The dementias are persistent or progressive disorders affecting more than one cognitive domain that interfere with an individual’s ability to function at work or home, and represent a decline from a previous level of function. In this thesis I consider the neurophysiology of dementia at a number of levels. I investigate the ways in which the connectivity and function of the brain predisposes to the specific focal patterns of neurodegeneration seen in the various dementias. I aim to identify the mesoscopic changes that occur in individuals with neurodegeneration and how these relate to their cognitive difficulties. I show how, by assessing patients in whom there is focal disruption of brain networks and observing the outcomes in comparison to controls, I can gain insight into the mechanisms by which the normal brain makes predictions and processes language. In Chapter 1, I set the scene for the focussed experimental investigations of model diseases by beginning with an introductory, clinically-focussed review that sets out the features, aetiology, management, epidemiology and prognosis of the dementias. This places these model diseases in the context of the broader clinical challenge posed by the dementias. In Chapter 2, I turn to ‘prototypical’ model diseases that represent neurodegenerative tauopathies with predominantly cortical (Alzheimer’s disease, AD) and subcortical (Progressive Supranuclear Palsy, PSP) disease burdens. I investigate the neurophysiological causes and consequences of Tau accumulation by combining graph theoretical analyses of resting state functional MR imaging and in vivo ‘Tau’ PET imaging using the ligand AV-1451. By relating Tau distribution to the functional connectome I provide in vivo evidence consistent with ‘prion-like’ trans-neuronal spread of Tau in AD but not PSP. This provides important validation of disease modification strategies that aim to halt or slow down the progression of AD by sequestration of pathological Tau in the synapse. In contrast, I demonstrate associations consistent with regional vulnerability to Tau accumulation due to metabolic demand and a lack of trophic support in PSP but not AD. With a cross-sectional approach, using Tau burden as a surrogate marker of disease severity, I then go on to show how the changes in functional connectivity that occur as disease progresses account for the contrasting cognitive phenotypes in AD and PSP. In advancing AD, functional connectivity across the whole brain becomes increasingly random and disorganised, accounting for symptomatology across multiple cognitive domains. In advancing PSP, by contrast, disrupted cortico-subcortical and cortico-brainstem interactions meant that information transfer passed through a larger number of cortical nodes, reducing closeness centrality and eigenvector centrality, while increasing weighted degree, clustering, betweenness centrality and local efficiency. Together, this resulted in increasingly modular processing with inter-network communication taking less direct paths, accounting for the bradyphrenia characteristic of the ‘subcortical dementias’. From chapter 3 onwards, I turn to the in-depth study of a model disease called non-fluent variant Primary Progressive Aphasia (nfvPPA). This disease has a clear clinical phenotype of speech apraxia and agrammatism, associated with a focal pattern of mild atrophy in frontal lobes. Importantly, general cognition is usually well preserved until late disease. In chapter 3 itself, I relate an experiment in which patients with nfvPPA and matched controls performed a receptive language task while having their brain activity recorded with magnetoencephalography. I manipulated expectations and sensory detail to explore the role of top-down frontal contributions to predictive processes in speech perception. I demonstrate that frontal neurodegeneration led to inflexible and excessively precise predictions, and that fronto-temporal interactions play a causal role in reconciling prior predictions with degraded sensory signals. The discussion here concentrates on the insights provided by neurodegenerative disease into the normal function of the brain in processing language. Overall, I demonstrate that higher level frontal mechanisms for cognitive and behavioural flexibility make a critical functional contribution to the hierarchical generative models underlying speech perception In chapter 4, I precisely define the sequence processing and statistical learning abilities of patients with nfvPPA in comparison to patients with non-fluent aphasia due to stroke and neurological controls. I do this by exposing participants to a novel, mixed-complexity artificial grammar designed to assess processing of increasingly complex sequencing relationships, and then assessing the degree of implicit rule learning. I demonstrate that agrammatic aphasics of two different aetiologies are not disproportionately impaired on complex sequencing relationships, and that the learning of phonological and non-linguistic sequences occurs independently in health and disease. In chapter 5, I summarise the synergies between the experimental chapters, and explain how I have applied a systems identification framework to a diverse set of experimental methods, with the common goal of defining the physiology of dementia. I then return to the results of chapter 3 with a clinical focus to explain how inflexible predictions can account for subjective speech comprehension difficulties, auditory processing abnormalities and (in synthesis with chapter 4) receptive agrammatism in nfvPPA. Overall, this body of work has contributed to knowledge in several ways. It has achieved its tripartite aims by: 1) Providing in vivo evidence consistent with theoretical models of trans-neuronal Tau spread (chapter 2), and a comprehensive clinical account of the previously poorly-understood receptive symptomatology of nfvPPA (chapter 5), thus demonstrating that systems neuroscience can provide a translational bridge between the molecular biology of dementia and clinical trials of therapies and medications. In this way, I begin to disentangle the network-level causes of neurodegeneration from its consequences. 2) Providing evidence for a causal role for fronto-temporal interactions in language processing (chapter 3), and demonstrating domain separation of statistical learning between linguistic and non-linguistic sequences (chapter 4), thus demonstrating that studies of patients with neurodegenerative disease can further our understanding of normative brain function. 3) Successfully integrating neuropsychology, behavioural psychophysics, functional MRI, structural MRI, magnetoencephalography and computational modelling to provide comprehensive research training, as the platform for a future research programme in the physiology of dementia.

