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.

INTRODUÇÃO: O Doppler Transcraniano funcional pode avaliar mudanças na velocidade do fluxo sanguíneo encefálico associadas a tarefas cognitivas e/ou sensitivo-motoras. Mede de maneira indireta a atividade metabólica de regiões cerebrais, segundo o princípio do acoplamento neurovascular. Os objetivos deste estudo foram: desenvolver um novo método de análise de Doppler transcraniano funcional para análise da lateralização hemisférica e verificar a capacidade deste novo método em diferenciar a lateralização hemisférica durante a execução de uma tarefa motora unimanual por indivíduos saudáveis. Adicionalmente, a lateralização hemisférica foi correlacionada com a preferência manual nestes indivíduos. MÉTODOS: Treze indivíduos saudáveis foram submetidos a um exame de Doppler transcraniano funcional durante uma prova de ativação motora manual (oposição de dedos). As sessões de Doppler transcraniano funcional foram realizadas com aparelho Doppler-Box Transcranial Doppler Unit. A prova manual compreendeu uma sequência de movimentos de oposição do primeiro e segundo dedos (thumb-tofinger opposition movement) realizado por uma mão e depois pela outra, em uma frequência de 1 movimento por segundo (1Hz) fornecida por um metrônomo digital. Durante a execução dos movimentos, foram insonadas simultaneamente as artérias cerebrais médias direita e esquerda. Para interpretação dos dados de Doppler transcraniano funcional desenvolvemos um novo programa de análise denominado FDAT, que tem vantagens de sofrer mínima influência de artefatos de ruído no sinal e de não assumir um formato pré-determinado da resposta hemodinâmica cerebral. Foi calculado um índice de lateralização (IL) como a diferença entre a velocidade relativa média da época de ativação e a velocidade relativa média da época de repouso para cada prova motora. Foi calculada a diferença dos valores de IL (ILe - ILd) provenientes da análise com cada método, obtendo-se um índice de ativação, próprio de cada sujeito. A comparação do índice de ativação durante a movimentação da mão direita, e durante a movimentação da mão esquerda, foi feita com o teste de Wilcoxon. A correlação entre o índice de ativação e a preferência manual avaliada pelo Inventário de Edimburgo foi avaliada pelo coeficiente rho de Spearman. RESULTADOS: Houve uma diferença estatisticamente significante entre o IA obtido durante a movimentação da mão direita ou da mão esquerda (p=0,02). Houve correlação estatisticamente significante entre a preferência manual e a assimetria na lateralização hemisférica identificada pelo Doppler Transcraniano funcional (rho = 0.85, p < 0.001). CONCLUSÕES: A análise do Doppler Transcraniano funcional mostrou-se viável pelo método proposto, capaz de avaliar o grau de lateralização hemisférica em uma prova de ativação motora, com boa correlação com a preferência manual. Trata-se de uma ferramenta prática, não invasiva e de baixo custo para a avaliação da lateralização hemisférica em determinadas provas funcionais / INTRODUCTION: Functional transcranial Doppler is a method for the assessment of changes in blood flow velocity of the middle cerebral artery. An asymmetric increase in blood flow velocity is a marker of hemispheric lateralization during unimanual motor task performance. The aims of this study were to propose a novel and efficient method for functional transcranial Doppler analysis based on cubic smoothing splines, and to verify the ability of this method to identify hemispheric lateralization during unimanual motor task performance in healthy subjects. In addition, hemispheric lateralization was correlated with handedness in these subjects. METHODS: Thirteen healthy subjects participated in the study. Blood flow velocities in the right and left middle cerebral arteries were recorded using functional transcranial Doppler during a finger-tapping task with either the right or left hand. Data were analyzed with a multi-step new method that included: baseline determination, raw data normalization, smoothing, lateralization Index calculation, definition of rest and motor task epochs and activation Index calculation. A positive activation Index reflects right-hemisphere lateralization and a negative activation index, left hemisphere lateralization. RESULTS: There was a statistically significant difference between the activation index obtained during right or left hand movements (p=0.02). Hand dominance was significantly correlated with asymmetry in hemispheric lateralization assessed with functional transcranial Doppler (rho = 0.85, p<0.001). CONCLUSIONS: This novel method for functional transcranial Doppler analysis was capable to assess the hemispheric lateralization during motor task performance, and correlated well with handedness. It is a practical, non-invasive and unexpensive tool for the assessment of hemispheric lateralization.

