Pathologisches Glücksspiel (PG) ist eine psychiatrische Erkrankung, die gerade erst im DSM-5 der gleichen Kategorie wie substanzgebundene Suchterkrankungen zugeordnet wurde. Bildgebungsstudien zu Substanzabhängigkeit beobachteten funktionelle und strukturelle Veränderungen im präfrontalen Kortex (PFC) und mesolimbischen Belohnungssystem (d.h. Striatum). Für PG wurden ähnliche Veränderungen berichtet; jedoch gibt es kaum Studien, die sich mit verschiedenen Aspekten funktioneller und struktureller Korrelate in diesen Regionen beschäftigen. Diese Arbeit untersuchte PG Patienten, alkoholabhängige (AD) Patienten und Kontrollpersonen (HC) mit Magnetresonanztomografie. In Analyse I wurden funktionelle Gehirndaten während der Belohnungsaufgabe zwischen den drei Gruppen verglichen. In Analyse II wurde das Volumen grauer Substanz mit voxelbasierter Morphometrie und in Analyse III die intrinsische Gehirnaktivität mit einer seedbasierten funktionellen Konnektivitätsanalyse von PG Patienten und HC ausgewertet. Die Analysen ergaben veränderte Aktivierungen in frontostriatalen Arealen während der Verarbeitung von Verlustvermeidung für PG Patienten im Vergleich zu HC. PG Patienten unterschieden sich dabei in ihrer Aktivierung von AD Patienten während der Antizipation von Geldverlust. Weiterhin zeigten PG Patienten erhöhte Volumina grauer Substanz und eine erhöhte funktionelle Konnektivität in frontostriatalen Arealen im Vergleich zu HC. Die Ergebnisse liefern weitere Hinweise für eine veränderte Belohnungsverarbeitung in PG und betonen die Bedeutung der Verlustvermeidungsverarbeitung. Die Volumenveränderungen im und die erhöhte Konnektivität zwischen dem PFC and Belohnungssystem deuten auf eine veränderte Interaktion zwischen diesen Regionen hin. Da solche Veränderungen in kortikostriatalen Systemen Ähnlichkeiten zu denen in Substanzabhängigkeiten aufweisen, unterstützen die Ergebnisse die neue Klassifikation des PG im DSM-5. / Pathological gambling (PG) is a psychiatric disorder newly classified under the same category as substance use disorders in the DSM-5. Neuroimaging studies on substance-related addictions reported functional and structural changes in the prefrontal cortex (PFC) and the mesolimbic reward system (i.e., striatum). For PG, findings are not that extensive, but also demonstrate altered reward processing and prefrontal function. However, there is a lack of studies focusing on different aspects of functional and structural correlates within these areas in PG. This thesis investigated PG patients, alcohol dependent (AD) patients and healthy controls with magnetic resonance imaging (MRI). In analysis I, functional brain data of a reward paradigm was compared between the three groups. In analysis II, local gray matter volume of PG patients and controls was processed via voxel-based morphometry. Resting-state data of PG patients and controls was analyzed via seed-based functional connectivity in analysis III. Results revealed altered brain responses in fronto-striatal areas during loss avoidance processing in PG patients as compared to controls. Importantly, PG patients differed in their brain responses from AD patients during the prospect of monetary loss. Moreover, PG patients showed an increase in local gray matter volume and functional connectivity in frontal-striatal areas as compared to controls. Our results add further evidence for an altered reward processing in PG and underline the importance of loss avoidance processing. Moreover, our findings of volumetric alterations within and increased connectivity between PFC and reward system, suggest an altered interaction between these brain regions. Since such alterations in cortico-striatal circuits resemble those reported for substance-related addictions, our findings support the new classification of PG in the DSM-5.