The Diploma thesis concerns creation of the choreographer Meg Stuart. It examines her experimental body work and presents the main physical tendencies in her artistic work. The main aim of this thesis is research of physical exercises and analysis of three performances created between 2000 ? 2011.

Cette thèse traite de problèmes inverses de sources dans deux cas : les sources fixes en 2D et 3D équations elliptiques et une source non-stationnaire dans une équation de diffusion. Dans le cadre de ce travail, nous considérons des sources ponctuelles (monopôles, dipôles et sources multipolaires) et des sources ayant support compact dans un nombre fini de petits sous-domaines qui modèlent les sources dans les problèmes EEG/MEG et le problème de tomographie par bioluminescence (BLT). Le but de cette thèse est de proposer des méthodes d’identification robustes qui permettent de déterminer leur nombre, leurs intensités et leurs positions. Des méthodes algébriques directes sont utilisées pour identifier les sources fixes et une méthode quasi-algébrique mélangée avec un problème d’optimisation est utilisé pour récupérer les sources avec des intensités variables dans le temps. Des résultats numériques sont effectués afin de mettre en évidence la robustesse de nos algorithmes d’identification. / This thesis deals with inverse source problems in 2 cases : stationary sources in 2D and 3D elliptic equations and a non-stationary source in a diffusion equation. the main form of sources considered are pointwise sources (monopoles, dipoles and multipolar sources) having compact support within a finite number of small subdomains modeling EEG/MEG problems and Bioluminescence Tomography (BLT) problems. The purpose o this thesis is mainly to propose robust identification methods that enable us to reconstruct the number, the intensity and the location of the sources. Direct algebraic methods are used to identify the stationary siurces and a quasi-algebraic method mixed with an optimieation method is employed to recover sources with time-variable intensities. Numerical results are shown to prove the robustness of our identification algorithms.

We humans are visual creatures, constantly extracting information from the world around us. The source of our ability to understand the visual world is an intricate arrangement of multiple areas in our brains: the visual system. It enables us to recognize our friends and family in diverse conditions, to focus our attention on important aspects of a scene and performs invariant object categorization on multiple levels of abstraction. Vision has been in the focus of scientific interest for many decades and yet our knowledge of the cortical mechanisms involved is only limited. I here describe a series of experiments, in which we investigated how the visual system robustly and efficiently extracts meaning from the environment. In particular, I will focus on thee aspects of object recognition: sampling the environment, visual invariance, and categorization and plasticity. Starting with the selection of visual information, three eyetracking experiments are described in which we investigate the interplay of overt visual attention and object recognition. We show that overt visual attention and object recognition exert a bi-directional influence on each other. Whereas initial patterns of overt visual attention causally affect the outcome of the later recognition, briefly presented contextual information leads to substantial changes in the attentional sampling behavior, which can be best understood in terms of a shifting exploration-exploitation bias. Following this, we turn to visual processing within the system and ask how invariant object recognition is accomplished despite large variation in retinal input. As an exemplary case, we focus on changes introduced by rotations in depth. Using a variety of techniques, ranging from fMRI to TMS and EEG, we show that viewpoint symmetry, i.e. the selectivity to mirror-symmetric viewing angles, is a prevalent feature of visual processing across a wide range of higher-level visual regions. These findings jointly suggest that viewpoint-symmetry constitutes a key computational step in achieving full viewpoint invariance. On the next level of abstraction, we investigate how visual categories are represented at different levels of experience, from novice to expert. By combining training of novel visual categories with psychophysical measures, we demonstrate a change in the underlying type of category representation. Following this, we combine the training paradigm with electrophysiological measurements. In line with our behavioral results, these data reveal a spatiotemporal shift in category selectivity: from late and frontal to early occipitotemporal activity. These results suggest that novel and re-occurring categories rely on partially separate cortical networks, allowing the brain to balance robust and fast recognition with considerable flexibility and plasticity. The results of all experiments presented are unified by the concept of a system that has evolved efficient mechanisms for robust performance in a large variety of conditions. Using dynamic sampling strategies, computational shortcuts and a division of labor, the visual system is optimally equipped to support higher-level cognitive function in a complex and constantly changing environment.