Despite extensive evidence documenting abnormal hypothalamic-pituitary-adrenal (HPA) axis functioning as a risk factor for the development of depression and other psychiatric disorders, and experimental evidence from acute stress manipulations, the effects of sustained cortisol alterations on clinically relevant cognitive-behavioural and neural processing remain poorly understood. The aim of this thesis was to characterise how non-acute changes in cortisol levels modify behavioural and neural biases implicated in stress-related disorders by following two complementary lines of evidence: firstly, by increasing cortisol via a direct pharmacological intervention; and secondly, by testing the ability of gut microbiota manipulations to alter cortisol reactivity. The first study found that sustained increases in cortisol following 10-day administration of hydrocortisone were associated with altered memory and emotional processing in healthy volunteers. Specifically, participants receiving hydrocortisone showed enhanced recognition of emotional words, while their neutral memory performance was unaffected despite lower parahippocampal and occipital activation during viewing and encoding of neutral pictures. Furthermore, we found that resting-state functional connectivity between limbic-temporal regions of interest (amygdala and hippocampus) and the striatum (head of the caudate), as well as frontal and prelimbic cortices was decreased. In contrast, hippocampal and visual processing during negative facial expressions, and functional connectivity between the amygdala and the brainstem at rest, were increased in the hydrocortisone versus placebo groups. Overall, these findings suggest that non-acute increases in glucocorticoids enhance processing of emotionally salient information in limbic-temporal regions, which may modulate further neural mechanisms of sensory and homeostatic relevance. Enhancements in declarative emotional memory following hydrocortisone also implicate the modulation of amygdalar-hippocampal interactions by cortisol. Conversely, neutral stimulus processing was found to be either reduced or unaffected across a number of cognitive and memory domains. A specific increase for negative processing was further supported by poorer self-reported well-being at the mid-point of the study in participants receiving hydrocortisone. In a separate study exploring the ability of prebiotic supplements to affect cortisol reactivity and emotional processing, a Bimuno-galactooligosaccharide prebiotic was found to reduce the waking cortisol response and increase positive versus negative attentional processing in healthy volunteers. While these effects were not found to be associated, they provide initial promising evidence of the ability to target the HPA axis and emotional processing via the gut microbiota in humans. Overall, this thesis supports the idea that stress-induced physiological changes after prolonged or repeated cortisol exposure are associated with neural and behavioural alterations, which in turn have been crucial in understanding neuropsychological mechanisms underlying psychiatric disease. A better stratification of the effects of sustained HPA axis alterations on psychiatrically relevant cognitive-emotional domains and neural mechanisms thus remains of high priority.

