HIQ et SIQ. Cette étude confirme notre hypothèse selon laquelle les deux profils HIQ sont caractérisés par une activité cérébrale différente, avec un effet plus prononcé chez les enfants Het-HIQ. Enfin, nous avons étudié la connectivité fonctionnelle et structurale dans une population d’adultes HIQ. Nous avons trouvé plusieurs corrélations entre les métriques de graphe et les autres indices du QI. De même que pour la population d’enfants, les capacités cognitives élevées des adultes sont corrélées à une organisation homogène des réseaux structurels et fonctionnels et une modularité réduite. En conclusion, on a démontré que la sensibilité des métriques de graphes basées sur des techniques 'IRM avancées et de connectivité, telles que l’IRMf-rs et l'IRMd, était très utile pour mieux caractériser les réseaux cérébraux des enfants et des adultes, ainsi que pour distinguer différents profils d'intelligence chez les enfants
The idea that intelligence is embedded not only in specific brain regions, but also in efficient brain networks has grown up. Indeed, human brain organization is believed to rely on complex and dynamic networks in which the communication between cerebral regions guarantees an efficient transfer of information. These recent concepts have led us to explore the neural bases of intelligence using both advanced MRI techniques in combination with graph analysis. On one hand, advanced MRI techniques, such as resting-state functional MRI (rs-fMRI) and diffusion MRI (dMRI) allow the exploration of respectively the functional and the structural brain connectivity while on the other hand, graph theory models allow the characterization of brain networks properties at different scales, thanks to global and local metrics. The aim of this thesis is to characterize the topology of functional and structural brain networks in children and in adults with an intelligence quotient higher (HIQ) than standard levels (SIQ). First, we focused our attention on a children population with different cognitive characteristics. Two HIQ profiles, namely homogeneous (Hom-HIQ) and heterogeneous HIQ (Het-HIQ), have been defined based on clinical observations and Intelligence Quotient (IQ) sub-tests. Using resting-state fMRI techniques, we examined the functional network topology changes, estimating the "hub disruption index", in these two HIQ profiles. We found significant topological differences in the integration and segregation properties of brain networks in HIQ compared to SIQ children, for the whole brain graph, for each hemispheric graph, and for the homotopic connectivity. These brain networks changes resulted to be more pronounced in Het-HIQ subgroup. Finally, we found significant correlations between the graph networks’ changes and the full-scale IQ, as well as some intelligence subscales. These results demonstrated for the first time, that different HIQ profiles are related to a different neural substrate organization. Then, the structural brain network connectivity, measured by dMRI in all HIQ children, were significantly different than in SIQ children. Also, we found strong correlations between the children brain networks density and their intelligence scores. Furthermore, several correlations were found between integration graph metrics suggesting that intelligence performances are probably related to a homogeneous network organization. These findings demonstrated that intelligence neural substrate is based on a strong white matter microarchitecture of the major fiber-bundles and a well-balanced network organization between local and global scales. This children population was finally studied using a memory-word task of fMRI. Significant changes were observed between both HIQ and SIQ groups. This study confirms our hypothesis that both HIQ profiles are characterized by a different brain activity, with stronger evidences in Het-HIQ children. Finally, we investigated both functional and structural connectivity in a population of adults HIQ. We found several correlations between graph metrics and intelligence sub-scores. As well as for the children population, high cognitive abilities of adults seem to be related brain structural and functional networks organization with a decreased modularity. In conclusion, the sensitivity of graph metrics based on advanced MRI techniques, such as rs-fMRI and dMRI, was demonstrated to be very helpful to provide a better characterization of children and adult HIQ, and further, to distinguish different intelligence profiles in children