Sound localisation is one of the most representative function of the auditory system and, as such, it has been extensively investigated across species. Spatial hearing can be dramatically altered across the life span, yet research in humans have highlighted the remarkable capacity of the brain to adapt to changes of listening conditions, such as temporary ear plugging or long lasting hearing impairments. Although several investigations have examined accommodation to altered auditory cues (Chapter 1), a common theoretical framework seems to lack and a number of questions remain open. This limits the possibility to translate our current knowledge into concrete clinical applications for individuals who experience spatial hearing difficulties after hearing loss. The current dissertation reflects the attempt to answer specific questions regarding the process of sound localisation. The first study (Chapter 2) aimed to investigate the relation between different reference frames in spatial hearing, namely egocentric and allocentric sound representation. We studies this topic in the context of a learning paradigm, assessing to what extent localisation of single sounds in simulated monaural hearing (unilateral ear plugging) can improve following an audio-visual spatial hearing training focused on egocentric sound processing vs allocentric sound processing. An untrained group was also included in the study. We found that localisation performance in the horizontal plane improved specifically in the side ipsilateral to the ear-plug for all groups. Yet, the trained groups showed a qualitatively different change of performance after four days of multisensory ego/allocentric training compared to the untrained group, providing initial evidence of the possible role of allocentric coding in acoustic space re-learning. These results further highlight the importance of including a test-retest group in paradigms of sound localisation training. The second study (Chapter 3) focused on a specific aspect of the phenomenological experience of spatial hearing, namely the subjective confidence about the perceived sound position. We examined the relation between objective localisation accuracy and subjective certainty while participants localised sounds in two different listening conditions – binaural or simulated monaural hearing. Results showed that overall subjective certainty on sound position decreased in the altered listening condition (unilateral ear-plugging). In simulated monaural hearing, localisation accuracy and spatial confidence dissociated. For instance, there were trials in which participants were accurate, but felt uncertain, and trials in which they were less accurate but expressed higher ratings of spatial confidence on sound position. Furthermore, subjective confidence increased as a function of time within the testing block, and it was related to the spatial distribution of the perceived sound-source position. The third study (Chapter 4) exploited magnetoencephalography (MEG) to study the dynamics of the cortical network implied in active sound localisation. We implemented a novel apparatus to study sound localisation in MEG with real sounds in external space, and collected behavioural and subjective responses (i.e., accuracy and confidence, as in Study 2) during this altered listening condition. Results showed that participants were able to perceive the spatial difference between the positions of stimulation, thus proving the reliability of our novel setting for the study of spatial hearing in MEG. MEG data highlight a distributed bilateral cortical network involved in active sound localisation, which emerged shortly after stimulus presentation (100—125 ms). The network comprise the classical dorsal auditory pathway plus other cortical regions usually underestimated in previous literature – most notably, regions in the central sulcus/precentral gyrus possibly involved in head movements. Connectivity analysis revealed different patterns of neural coupling, as a function of frequency band. In particular, coherence in high gamma revealed significant connections involving the parietal cortex and the posterior superior temporal cortex. In the final chapter (Chapter 5), I summarise the main findings of the three studies, discuss their implications and outline potential future directions.