This study aims to shed light on how and when mechanisms of the human brain evolved to support complex cognition and language. The field of evolutionary cognitive archaeology asserts that prehistoric technologies, as products of past cognition in action, are informative of the minimum cognitive and linguistic abilities that hominins needed to possess for their production. Previous researchers attempted to reconstruct the neural correlates of two Early Stone Age (ESA) tool industries, the 2.6 million-year-old Oldowan industry and the 0.7 million-year-old late Acheulian industry, by using positron emission tomography (PET) to observe the functional activation occurring in the brains of trained and expert stone knappers after making these different tool types. Because of evidence for overlap between the knapping and language circuits of the brain and increased anterior frontal activity during Acheulian tool production, these researchers argued that their results 1) indicate increased cognitive demands for late Acheulian tool production relative to Oldowan tool production and 2) support a technological origin for language, meaning that certain language functions co-opted the neural substrate and functions that were already established for toolmaking and tool use. Because of the motion limiting aspects of PET, however, these studies were unable to record the hemodynamic response of naturalistic stone knapping in real-time. They also were unable to observe the functional activation associated with the earliest stage of learning, which is likely to differ from late stage learning or expertise. Furthermore, any conclusion regarding a technological origin for language is problematic if it relies on data obtained from participants who learned to knap with verbal instruction. To test these two claims, this dissertation utilized a neuroimaging technique called functional near-infrared spectroscopy (fNIRS) to explore the neural correlates of real-time, naturalistic Oldowan and Acheulian stone knapping at three different points in learning. Participants in the study were separated into two groups to learn ESA knapping skills. Both groups watched the same video tutorials that depicted an expert’s hands as he made stone tools, but those in the verbal group heard spoken instructions, while those in the nonverbal group watched a version with the sound turned off. Functional brain images were reconstructed from the digitized landmarks of each participant’s head and from the optical data. An analysis of variance (ANOVA) revealed a clearer distinction between the neural processes of Oldowan and Acheulian tool manufacturing tasks than has previously been demonstrated. Only the Acheulian task recruited a frontotemporal working memory network. Selection for individuals with increased working memory capacities, which would have allowed them to make increasingly complex tools to gain access to novel dietary items, may have spurred the evolution of larger brain size in the genus Homo during the early Pleistocene. The results also demonstrated that the presence or absence of language during training dictated which higher-order cognitive areas of the brain become engaged and at what point in training. Thus, the results of previous neuroarchaeological studies reflect a very specific condition of stone knapping skill acquisition that involves linguistic instruction, which may not be analogous to how skills were transmitted during the ESA. Finally, evidence of overlap between left hemisphere language and stone knapping circuits among the participants in the nonverbal group lends additional support for the technological origin for language hypothesis.

Chronic musculoskeletal pain is a leading cause of disability worldwide yet the mechanisms of chronification and neural responses to effective treatment remain elusive. Non-invasive imaging techniques are useful for investigating brain alterations associated with health and disease. Thus the overall goal of this dissertation was to investigate the white (WM) and grey matter (GM) structural differences in patients with musculoskeletal pain before and after psychotherapeutic intervention: cognitive behavioral therapy (CBT). To aid in the interpretation of clinical findings, we used a novel porcine model of low back pain-like pathophysiology and developed a post-mortem, in situ, neuroimaging approach to facilitate translational investigation. The first objective of this dissertation (Chapter 2) was to identify structural brain alterations in chronic pain patients compared to healthy controls. To achieve this, we examined GM volume and diffusivity as well as WM metrics of complexity, density, and connectivity. Consistent with the literature, we observed robust differences in GM volume across a number of brain regions in chronic pain patients, however, findings of increased GM volume in several regions are in contrast to previous reports. We also identified WM changes, with pain patients exhibiting reduced WM density in tracts that project to descending pain modulatory regions as well as increased connectivity to default mode network structures, and bidirectional alterations in complexity. These findings may reflect network level dysfunction in patients with chronic pain. The second aim (Chapter 3) was to investigate reversibility or neuroplasticity of structural alterations in the chronic pain brain following CBT compared to an active control group. Longitudinal evaluation was carried out at baseline, following 11-week intervention, and a four-month follow-up. Similarly, we conducted structural brain assessments including GM morphometry and WM complexity and connectivity. We did not observe GM volumetric or WM connectivity changes, but we did discover differences in WM complexity after therapy and at follow-up visits. To facilitate mechanistic investigation of pain related brain changes, we used a novel porcine model of low back pain-like pathophysiology (Chapter 6). This model replicates hallmarks of chronic pain, such as soft tissue injury and movement alteration. We also developed a novel protocol to perform translational post-mortem, in situ, neuroimaging in our porcine model to reproduce WM and GM findings observed in humans, followed by a unique perfusion and immersion fixation protocol to enable histological assessment (Chapter 4). In conclusion, our clinical data suggest robust structural brain alterations in patients with chronic pain as compared to healthy individuals and in response to therapeutic intervention. However, the mechanism of these brain changes remains unknown. Therefore, we propose to use a porcine model of musculoskeletal pain with a novel neuroimaging protocol to promote mechanistic investigation and expand our interpretation of clinical findings.