Human structural and functional brain architecture is increasingly studied by applying the mathematical framework of complex networks to data from magnetic resonance imaging. Connections (edges) in such brain networks are commonly constructed using correlations of features between pairs of brain regions, such as regional morphology (across participants) or neurophysiological time series (within participants). Subsequent analyses frequently focus on summary network statistics calculated using the strongest correlations, but often neglect potential underlying shifts within the correlation distribution. This thesis presents methods for the construction and analysis of correlation-derived structural and functional brain networks, focusing on the implications of changes within the correlation distribution. First, schizophrenia is considered as an example disease which is known to present a reduction in mean correlation between regional neurophysiological time series. Previous studies reported increased network randomisation in schizophrenia, but these results may have been driven by inclusion of a greater number of noisy edges in patients’ networks, based on retention of a fixed proportion of the strongest edges during network thresholding. Here, a novel probabilistic thresholding procedure is applied, based on the realisation that the strongest edges are not necessarily most likely to be true following adjustment of edge probabilities for effects of participant in-scanner motion. Probabilistically thresholded functional networks show decreased randomness, and increased consistency across participants. Further, applying probabilistic thresholding eliminates increased network randomisation in schizophrenia, supporting the hypothesis that previously reported group differences originated in the application of standard thresholding approaches to patient networks with decreased functional correlations. Subsequently, healthy adolescent development is studied, to help understand the frequent emergence of psychiatric disorders in this period. Importantly, both structural and functional brain networks undergo maturational shifts in correlation distribution over adolescence. Due to reliance of structural correlation networks on a group of subjects, previous studies of adolescent structural network development divided groups into discrete age-bins. Here, a novel sliding-window method is used to describe adolescent development of structural correlation networks in a continuous manner. Moreover, networks are probabilistically thresholded by retaining edges that are most consistent across bootstrapped samples of participants, leading to clearer maturational trajectories. These structural networks show non-linear trajectories of adolescent development driven by changes in association cortical areas, compatible with a developmental process of pruning combined with consolidation of surviving connections. Robustness of the results is demonstrated using extensive sensitivity analyses. Finally, adolescent developmental changes in functional network architecture are described, focusing on the characterisation of unthresholded (fully weighted) networks. The distribution of functional correlations presents a non-uniform shift over adolescence. Initially strong cortical connections to primary sensorimotor areas further strengthen into adulthood, whereas association cortical and subcortical edges undergo a subtler reorganisation of functional connectivity. Furthermore, individual subcortical regions show distinct maturational profiles. Patterning of maturation according to known functional systems is affirmed by partitioning regions developing at similar rates into maturational modules. Taken together, this thesis comprises novel methods for the characterisation of disease-related and normative developmental changes in structural and functional correlation brain networks. These methods are generalizable to a wide range of scenarios, beyond the specific disease and developmental age-ranges presented herein.

La présente thèse vise à construire l'histoire du neurone autour de la problématique de la connexion ainsi qu'à analyser, d'une part, de façon chronologique, l'évolution de la notion de connexion au cours de l'histoire du neurone, à partir de l'identification anatomique et fonctionnelle de la cellule nerveuse jusqu'à la simulation du cerveau ; d'autre part, de façon synchronique, les différentes notions de connexion selon les contextes et les sujets d'étude spécifiques. Et la notion de connexion est toujours étudiée par rapport à ses opposées, la séparation et la division, qui sont elles aussi pourvues de sens variés selon les champs de recherche et à travers le temps
This doctoral thesis aims to build the history of the neuron around the problematique of the connection and to analyze, first, chronologically, evolution of the concept of connection inthe history of the fleuron, from anatomical and functional identification of the nerve tell to brain simulation; secondly, synchronically, various concepts of connection according to specific contexts and subjects of study. And the concept of connection is always studied in relation to its opposites, separation and division, which are also provided with different meanings in different research fields and through time

Ce travail a été développé dans le cadre du projet européen FACETS-ITN, dans le domaine des Neurosciences Computationnelles. Son but est d'améliorer la compréhension des réseaux de neurones stochastiques de taille finie, pour des sources corrélées à caractère aléatoire et pour des matrices de connectivité biologiquement réalistes. Ce résultat est obtenu par l'analyse de la matrice de corrélation du réseau et la quantification de la capacité de codage du système en termes de son information de Fisher. Les méthodes comprennent diverses techniques mathématiques, statistiques et numériques, dont certaines ont été importés d'autres domaines scientifiques, comme la physique et la théorie de l'estimation. Ce travail étend de précédents résultats fondées sur des hypothèses simplifiées qui ne sont pas réaliste d'un point de vue biologique et qui peuvent être pertinents pour la compréhension des principes de travail liés cerveau. De plus, ce travail fournit les outils nécessaires à une analyse complète de la capacité de traitement de l'information des réseaux de neurones, qui sont toujours manquante dans la communauté scientifique.