This dissertation is comprised of four studies investigating the hypothesis that adults and children who stutter differ from their same-age fluent peers in the neuroanatomy and neurophysiology underlying auditory speech processing. It has been consistently reported that adults who stutter demonstrate unique functional neural activation patterns during speech production, including reduced auditory activation, relative to nonstutterers. The extent to which these functional differences are accompanied by abnormal morphology of the brain in stutterers is unclear. The first study in this dissertation examined the neuroanatomical differences in speech-related cortex between adults who do and do not stutter using magnetic resonance imaging and voxel-based morphometry analyses. Adults who stutter were found to have localized grey matter volume increases in auditory and motor speech related cortex. The second study extended this line of research to children who stutter, who were found to have localized grey matter volume decreases in motor speech related cortex. Together, these studies suggest an abnormal trajectory of regional grey matter development in motor speech cortex of people who stutter. The last two studies investigated the mechanism underlying the repeated findings of reduced auditory activation during speech in people who stutter in more detail. Magnetoencephalography was used to investigate the hypothesis that people who stutter have increased speech induced suppression of early evoked auditory responses. Adults and children who stutter demonstrated typical levels of speech induced suppression relative to fluent peers. However, adults and children who stutter showed differences from peers in the timing of cortical auditory responses. Taken together, the studies demonstrate structural and functional abnormalities in brain regions related to auditory processing and point to the possibility that people who stutter have difficulty forming the neural representations of speech sounds necessary for fluent speech production.

The objective of this study is to test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single trial magneto-encephalographic (MEG) signals. This study was performed using motor execution and motor imagery movements to achieve a potential high performance Brain-Computer interface (BCI). Seven healthy volunteers, naïve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification for natural movement intentions was performed offline; Genetic Algorithm based Mahalanobis Linear Distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation. Through SAM imaging, strong and distinct event related desynchronisation (ERD) associated with sustaining, and event related synchronisation (ERS) patterns associated with ceasing of hand movements were observed in the beta band (15 - 30 Hz). The right and left hand ERD/ERS patterns were observed on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these cortical areas of high activity to correspond with the motor tasks as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51 ± 2.43 %) as well as motor imagery sessions (GA-MLD: 89.69 ± 3.34%). Thus, multiple movement intentions can be reliably detected from SAM-based spatially-filtered single trial MEG signals. MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control. This may prove tremendously helpful for patients suffering from various movement disorders to improve their quality of life.

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).

Made available in DSpace on 2016-04-25T16:44:16Z (GMT). No. of bitstreams: 1 MARCELO MORAES GARCIA.pdf: 919004 bytes, checksum: 13e70fb80b98146e763b703d8a897331 (MD5) Previous issue date: 2010-10-28 / The context in which large Brazilian construction companies are incorporated in the year 2010, with increased real estate financing and government investment, has provided great opportunities in this sector. This study aims to evaluate a group of 10 large builders, focusing on residential or commercial work, according to the criteria of the Management Excellence Model (MEM) of the National Quality Foundation (NQF). A questionnaire was adapted from NFQ and answered by the president or CEO of companies surveyed, as the sole builder of winning the National Quality Award (NQA). The survey results showed that the companies surveyed have a good level of quality management, with companies focusing on business performance works more subtly. Data analysis was performed considering the seven criteria: customers, society, leadership, strategies and plans, people, processes, and finally information and knowledge. The main conclusion of this study is that despite the good level of management of the companies surveyed, there is an imbalance of the shares in these companies as to the criteria of MEM, with overvaluation of the criteria procedures, and reduced recovery of actions directed to the criteria society. In comparison with the company winning the NQA, one of the companies showed indicators of high excellence in its management process and may initiate a process of competition to the NQA / O contexto em que as grandes construtoras brasileiras estão inseridas neste ano de 2010, com aumento do financiamento imobiliário e investimentos estatais, tem propiciado grandes oportunidades nesse setor. Esse estudo busca avaliar um grupo de 10 construtoras de grande porte, com foco em obras residenciais ou comerciais, de acordo com os critérios estabelecidos no Modelo de Excelência da Gestão (MEG) da Fundação Nacional da Qualidade (FNQ). Um questionário da FNQ foi adaptado e respondido pelo presidente ou diretor geral das empresas pesquisadas, assim como pela única construtora ganhadora do Prêmio Nacional da Qualidade (PNQ). Os resultados da pesquisa mostraram que as empresas pesquisadas possuem um bom nível de gestão da qualidade, tendo as empresas com foco em obras comerciais desempenho sutilmente superior. A análise dos dados foi realizada considerando-se sete critérios: clientes, sociedade, liderança, estratégias e planos, pessoas, processos e, por fim, informações e conhecimento. A principal conclusão deste estudo é que, apesar do bom nível de gestão das empresas pesquisadas, há um desbalanceamento das ações nessas empresas quanto aos critérios do MEG, havendo supervalorização do critério processos e reduzida valorização das ações voltadas ao critério sociedade. Na comparação com a empresa ganhadora do PNQ, uma das empresas mostrou indicadores de excelência elevados em seu processo de gestão, podendo iniciar um processo de concorrência ao PNQ