Cannabis use initiated during adolescence may precipitate lasting consequences on the brain and behavioral health of the individual. However, research on the risk factors for cannabis use during adolescence has been largely cross-sectional in design. Despite the few prospective studies, even less is known about the neurobiological predictors. This dissertation improves on the extant literature by leveraging a large longitudinal study to uncover the predictors of cannabis use in adolescent samples collected prior to exposure. All data were drawn from the IMAGEN study and contained a large sample of adolescents studied at age 14 (N=2,224), and followed up at age 16 and 19. Participants were richly characterized using psychosocial questionnaires, structural and functional MRI, and genetic measurements. Two hypothesis-driven studies focused on amygdala reactivity and two data-driven studies across the feature domains were completed to characterize cannabis use in adolescence. The first study was cross-sectional and identified bilateral amygdala hyperactivity to angry faces in a sample reporting cannabis use by age 14 (n=70). The second study determined this amygdala effect was predictive of cannabis use by studying a sample of cannabis-naïve participants at age 14 who then used cannabis by age 19 (n=525). A dose-response relationship was observed such that heavy cannabis users exhibited higher amygdala reactivity. Exploratory analyses suggested amygdala reactivity decreased from age 14 to 19 within the cannabis sample, although statistical significance was not found. In the third study, data-driven machine learning analyses predicted cannabis initiation by age 16 separately for males (n=207) and females (n=158) using data from all feature domains. These analyses identified a sparse set of shared psychosocial predictors, whereas the identified brain predictors exhibited sex- and drug-specificity. Additional analyses predicted initiation by age 19 and identified a sparse set of psychosocial predictors for females only (n=145). The final study improved on drug-specificity by performing differential prediction analyses between matched samples of participants who initiated cannabis+binge drinking vs. binge drinking only by age 16 (males n=178; females n=148). A sparse subset of psychosocial predictors identified in the third study was reproduced, and novel brain predictors were identified. Those analyses were unique as they compared two machine learning algorithms, namely regularized logistic regression and random forest analyses. These studies substantiated the use of both hypothesis- and data-driven prediction analyses applied to large longitudinal datasets. They also addressed common issues related to human addiction research by examining sex-differences and drug-specificity. Critically, these studies uncovered predictors of use in samples collected prior to cannabis-exposure. The identified predictors are therefore disentangled from consequences of use. Results from all studies inform etiological mechanisms influencing cannabis use in adolescence. These findings can also be used to stratify risk in vulnerable adolescents and inform targets for interventions designed to curb use.