Si les régions cérébrales du langage ont étélargement explorées grâce à l’IRM fonctionnelle (IRMf) et la stimulation électrique directe (SED)peropératoire, leur connectivité reste encore incomplètement documentée. Il n’est pas seulement débattuquels faisceaux de SB contribuent au langage, mais également quelle est leur anatomie précise et leur rôlefonctionnel spécifique. Une meilleure compréhension du connectome du langage est requise pourdiminuer la morbidité postopératoire en neurochirurgie et développer de nouveaux traitements cibléspour la rééducation des aphasies. Notre objectif était de cartographier structurellement etfonctionnellement, in vivo, la connectivité du langage. Dans une première étude préclinique portant sur 2Oadultes sains, nous avons combiné des informations structurelles axonales révélées par la tractographieavec des informations fonctionnelles corticales dérivées de l’IRMf (tâche de lecture compréhensive). Huitfaisceaux de SB ont été explorés —i.e. faisceau arqué, faisceau longitudinal supérieur, faisceau frontooccipitalinférieur, faisceau unciné, faisceau longitudinal inférieur, faisceau longitudinal moyen, faisceauoperculo-prémoteur, faisceau frontal transverse—, dont le rôle fonctionnel a été analysé en recherchantune connexion entre leurs terminaisons corticales et les activations IRMf. Les caractéristiquesanatomiques des faisceaux (i.e. volume, longueur, terminaisons corticales), leurs asymétries interhémisphériqueset leurs variations interindividuelles ont été colligées. Ce protocole a permis deconstruire le connectome du langage et d’étudier en détails son organisation structurelle macroscopique.Dans une seconde partie, ces données ont été transposées à la clinique pour le traitement chirurgical depatients souffrant de tumeurs cérébrales (gliomes) en régions du langage. Pendant la résection tumorale,des images de tractographie intégrées à un système de neuronavigation ont été systématiquementcombinées à la SED au cours d’un test de dénomination orale d’images. Ce protocole opératoire a permisd’optimiser les résultats chirurgicaux en termes de qualité d’exérèse et de préservation du langage, et aconstitué une opportunité unique d’étudier en temps réel les corrélations structure – fonction. Encouplant la localisation anatomique précise où chaque SED a été délivrée —obtenue grâce aux images detractographie naviguées— et la sémiologie des paraphasies induites par la SED —colligée par unorthophoniste présent au bloc opératoire—, nous avons déterminé le rôle spécifique de 5 faisceaux tantcortico-corticaux (faisceau arqué, faisceau fronto-occipital inférieur, faisceau frontal transverse) quecortico-sous-corticaux (fibres prémotrices orofaciales, faisceau fronto-striatal) dans différentes souscomposantesdu langage, i.e. traitement phonologique, traitement sémantique, contrôle moteur,planification articulatoire, contrôle exécutif/cognitif de la réponse verbale. Considérés de façon globale,nos résultats permettent d’envisager une meilleure compréhension de l’organisation anatomofonctionnelledes réseaux cérébraux du langage. Au-delà de l’intérêt scientifique, la possibilité deconstruire le connectome du langage spécifique à chaque individu ouvre la voie vers d’importantesapplications en neurochirurgie, dans une perspective de médecine personnalisée. Aujourd’hui, la chirurgiedes tumeurs cérébrales guidée par l’image. Demain, le développement de nouveaux traitements pour larééducation des aphasies, e.g. la déposition ciblée d’agents pharmacologiques, de cellules souches ou deneuromodulations, interagissant directement avec la connectivité résiduelle épargnée par la lésion
The langage connectome is defined as the neuronal networks that subserve languagefunctions. Anatomically, it comprises specialized cortical areas and modulatory subcortical areas (i.e. deepgray nuclei and cerebellum), as well as their interconnections trough white matter (WM) fascicles.Although brain regions involved in language have been largely explored thanks to functional MRI (fMRI)and intraoprative electrical stimulation (IES), the underlying WM connectivity is still not mastered. It isnot only unknown which WM fascicles specifically contribute to language, but there is also much debateabout their precise anatomy and the functions they subserve during language processing. Betterunderstanding of the structural and functional organization of the language connectome is requisite toreduce postoperative morbidity in neurosurgery and develop targeted treatments for aphasiarehabilitation. Herein, our objective was to map structurally and functionally, in vivo, the subcorticalconnectivity of language. First, we conducted a preclinical study in 20 healthy subjects, combining DTItractography and fMRI (reading comprehension task) to yield connectivity associated with language. Weexplored 8 WM fascicles that have been proposed as putative candidates for language —i.e. arcuatefascicle, superior longitudinal fascicle, inferior fronto-occipital fascicle, uncinate fascicle, inferiorlongitudinal fascicle, middle longitudinal fascicle, operculopremotor fascicle, frontal aslant tract—, towhich we assigned functionality by tracking their connections to the fMRI-derived clusters. We generateda normative database of anatomical characteristics for each WM fascicle, such as volume, length, corticalterminations and their interhemispheric and interindividual variations. By using this construct, weprovided in explicit details the structural map of the language connectome. Second, this body ofknowledge was transposed to brain tumor surgery. Patients suffering of gliomas located close to languageregions were operated on under local anesthesia (i.e. awake surgery) in order to perform intraoperativelanguage mapping (object naming task). Essential language sites were localized through IES andanatomically characterized thanks to navigated tractography images. This intraoperative protocol allowedmaximum tumor resection while preserving language functions. Furthermore, it gave us a uniqueopportunity to perform reliable, real-time structure – function relationships, determining the role of 5WM fascicles (arcuate fascicle, inferior fronto-occipital fascicle, frontal aslant tract, orofacial premotorfibers, frontostriatal fascicle) in different subcomponents of language, i.e. phonological processing,semantic processing, articulatory planning, motor control and executive/cognitive control of verbalresponse. Globally considered, our results allow a better understanding of the anatomo-functionalorganization of the language network in the human brain. Beyond the scientific interest, the possibility toconstruct the individual (patient-specific) connectome paves the way for major applications inneurosurgery, in the perspective of personalized medicine. Today, the maximum safe resection of braintumors located in eloquent language areas, guided by navigated, multimodal images. Tomorrow, thedevelopment of new treatments for rehabilitation of post-stroke aphasia patients, such as the targeteddelivery of drugs, stem cells, or neuromodulation devices, fitting with the residual functional connectivityspared by the lesion

