Orientador: Gabriela Castellano / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas / Made available in DSpace on 2018-08-24T17:19:19Z (GMT). No. of bitstreams: 1 Pereira_FabricioRamosSilvestre_D.pdf: 32802688 bytes, checksum: 367566b29c460e1b33f48f861845217a (MD5) Previous issue date: 2013 / 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

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.

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.

The default mode network (DMN) is a highly cited neural network whose functional roles are not well understood. Until recently, event related fMRI experiments used to study the DMN could only be conducted in an open-loop format. The purpose of this study was to demonstrate the potential statistical advantages of real-time fMRI studies to conduct closed-loop experiments to directly test putative DMN functions. Using both fMRI simulations and large archival datasets, we demonstrate that open-loop designs are less statistically powerful than closed-loop experiments that can trigger stimuli at controlled levels of brain activity. When simulating event scheduling on resting state data, DMN levels were normally distributed, but the event timing proved to be ineffective in capturing the highest and lowest DMN values on average across subjects. Statistical differences in DMN levels collected by the Human Connectome Project-Aging (HCP-A) during a Go/NoGo task were also reported, along with the network's distributional effects across subjects. When examining DMN levels in 136 subjects more prone to commission errors the mean DMN levels were reported to be higher during and prior to incorrect NoGo responses. Exploring DMN levels in these same individuals reacting to a Go task also revealed differing measurement patterns when compared to all 711 subjects in the study. Additionally, the distribution of total DMN levels across all participants, as well as during a Go or NoGo trial, showed a shift in the mean towards deactivation. Furthermore, the peak at this location was greater and revealed that increased sampling occurred at the mean and under sampling at the tails. Overall, the cumulative findings in this study were successful in providing statistical arguments to support propositions for more powerful closed-loop experimentation in fMRI. / Master of Science / Activity in a neural network is observed through the use of functional MRI (fMRI) by tracking higher levels of oxygenated blood to that region when active and lower quantities when inactive. Neural networks vary in their responsibilities, thus fMRI tasks are designed to trigger a response based on the functional role of the network. This can be exemplified by studying the blood flow to default mode network (DMN), a network responsible for mind wandering, during a task that requires focus. Researchers can then correlate moments of high activity, which indicates a greater degree of mind wandering, or low activity to a correct or incorrect response to the task. Unfortunately, the timing in which a task is presented to the participant is predetermined prior to the subject entering the MRI making it difficult to capture a correct or incorrect response at the precise moment of activation or deactivation. This concept is known as open-loop and often collects data at moments of neutral activity, neither high nor low. In contrast, a closed-loop design allows a researcher to monitor the DMN's activation levels in real time and present the task at a desired time. This provides more useful data to the experimenter as all recorded responses to the task correlate with exact moments of high and low activation. This makes claims about the neural network's role statistically more powerful as there is a greater quantity of data at these moments rather than during a neutral activation state. The purpose of this thesis is to provide statistical arguments that support propositions for more powerful closed-loop experimentation in fMRI.

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).

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.

Connectomics – the study of neural connectivity – is primarily concerned with the mapping and characterisation of wired synaptic links; however, it is well established that long-distance chemical signalling via extrasynaptic volume transmission is also critical to brain function. As these interactions are not visible in the physical structure of the nervous system, current approaches to connectomics are unable to capture them. This work addresses the problem of missing extrasynaptic interactions by demonstrating for the first time that whole-animal volume transmission networks can be mapped from gene expression and ligand-receptor interaction data, and analysed as part of the connectome. Complete networks are presented for the monoamine systems of Caenorhabditis elegans, along with a representative sample of selected neuropeptide systems. A network analysis of the synaptic (wired) and extrasynaptic (wireless) connectomes is presented which reveals complex topological properties, including extrasynaptic rich-club organisation with interconnected hubs distinct from those in the synaptic and gap junction networks, and highly significant multilink motifs pinpointing locations in the network where aminergic and neuropeptide signalling is likely to modulate synaptic activity. Thus, the neuronal connectome can be modelled as a multiplex network with synaptic, gap junction, and neuromodulatory layers representing inter-neuronal interactions with different dynamics and polarity. This represents a prototype for understanding how extrasynaptic signalling can be integrated into connectomics research, and provides a novel dataset for the development of multilayer network algorithms.

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.

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

L'activité neuronale partagée entre les différentes régions cérébrales permet d'estimer les patrons d'activation fonctionnelle à l'échelle de réseaux distribués, même en l'absence de paradigme. Constamment rapportés dans la population saine, les réseaux fonctionnels au repos (RSNs) peuvent être utilisés comme objet d'étude pour comprendre la contribution du développement sensoriel atypique sur la communication globale inter-réseau. À ce jour, peu d'études ont exploré l'organisation cérébrale au repos dans le contexte de la surdité. Pourtant, de multiples évidences soutiennent l'importance des entrées sensorielles en début de vie dans la consolidation de l'architecture fonctionnelle du cerveau. L'étude présentée dans ce mémoire a été développée et conceptualisée pour rendre compte de la relation entre la privation sensorielle et l'activité cérébrale spontanée entre les RSNs. À cette fin, 17 personnes avec une surdité congénitale de degré sévère à profond et 18 personnes entendantes non signeurs ont été recrutées et ont effectué 10 minutes d'enregistrement par imagerie magnétique fonctionnelle (IRMf) à l'état de repos. Les estimations de connectivité fonctionnelle de 17 RSNs extraites par une méthode de parcellisation fonctionnelle du cerveau ont été comparées entre les groupes. Le couplage entre les réseaux d'attention dorsale (DAN) et d'attention ventrale (VAN) était significativement plus élevé chez les participants qui présentent une surdité. Ces deux systèmes sont impliqués dans les tâches attentionnelles descendantes (« top-down ») et ascendantes (« bottom-up »), respectivement. Les résultats démontrent une réorganisation du cerveau au sein des réseaux associatifs et proposent une preuve potentielle des substrats neuronaux qui sous-tendraient les performances attentionnelles supérieures des personnes avec une surdité. / Neural activity shared between different brain regions allows estimation of functional activation patterns at the scale of distributed networks, even in the absence of a paradigm. Consistently reported in the healthy population, resting-state functional networks (RSNs) can be studied to understand the contribution of atypical sensory development on global inter-network communication. To date, few studies have explored brain organization at rest in the context of deafness. Yet, numerous evidence supports the importance of early sensory input in the consolidation of the brain's functional architecture. The study presented in this thesis was developed and conceptualized to report on the relationship between sensory deprivation and spontaneous brain activity between RSNs. To this end, 17 individuals with severe to profound congenital hearing loss and 18 non-signer hearing individuals were recruited and performed 10 minutes of functional magnetic imaging (fMRI) recording at rest. Functional connectivity estimates of 17 RSNs extracted by a functional brain parcellation method were compared between groups. The coupling between dorsal attention (DAN) and ventral attention (VAN) networks was significantly higher in deaf participants. These two systems are involved in topdown and bottom-up attentional tasks, respectively. The results demonstrate brain plasticity within associative networks and offer potential evidence of neural substrates that may underlie superior attentional performances observed in individuals with deafness.