Anxiety disorders (ADs) and stress-related disorders are some of the most common psychiatric disorders in the United States. Like other c0mplex psychiatric illness, genetics and neuroimaging research has focused on understanding their underlying neurobiology. Areas within the fear-network play important roles in threat perception, fear conditioning/learning, cognitive processing, and modulation of fear responses including contextual modulation and extinction and have been implicated in ADs as well as stress disorders such as posttraumatic stress disorder (PTSD). The primary gap in the current search for underlying biological mechanisms is in whether biomarkers associated with disorders share genetic influences with the disorders they index. Therefore, the aims of this dissertation are: 1) to examine the shared etiology of PTSD and threat-related brain regions while accounting for trauma using a large sample of male twins who served in the military during the Vietnam War; 2) to elucidate the shared and specific risk factors (genetic, familial environment and unique environment) and their roles amongst fear and anxiety domains in children; and 3) to examine whether brain regions previously implicated in fear processing and anxiety are significantly associated with a genetic factor score indexing fear and anxiety measures in a child sample. Using biometrical twin modeling this dissertation produced several novel findings regarding etiology of PTSD, threat-related domains and associated brain morphometry. Analyses investigating brain morphometric differences as potential endophenotypes for PTSD provided preliminary evidence that their phenotypic association is largely accounted for by environmental influences, specifically trauma exposure. However, sample size-induced model instability limits the ability to make definitive conclusions. Examining domains of fear and anxiety in children suggested a substantial genetic overlap between the two. Finally, the incorporation of a genetic factor score derived from the results of the biometrical modeling of fear and anxiety provided preliminary evidence for a genetic relationship between fear/anxiety and brain regions of interest. Although these results should be interpreted within the context of important limitations, they provide clear evidence that additional research into the genetic relationship between brain regions and disorders with larger sample sizes is justified.

Le travail présenté dans cette thèse fait partie d’un programme de recherche qui vise à comprendre comment le cerveau traite et représente les structures symboliques dans des domaines comme le langage ou les mathématiques. L’existence de structures composées de sous-éléments, tel que les morphèmes, les mots ou les phrases est très fortement suggérée par les analyses linguistiques et les données expérimentales de la psycholinguistique. En revanche, l’implémentation neuronale des opérations et des représentations qui permettent la nature combinatoire du langage reste encore essentiellement inconnue. Certaines opérations de composition élémentaires permettant une représentation interne stable des objets dans le cortex sensoriel, tel que la reconnaissance hiérarchique des formes, sont aujourd’hui mieux comprises [5]. En revanche, les modèles concernant les opérations de liaisons(binding) nécessaires à la construction de structures symboliques complexes et possiblement hiérarchiques, pour lesquelles des manipulations précises des composants doit être possible, sont encore peu testés de façon expérimentale et incapables de prédire les signaux en neuroimagerie. Combler le fossé entre les données de neuroimagerie expérimentale et les modèles proposés pour résoudre le problème de binding est une étape cruciale pour mieux comprendre les processus de traitements et de représentation des structures symboliques. Au regard de ce problème, l’objectif de ce travail était d’identifier et de tester expérimentalement les théories basées sur des réseaux neuronaux, capables de traiter des structures symboliques pour lesquelles nous avons pu établir des prédictions testables, contre des mesures existantes de neuroimagerie fMRI et ECoG dérivées de tâches de traitement du langage. Nous avons identifié deux approches de modélisation pertinentes. La première approche s’inscrit dans le contexte des architectures symboliques vectorielles (VSA), qui propose une modélisation mathématique précise des opérations nécessaires pour représenter les structures dans des réseaux neuronaux artificiels. C’est le formalisme de Paul Smolensky[10], utilisant des produit tensoriel (TPR) qui englobe la plupart des architectures VSA précédemment proposées comme, par exemple, les modèles d’Activation synchrones[9], les représentations réduites holographique[8], et les mémoires auto-associatives récursives[1]. La seconde approche que nous avons identifiée est celle du "Neural Blackboard Architecture" (NBA), développée par Marc De Kamps et Van der Velde[11]. Elle se démarque des autres en proposant une implémentation