Drosophila larvae present unique opportunity for anatomical and functional mapping of their nervous system because of features such as numerical simplicity of neurons its nervous system is composed of, and ability to exhibit quantifiable behaviors such as chemotaxis. Here, we mapped entire antennal lobe of larval Drosophila with one of its circuits responsible for controlling sensorimotor transformation in lateral horn (LH) (higher brain) through a single brain descending neuron using electron microscopic 3D reconstruction. In antennal lobe, we reported a canonical circuit with uniglomerular projection neurons (uPNs), working to relay gain-controlled ORN activity to higher brain centers like Mushroom body and lateral horn. We also found a parallel circuit with multiglomerular projection neurons (mPNs) and hierarchically organized local neurons (LNs) selectively integrating signal from multiple ORNs at the first synapse with LN-LN connectivity putatively implementing gain control mechanism that can potentially switch from computing distinguished odor signals through panglomerular inhibition to allowing system to respond to faint aversive odor in an environment rich with strong appetitive odors. We also reconstructed and studied one of the olfactory connected circuits in the LH that was found to be influencing chemotaxis behavior in larva through a single brain descending neuron, PVM027. We found that this neuron was responsible in controlling stop response of chemotaxis behavior. EM reconstruction revealed its connection with variety of motor systems and SEZ descending neurons in the VNC. Connections were revealed with the peristaltic wave propagation circuit of larva, and PVM027 was found to be implementing stop by terminating and ceasing the origin of forward peristaltic waves.
Las larvas de Drosophila ofrecen una oportunidad única para el mapeo anatómico y funcional de su sistema nervioso debido a propiedades como la simplicidad numérica de neuronas que componen su sistema nervioso y su habilidad de exhibir comportamientos cuantificables como la quimiotaxis. En este estudio hemos mapeado el lóbulo antenal de la larva de Drosophila con uno de sus circuitos responsable de controlar la transformación sensorial-motora en el asta lateral (LH) (cerebro superior) a través de una sola neurona descendiente usando la reconstrucción 3D para microscopia electrónica. Hemos presentado, en el lóbulo antenal, un circuito canónico con proyecciones neuronales uniglomerulares (uPNs) responsables de transmitir aumentos controlados de actividad desde sus ORN* hasta centros superiores del cerebro como el cuerpo fungiforme y el asta lateral del protocerebro. Hemos descubierto también un circuito paralelo formado por neuronas con proyecciones multiglomerulares (mPNs) y neuronas locales (Lns), organizadas jerárquicamente, que integran selectivamente señales desde múltiples ORNs a nivel de primera sinapsis con conectividad LN-LN implementando aparentemente un mecanismo de aumento de control que potencialmente puede intercambiar señales olfativas distintas computacionalmente a través de inhibición panglomerular permitiendo al sistema responder a olores vagamente aversivos en un ambiente rico en fuertes olores apetitosos. También hemos reconstruido y estudiado uno de los circuitos olfativos que conectan con el LH conocido por influenciar la quimiotaxis de la larva a través de un sola neurona cerebral descendiente, la PVM027. Hemos descubierto que dicha neurona es la responsable de controlar la respuesta stop en el comportamiento de quimiotaxis. La reconstrucción por EM revela su conexión con una variedad de sistemas motores así como neuronas descendientes SEZ en el VNC. Observamos dichas conexiones gracias al circuito de propagación de onda peristáltica de la larva, y descubrimos que la PVM027 implementa la señal de stop terminando e interrumpiendo el origen de la onda peristáltica.