L’aphasie est un trouble acquis du langage entraînant des problèmes de communication pouvant toucher la compréhension et/ou l’expression. Lorsque l’aphasie fait suite à un accident vasculaire cérébral, une régression des déficits communicatifs s'observe initialement, mais elle peut demeurer sévère pour certains et est considérée chronique après un an. Par ailleurs, l’aphasie peut aussi être observée dans l’aphasie progressive primaire, une maladie dégénérative affectant uniquement le langage dans les premières années. Un nombre grandissant d’études s’intéressent à l’impact de la thérapie dans l’aphasie chronique et ont démontré des améliorations langagières après plusieurs années. L’hémisphère gauche semble avoir un rôle crucial et est associé à de meilleures améliorations langagières, mais la compréhension des mécanismes de plasticité cérébrale est encore embryonnaire. Or, l’efficacité de la thérapie dans l’aphasie progressive primaire est peu étudiée. À l’aide de la résonance magnétique fonctionnelle, le but des présentes études consiste à examiner les mécanismes de plasticité cérébrale induits par la thérapie Semantic Feature Analysis auprès de dix personnes souffrant d’aphasie chronique et d’une personne souffrant d’aphasie progressive primaire. Les résultats suggèrent que le cerveau peut se réorganiser plusieurs années après une lésion cérébrale ainsi que dans une maladie dégénérative. Au niveau individuel, une meilleure amélioration langagière est associée au recrutement de l’hémisphère gauche ainsi qu’une concentration des activations. Les analyses de groupe mettent en évidence le recrutement du lobule pariétal inférieur gauche, alors que l’activation du gyrus précentral gauche prédit l’amélioration suite à la thérapie. D’autre part, les analyses de connectivité fonctionnelle ont permis d’identifier pour la première fois le réseau par défaut dans l’aphasie. Suite à la thérapie, l’intégration de ce réseau bien connu est comparable à celle des contrôles et les analyses de corrélation suggèrent que l’intégration du réseau par défaut a une valeur prédictive d’amélioration. Donc, les résultats de ces études appuient l’idée que l’hémisphère gauche a un rôle prépondérant dans la récupération de l’aphasie et fournissent des données probantes sur la neuroplasticité induite par une thérapie spécifique du langage dans l’aphasie. De plus, l’identification d’aires clés et de réseaux guideront de futures recherches afin d’éventuellement maximiser la récupération de l’aphasie et permettre de mieux prédire le pronostic. / Aphasia is an acquired language impairment leading to communication disorders which may affect comprehension and/or expression. When aphasia follows a stroke, major recovery of the communicative deficits is initially observed after the lesion, but for some the aphasia may remain severe and is considered to be chronic after a year. Furthermore aphasia can be observed in primary progressive aphasia, a degenerative disease only affecting language in the early years. The impact of therapy in chronic aphasia is the subject of growing literature in recent years and has shown language improvements after several years of therapy. The left hemisphere seems to have a crucial role and is associated with greater language improvements but our understanding of brain plasticity mechanisms is still lacking. In primary progressive aphasia, few studies have examined therapy effectiveness. Using functional magnetic resonance imaging, the aim of these studies was to examine therapy-induced brain plasticity mechanisms following Semantic Feature Analysis in ten participants suffering from chronic aphasia and one participant with primary progressive aphasia. The results suggest that brain reorganization is possible several years after injury and in degenerative disease. At the individual level, greater language improvement is associated with the recruitment of the left hemisphere and less activated areas. Group analysis shows the recruitment of left inferior parietal lobule, whereas the activation of left precentral gyrus predicts improved response to therapy. Functional connectivity analysis allowed for the first time the identification of the default-mode network in aphasia. Following therapy, the integration of this well-known network is comparable to that of the controls and the correlation analysis suggests that the default-mode network integration has a predictive value for improvement. Therefore, the results of these studies support the idea that the left hemisphere has a major role in the recovery of aphasia and provide evidence on therapy-induced neuroplasticity in aphasia. In addition, the identification of key areas and networks will guide future research in order to possibly maximize the recovery of aphasia and to better predict the prognosis.