des mécanismes associatifs à travers des circuits formés par des assemblages de réseaux neuronaux. L’architecture du Blackboard repose sur des changements de connectivité transitoires des circuits d’assemblages neuronaux, de sorte que le potentiel de l’activité neurale permise par les mécanismes de mémoire de travail après un processus de liaison, représente implicitement les structures symboliques. Dans la première partie de cette thèse, nous détaillons la théorie derrière chacun de ces modèles et les comparons, du point de vue du problème de binding. Les deux modèles sont capables de répondre à la plupart des défis théoriques posés par la modélisation neuronale des structures symboliques, notamment ceux présentées par Jackendo[3]. Néanmoins, ces deux classes de modèles sont très différentes. Le TPR de Smolenky s’appuie principalement sur des considérations spatiales statiques d’unités neurales artificielles, avec des représentations explicites complètement distribuées et spatialement stables mises en œuvre par des vecteurs. La NBA en revanche, considère les dynamiques temporelles de décharge de neurones artificiels, avec des représentations spatialement instables implémentées par des assemblages neuronaux. (...) / The aim of this thesis is to understand how the brain computes and represents symbolic structures, such like those encountered in language or mathematics. The existence of parts in structures like morphemes, words and phrases has been established through decades of linguistic analysis and psycholinguistic experiments. Nonetheless the neural implementation of the operations that support the extreme combinatorial nature of language remains unsettled. Some basic composition operations that allow the stable internal representation of sensory objects in the sensory cortex, like hierarchical pattern recognition, receptive fields, pooling and normalization, have started to be understood[5]. But models of the binding operations required for construction of complex, possibly hierarchical, symbolic structures on which precise manipulation of its components is a requisite, lack empirical testing and are still unable to predict neuroimaging signals. In this sense, bridging the gap between experimental neuroimaging evidence and the available modelling solutions to the binding problem is a crucial step for the advancement of our understanding of the brain computation and representation of symbolic structures. From the recognition of this problem, the goal of this PhD became the identification and experimental test of the theories, based on neural networks, capable of dealing with symbolic structures, for which we could establish testable predictions against existing fMRI and ECoG neuroimaging measurements derived from language processing tasks. We identified two powerful but very different modelling approaches to the problem. The first is in the context of the tradition of Vectorial Symbolic Architectures (VSA) that bring precise mathematical modelling to the operations required to represent structures in the neural units of artificial neural networks and manipulate them. This is Smolensky’s formalism with tensor product representations (TPR)[10], which he demonstrates can encompass most of the previous work in VSA, like Synchronous Firing[9], Holographic Reduced Representations[8] and Recursive Auto-Associative Memories[1]. The second, is the Neural Blackboard Architecture (NBA) developed by Marc De Kamps and Van der Velde[11], that importantly differentiates itself by proposing an implementation of binding by process in circuits formed by neural assemblies of spiking neural networks. Instead of solving binding by assuming precise and particular algebraic operations on vectors, the NBA proposes the establishment of transient connectivity changes in a circuit structure of neural assemblies, such that the potential _ow of neural activity allowed by working memory mechanisms after a binding process takes place, implicitly represents symbolic structures. The first part of the thesis develops in more detail the theory behind each of these models and their relationship from the common perspective of solving the binding problem. Both models are capable of addressing most of the theoretical challenges posed currently for the neural modelling of symbolic structures, including those presented by Jackendo_[3]. Nonetheless they are very different, Smolenky’s TPR relies mostly on spatial static considerations of artificial neural units with explicit completely distributed and spatially stable representations implemented through vectors, while the NBA relies on temporal dynamic considerations of biologically based spiking neural units with implicit semi-local and spatially unstable representations implemented through neural assemblies. For the second part of the thesis, we identified the superposition principle, which consists on the addition of the neural activations of each of the sub-parts of a symbolic structure, as one of the most crucial assumptions of Smolensky’s TPR. (...)