The human brain is a complex network that shares and processes information by using the structural paths between areas in order to perform a function. The connectome models the brain as a graph where nodes correspond to brain regions and edges to structural or functional connections. In this thesis, we investigate and provide new methods to study the brain complexity and improve the understanding of the brain functioning by using information theory. Firstly, we focus on brain parcellation, which is a key step to perform brain studies since determines the regions to be analyzed. Secondly, we focus on the definition of measures to characterize the complexity of the brain networks. Finally, the consistency of the results across healthy subjects using functional or structural connectivity data, demonstrates the flexibility and robustness of the proposed methods
El cervell humà és una xarxa complexa que comparteix i processa la informació mitjançant els camins estructurals per tal de realitzar una funció. El connectoma és una representació del cervell en forma de graf, on els nodes corresponen a regions del cervell i les arestes a connexions estructurals o funcionals. En aquesta tesi, s'investiga i es proporcionen nous mètodes per estudiar la complexitat del cervell i millorar la comprensió del seu funcionament mitjançant l'ús de la teoria de la informació. En primer lloc, ens centrem en mètodes de parcel.lació del cervell, el qual és un pas clau per realitzar estudis de complexitat ja que determina les regions a analitzar. En segon lloc, ens centrem en la definició de mesures per a caracteritzar la complexitat de les xarxes cerebrals. Finalment, la consistència dels resultats entre els subjectes sans a partir de dades de connectivitat funcional o estructural, demostra la flexibilitat i robustesa dels mètodes proposats

Complex network models of functional connectivity have emerged as a paradigm shift in brain mapping over the past decade. Despite significant attention within the neuroimaging and cognitive neuroscience communities, these approaches have hitherto not been extensively explored in neurosurgery. The aim of this thesis is to investigate how the field of connectomics can contribute to understanding the effects of focal brain lesions and to functional brain mapping in neurosurgery. This datasets for this thesis include a clinical population with focal brain tumours and a cohort focused on healthy adolescent brain development. Multiple network analyses of increasing complexity are performed based upon resting state functional MRI. In patients with focal brain tumours, the full complement of resting state networks were apparent, while also suggesting putative patterns of network plasticity. Connectome analysis was able to identify potential signatures of node robustness and connections at risk that could be used to individually plan surgery. Focal lesions induced the formation of new hubs while down regulating previously established hubs. Overall these data are consistent with a dynamic rather than a static response to the presence of focal lesions. Adolescent brain development demonstrated discrete dynamics with distinct gender specific and age-gender interactions. Network architecture also became more robust, particularly to random removal of nodes and edges. Overall these data provide evidence for the early vulnerability rather than enhanced plasticity of brain networks. In summary, this thesis presents a combined analysis of pathological and healthy development datasets focused on understanding the functional effects of focal brain lesions at a network level. The coda serves as an introduction to a forthcoming study, known as Connectomics and Electrical Stimulation for Augmenting Resection (CAESAR), which is an evolution of the results and methods herein.