Cette thèse est consacrée à l'étude des signaux mesurés par Electroencéphalographie (EEG) et Magnétoencéphalographie (MEG) afin d'améliorer notre compréhension du cerveau humain. La MEG et l'EEG sont des modalités d'imagerie cérébrale non invasives. Elles permettent de mesurer, hors de la tête, respectivement le potentiel électrique et le champ magnétique induits par l'activité neuronale. Le principal objectif lié à l'exploitation de ces données est la localisation dans l'espace et dans le temps des sources de courant ayant généré les mesures. Pour ce faire, il est nécessaire de résoudre un certain nombre de problèmes mathématiques et informatiques difficiles. La première partie de cette thèse part de la présentation des fondements biologiques à l'origine des données M/EEG, jusqu'à la résolution du problème direct. Le problème direct permet de prédire les mesures générées pour une configuration de sources de courant donnée. La résolution de ce problème à l'aide des équations de Maxwell dans l'approximation quasi-statique passe par la modélisation des générateurs de courants, ainsi que de la géométrie du milieu conducteur, dans notre cas la tête. Cette modélisation aboutit à un problème direct linéaire qui n'admet pas de solution analytique lorsque l'on considère des modèles de tête réalistes. Notre première contribution porte sur l'implémentation d'une résolution numérique à base d'éléments finis surfaciques dont nous montrons l'excellente précision comparativement aux autres implémentations disponibles. Une fois le problème direct calculé, l'étape suivante consiste à estimer les positions et les amplitudes des sources ayant généré les mesures. Il s'agit de résoudre le problème inverse. Pour ce faire, trois méthodes existent: les méthodes paramétriques, les méthodes dites de "scanning", et les méthodes distribuées. Cette dernière approche fournit un cadre rigoureux à la résolution de problème inverse tout en évitant de faire de trop importantes approximations dans la modélisation. Toutefois, elle impose de résoudre un problème fortement sous-contraint qui nécessite de fait d'imposer des a priori sur les solutions. Ainsi la deuxième partie de cette thèse est consacrée aux différents types d'a priori pouvant être utilisés dans le problème inverse. Leur présentation part des méthodes de résolution mathématiques jusqu'aux détails d'implémentation et à leur utilisation en pratique sur des tailles de problèmes réalistes. Un intérêt particulier est porté aux a priori induisant de la parcimonie et conduisant à l'optimisation de problèmes convexes non différentiables pour lesquels sont présentées des méthodes d'optimisation efficaces à base d'itérations proximales. La troisième partie porte sur l'utilisation des méthodes exposées précédemment afin d'estimer des cartes rétinotopiques dans le système visuel à l'aide de données MEG. La présentation porte à la fois sur les aspects expérimentaux liés au protocole d'acquisition jusqu'à la mise en oeuvre du problème inverse en exploitant des propriétés sur le spectre du signal mesuré. La contribution suivante ambitionne d'aller plus loin que la simple localisation d'activités par le problème inverse afin de donner accès à la dynamique de l'activité corticale. Partant des estimations de sources sur le maillage cortical, la méthode proposée utilise des méthodes d'optimisation combinatoires à base de coupes de graphes afin d'effectuer de façon robuste le suivi de l'activité au cours du temps. La dernière contribution de cette thèse porte sur l'estimation de paramètres sur des données M/EEG brutes non moyennées. Compte tenu du faible rapport signal à bruit, l'analyse de données M/EEG dites "simple essai" est un problème particulièrement difficile dont l'intérêt est fondamental afin d'aller plus loin que l'analyse de données moyennées en explorant la variabilité inter-essais. La méthode proposée utilise des outils récents à base de graphes. Elle garantit des optimisations globales et s'affranchit de problèmes classiques tels que l'initialisation des paramètres ou l'utilisation du signal moyenné dans l'estimation. L'ensemble des méthodes développées durant cette thèse ont été utilisées sur des données M/EEG réels afin de garantir leur pertinence dans le contexte expérimental parfois complexe des signaux réelles M/EEG. Les implémentations et les données nécessaires à la reproductibilité des résultats sont disponibles. Le projet de rétinotopie par l'exploitation de données de MEG a été mené en collaboration avec l'équipe du LENA au sein de l'hôpital de La Pitié-Salpêtrière (Paris).