One of the major goals of cognitive neuroscience is to better understand the psychological and neural bases of human executive-attention. Executive or supervisory attention refers to a collection of higher-order cognitive functions whose primary contribution to behavior is to support controlled information processing and action. The capacity to control attention is essential for our adaptive interaction with the environment because it allows flexibility in our responses to ever changing situational contexts and demands. Executive-attention processes therefore play a unique role in shaping the human experience. Use of three-dimensional functional neuroimaging has fast become the empirical standard for investigating how executive-attention is implemented in the human brain. Most recently, emphasis has been placed on the use of these techniques to parse discrete components of a putative neural network relating to action-monitoring and cognitive control processes of the medial and lateral prefrontal cortex. This work has relied heavily on the use of popular experimental paradigms such as the Stroop task and their unique capacity to challenge such processes in humans. These tasks have also been especially useful for conceptualizing the nature of higher-cognitive dysfunction in complex brain disorders such as schizophrenia. The focus of this thesis concerns a novel application of the Stroop paradigm and functional imaging approach to examine executive-attention performance in healthy subjects and patients with schizophrenia and obsessive-compulsive disorder. On one hand, this work aimed to address current ideas on the nature of executive-control mechanisms and how they may be compromised in these two common psychiatric disorders. On the other hand, this work aimed to examine important conceptual and methodological issues associated with functional imaging approaches to the study of higher-cognition and cognitive psychopathology in humans. In line with connectionist models of executive-attention phenomena, the first study in this thesis investigated the effects of task practice on a larger-scale neurocognitive network associated with performance of the Stroop task in healthy subjects. This study involved the use of a novel methodological approach to model physiological covariances or ?functional connectivity? in PET data, which generated previously unseen and interesting insights into the neural basis of Stroop phenomena, whilst complimenting existing ideas on the role of the anterior cingulate and lateral prefrontal cortex in mediating executive-control functions. These findings were then extended to a comparative study of patients with schizophrenia and obsessive-compulsive disorder. This study largely corroborated previous reports of prefrontal executive dysfunction in schizophrenia, although patients also showed evidence for a compensatory strengthening of connectivity in a fronto-parietal network that accompanied task practice. This finding has important implications for existing models of higher-cognitive dysfunction and abnormal brain integration in schizophrenia. For patients with OCD compared to healthy subjects, performance of the Stroop task evoked a pattern of abnormal connectivity among predominantly corticostriatal regions, including a previously reported hyperfunction of the dorsal anterior cingulate cortex. While this latter result has been linked to a specific disturbance of action-monitoring in patients with OCD, the current study suggests that this may map onto a more extensive corticostriatal network abnormality in line with current theoretical models of this illness. One caveat raised in the first study of patients with schizophrenia concerned the effects of illnesschronicity and medication on functional imaging studies of higher-cognition and prefrontal function in schizophrenia. To address this, a second clinical study was undertaken in patients with a first-episode of schizophrenia (diagnosis confirmed at follow-up) who were examined before and after commencing antipsychotic treatment. Overall, the findings from this study support the idea of trait-like disturbances of prefrontal executive function in schizophrenia; however, they also suggested that aspects of this disturbance may be specific to the critical, early stage of illness - implicating progressive changes with illness chronicity and/or treatment intervention. These findings are discussed in relation to the developmental context of cognitive psychopathology in schizophrenia.

<p>Although consciousness has been studied since ancient time, how the brain implements consciousness is still considered a great mystery by most. This thesis investigates the neural correlates of consciousness by measuring brain activity with functional magnetic resonance imaging (fMRI) while specific contents of consciousness are defined and maintained in various experimental settings. Study 1 showed that the brain works differently when creating a new conscious percept compared to when maintaining the same percept over time. Specifically, sensory and fronto-parietal regions were activated for both conditions but with different activation patterns within these regions. This distinction between creating and maintaining a conscious percept was further supported by Study 2, which in addition showed that there are both differences and similarities in how the brain works when defining a visual compared to an auditory percept. In particular, frontal cortex was commonly activated while posterior cortical activity was modality specific. Study 3 showed that task difficulty influenced the degree of frontal and parietal cortex involvement, such that fronto-parietal activity decreased as a function of ease of identification. This is interpreted as evidence of the non-necessity of these regions for conscious perception in situations where the stimuli are distinct and apparent. Based on these results a model is proposed where sensory regions interact with controlling regions to enable conscious perception. The amount and type of required interaction depend on stimuli and task characteristics, to the extent that higher-order cortical involvement may not be required at all for easily recognizable stimuli.</p>