Les crises épileptiques sont des épisodes paroxysmiques d'activité cérébrale hypersynchrone. Ce travail de thèse s'attache à examiner les mécanismes de propagation des crises d'épilepsie sur une échelle temporelle lente et une grande échelle spatiale dans le cerveau humain et à les appliquer au contexte clinique. Chez les patients souffrant d'épilepsie partielle réfractaire, les crises débutent dans certaines régions localisées du cerveau, dénommées zone épileptogène, avant de recruter des régions distantes. Le succès de l'ablation chirurgicale de la zone epileptogène dépend principalement de sa délimitation adéquate, un problème souvent épineux en pratique clinique. À cela s'ajoute notre compréhension parcellaire des mécanismes à l'origine des crises et de leur propagation. Nous utilisons un modèle mathématique de masse neuronale reproduisant le décours temporel de l'activité moyenne critique et intercritique d'une région cérébrale, guidé de manière autonome par une variable permittive lente. Nous introduisons tout d'abord un couplage permittif lent entre ces masses neuronales, afin de révéler l'importance de la variété lente dans le recrutement des régions cérébrales dans la crise. Nous présentons ensuite un pipeline de traitement des données structurelles et de diffusion IRM pour reconstruire automatiquement le cerveau virtuel d'un patient. Nous utilisons ensuite une analyse de stabilité linéaire et la connectivité large-échelle pour prédire la zone de propagation. Nous appliquons notre méthode à un jeu de données de 15 patients épileptiques et démontrons l'importance du connectome pour prédire la direction de propagation des crises
Epileptic seizures are paroxysmal hypersynchronizations of brain activity, spanning several temporal and spatial scales. In the present thesis, we investigate the mechanisms of epileptic seizure propagation on a slow temporal and large spatial scale in the human brain and apply them to a clinical context. For patients with partial refractory epilepsy, seizures arise from a localized region of the brain, the so-called epileptogenic zone, before recruiting distant regions. Success of the resective surgery of the epileptogenic zone depends on its correct delineation, which is often difficult in clinical practice. Furthermore, the mechanisms of seizure onset and recruitment are still largely unknown. We use a mathematical neural mass model to reproduce the time course of interictal and ictal mean activity of a brain region, in which the switching between these states is guided by an autonomous slow permittivity variable. We first introduce a slow permittivity coupling function between these neural masses, hypothesizing the importance of the slow manifold in the recruitment of brain regions into the seizure. Before exploring large-scale networks of such coupled systems, we present a processing pipeline for automatic reconstruction of a patient's virtual brain, including surface and connectivity (i.e., connectome), using structural and diffusion MRI, and tractography methods. Using linear stability analysis and large-scale connectivity, we predict the propagation zone. We apply our method to a dataset of 15 epileptic patients and establish the importance of the connectome in determining large-scale propagation of epileptic seizures

Diffusion magnetic resonance imaging (dMRI) allows uniquely the study of the human brain non-invasively and in vivo. Advances in dMRI offer new insight into tissue microstructure and connectivity, and the possibility of investigating the mechanisms and pathology of neurological diseases. The great potential of the technique relies on indirect inference, as modelling frameworks are necessary to map dMRI measurements to neuroanatomical features. However, this mapping can be computationally expensive, particularly given the trend of increasing dataset sizes and/or the increased complexity in biophysical modelling. Limitations on computing can restrict data exploration and even methodology development. A step forward is to take advantage of the power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally intensive scientific problems that were intractable before. However, they are not inherently suited for all types of problems, and bespoke computational frameworks need to be developed in many cases to take advantage of their full potential. In this thesis, we propose parallel computational frameworks for the analysis of dMRI using GPUs within different contexts. We show that GPU-based designs can offer accelerations of more than two orders of magnitude for a number of scientific computing tasks with different parallelisability requirements, ranging from biophysical modelling for tissue microstructure estimation to white matter tractography for connectome generation. We develop novel and efficient GPUaccelerated solutions, including a framework that automatically generates GPU parallel code from a user-specified biophysical model. We also present a parallel GPU framework for performing probabilistic tractography and generating whole-brain connectomes. Throughout the thesis, we discuss several strategies for parallelising scientific applications, and we show the great potential of the accelerations obtained, which change the perspective of what is computationally feasible.

Electrical connectors are the blocks that connect solar panels together. Whenever a photovoltaic plant commences, the main discussion goes around on solar panels, inverters, charge controllers, etc. But the topic of connectors is usually hardly discussed. Connectors in a photovoltaic system can definitely contribute to improve the overall performance of the system, provided that importance is given while selecting the connectors. The electrical connectors used in photovoltaic systems can be connected in two possible ways. Connectors can be connected either in a pure-connection or in a cross-connection. Male and female connectors from the same brand results a pure-connection (P-C). Male and female connectors from two different brands results in a cross-connection (C-C). There have been discussions in photovoltaic, electrical connector markets and international solar events regarding the risks involved, losses and consequences due to a cross-connection. The main reason behind cross-connections is the unawareness of the installers in knowing the difference between a pure-connection and a cross-connection. Even though the installers are aware of this difference, they are not aware of the consequences of cross-connections. Multi-Contact, a leading electrical connector manufacturer of MC4 photovoltaic connectors affected by the counterfeit products of MC4, due to the sudden boom in the solar market during 2011-12. With the help of TÜV Rheinland, Multi-Contact conducted couple of tests namely temperature increase test and accelerated stress tests to understand the disadvantages of cross-connections. This thesis tried to replicate the tests performed by Multi-Contact in an attempt to understand the test results by using connectors that are used in the Swedish market. Performing temperature increase test and accelerated stress tests on most commonly used connectors in the Swedish market is the main aim of this thesis. The first test, gives an understanding of the temperature variations across various connector sets (four connector sets from various manufacturers used in this thesis) and the latter tests helps to understand the quality of the contact resistance of these connector sets. The four connector set manufacturers used in this test were Multi-Contact (MC), Weidmüller (WM), Blussun solar (BSS) and PBM. The quality of contact resistance of a connector is directly related to the quality of the connector set. During the 20 minutes of the temperature increase test, the connector set from WM performed better than its competitors in the P-C. Whereas, the MC-BSS connector set had performed well in the C-C. The connector type of male MC and female BSS showed its dominance throughout the test. Unfortunately, no conclusions were able to be drawn from this test results due to insufficient information about the test procedure. From the results of accelerated stress tests, the C-C set from MC outperformed its P-C counterpart. All ten connector sets used in this project passed the standard and qualified as connectors with good quality contact resistance. Therefore the best results out of only a P-C connector set does not seems to be completely true. With the standard used in this thesis, it is quite difficult to judge the quality of connectors. Rather than saying a P-C is superior and a C-C is inferior in terms of quality, there is a need to come up with a new method to evaluate the quality of connectors. Matching the connectors based on their tolerances could be a potential solution to the mismatching problem in connectors.

Il cervello si può definire un network complesso in cui delle regioni sono interconnesse fra loro. L'imaging di risonanza magnetica pesato in diffusione (DWI) insieme alla trattografia, permettono di ricostruire i fasci di fibre assonali di sostanza bianca indagando la connettività strutturale tra le aree di sostanza grigia. Il "connettoma" risultante può essere analizzato e caratterizzato attraverso la graph theory. Il lavoro che ho sviluppato presso l'unità di RM funzionale del Policlinico S. Orsola-Malpighi, e il DIBINEM, Università di Bologna, si propone di ricostruire il connettoma tramite due diversi metodi trattografici probabilistici confrontando i risultati ottenuti da acquisizioni DWI con diverso numero di direzioni del gradiente di diffusione (NDGD), ma con rapporto segnale rumore (SNR) costante. È stata effettuata un’acquisizione a 66 e tre a 22-NDGD per 18 soggetti sani. Le scansioni a 22-NDGD sono state mediate fra loro per ottenere un SNR comparabile con le 66-NDGD (22avg) e poter confrontare correttamente i diversi NDGD. Questo tipo di analisi non è ancora presente in letteratura. Dopo aver segmentato il cervello in diverse aree è stata effettuata la trattografia, tramite gli algoritmi PROBTRACKX2 e iFOD2, per costruire un network pesato del connettoma. Abbiamo effettuato misure locali e globali sui network e analizzato le proprietà di small-world e l'organizzazione modulare. Tali misure sono state confrontate fra i diversi NDGD e algoritmi trattografici. Si è visto come PROBTRACKX2 risulti più sensibile alle variazioni del SNR nel confronto dei network a 22 e 22avg. Per entrambi gli algoritmi sono state misurate differenze significative fra i network a 66 e a 22avg suggerendo che l'aumento della risoluzione angolare influenza fortemente le proprietà del network. In particolare, a livello locale si evidenzia un'alterazione delle misure nodo-specifiche nelle zone della sostanza grigia profonda e nell'area fronto temporale, per entrambi gli algoritmi.

MEG and fMRI offer complementary insights into connected human brain function. Evidence from the use of both techniques in the study of networked activity indicates that functional connectivity reflects almost every measurable aspect of human reality, being indicative of ability and deteriorating with disease. Functional network analyses may offer improved prediction of dysfunction and characterisation of cognition. Three factors holding back progress are the difficulty in synthesising information from multiple imaging modalities; a need for accurate modelling of connectivity in individual subjects, not just average effects; and a lack of scalable solutions to these problems that are applicable in a big-data setting. I propose two methodological advances that tackle these issues. A confound to network analysis in MEG, the artificial correlations induced across the brain by the process of source reconstruction, prevents the transfer of connectivity models from fMRI to MEG. The first advance is a fast correction for this confound, allowing comparable analyses to be performed in both modalities. A comparative study demonstrates that this new approach for MEG shows better repeatability for connectivity estimation, both within and between subjects, than a wide range of alternative models in popular use. A case-study analysis uses both fMRI and MEG recordings from a large dataset to determine the genetic basis for functional connectivity in the human brain. Genes account for 20% - 65% of the variation in connectivity, and outweigh the influence of the developmental environment. The second advance is a Bayesian hierarchical model for sparse functional networks that is applicable to both modalities. By sharing information over a group of subjects, more accurate estimates can be constructed for individuals' connectivity patterns. The approach scales to large datasets, outperforms state-of-the-art methods, and can provide a 50% noise reduction in MEG resting-state networks.

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.

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

How does a nervous system orchestrate compound behaviors? Finding the neural basis of behavior requires knowing which neurons control the behavior and how they are connected. To accomplish this we measured and manipulated neural activity in a live, behaving animal with a completely defined connectome. The C. elegans escape response is a compound behavior consisting of a sequence of behavioral motifs. Gentle touch induces a reversal and suppression of head movements, followed by a deep turn allowing the animal to navigate away from the stimulus. The connectome provides a framework for the neural circuit that controls this behavior. We used optical physiology to determine the activity patterns of individual neurons during the behavior. Calcium imaging of locomotion interneurons and motor neurons reveal unique activity profiles during different motifs of the escape response. Furthermore, we used optogenetics and laser ablations to determine the contribution of individual neurons to each motif. We show these that the suppression of head movements and turning motifs are distinct motor programs and can be uncoupled from the reversal. The molecular mechanisms that regulate these motifs involve from signaling with the neurotransmitter tyramine. Tyramine signaling and gap junctions between locomotion interneurons and motor neurons regulate the temporal orchestration of the turning motif with the reversal. Additionally, tyramine signaling through a GPCR in GABAergic neurons facilitates the asymmetric turning during forward viii locomotion. The combination of optical tools and genetics allows us to dissect a how a neural circuit converts sensory information into a compound behavior.