Este estudo foi realizado para testar técnicas de ressonância magnética funcional (RMf) para investigar aspectos funcionais da pineal humana. Foram coletadas imagens funcionais e amostras de sangue total para a dosagem da melatonina plasmática antes, durante e após a apresentação de estímulo com luz azul quase monocromática em indivíduos saudáveis. Os participantes realizaram o exame de tomografia computadorizada do crânio sem o uso de contraste endovenoso (TC), para a avaliação qualitativa do grau de calcificação pineal. As conclusões foram: a) não houve ativação da pineal em resposta à aplicação da luz; b) não houve diferenças estatisticamente significativas entre as condições pré-estímulo, durante o estímulo ou pós-estímulo usando diferentes métodos de análise dos dados de RMf, porém, observou-se tendência de maior poder espectral na pineal durante a aplicação do estímulo luminoso do que nas condições pré e pós-estímulo; c) foi identificada a conectividade funcional da pineal, que poderá ser melhor avaliada em estudo futuro. / This study aimed to test the functional magnetic resonance imaging (fMRI) techniques in order to investigate the functional aspects of human pineal gland. Some functional images and total blood samples for dosing the plasmatic melatonin concentration were collected before, during and after the presentation of a monochromatic blue light stimulation in healthy individuals. All subjects were examined by a brain CT scan, with no the administration of endovenous contrast, for the qualitative assessment of the pineal calcification level. The conclusions were the following: a) there was no pineal activation in response to the application of light; b) there were no statistically significant differences between the pre-, during and poststimulation conditions with different analysis methods of fMRI data, however, there was a trend of greater spectral power in the pineal gland during the luminous stimulation application than under the other conditions; c) the functional connectivity of the pineal could be identified, which should be better assessed in a future study.

Functional magnetic resonance imaging (fMRI) is recent important method, used in neuroimaging. The aim of this thesis is to develop software tool for comparison of two methods for functional and effective connectivity estimation. In this thesis are described the basics of magnetic resonance imaging, fMRI, basic terms of fMRI experiments and generally are described methods of functional and effective connectivity. Then are more detailed mentioned methods of dynamic causal modeling (DCM), Granger causal modeling (GCM) and independent component analysis (ICA). Practical implementation of DCM in toolbox SMP and ICA in toolbox GIFT is also mentioned. In purpose to describe behavior of DCM and GCM in dependence on several parameters are performed Monte Carlo simulations. Then the concept and realization of software tool for simulating connectivity and comparison of DCM and GCM are described. Finally results of DCM and GCM comparison and results of Monte Carlo simulations are discussed.

La maladie d’Alzheimer (MA) est à l’origine de la majorité des cas de démence chez les personnes âgées. Son diagnostic précoce est essentiel pour mieux comprendre les mécanismes cérébraux sous-tendant la manifestation phénotypique de la maladie et développer des interventions conséquentes. Le fait d’étudier des individus à risque de développer la MA, par exemple ceux présentant un déclin cognitif subjectif (DCS) ou un trouble cognitif léger (TCL), offre l’opportunité d’examiner les processus neuropathophysiologiques qui précèdent le stade démentiel. Cela permettrait, entre autres, d’identifier des biomarqueurs avant-coureurs de la maladie. Cette thèse avait pour but d’investiguer la présence d’hyperactivation cérébrale chez des individus à risque de développer la MA, et d’examiner les réseaux cérébraux fonctionnels associés à l’hyperactivation. L’hyperactivation se définit par la présence de niveaux supérieurs d’activation cérébrale chez des personnes faisant partie de groupes à risque pour la MA (p.ex. DCS ou TCL), comparativement à des participants contrôles cognitivement sains. L’hyperactivation est le plus souvent mesurée par l’imagerie par résonance magnétique fonctionnelle (IRMf) en condition de réalisation de tâche. Dans cette thèse, le lecteur ou la lectrice sera d’abord exposée aux études ayant utilisé l’IRMf pour examiner les patrons d’activation cérébrale et de connectivité fonctionnelle chez les individus ayant reçu un diagnostic clinique de MA, de TCL ou présentant un DCS. Les modèles théoriques découlant de ces études seront ensuite présentés. Afin de mieux comprendre le phénomène d’hyperactivation et sa relation avec les patrons de connectivité fonctionnelle, les divers enjeux scientifiques qui demeurent à être abordés seront ensuite décrits (Chapitre 1). Trois articles exposant les études empiriques formant le corps de la thèse seront ensuite présentés. La première étude avait pour but de documenter la présence, la localisation et l’évolution longitudinale de l’hyperactivation iv associée à une tâche de mémoire épisodique chez des individus qui rencontrent les critères de TCL et qui ont ultérieurement progressé vers une démence (Chapitre 2). La deuxième étude visait à déterminer la trajectoire de l’activation cérébrale associée à une tâche de mémoire associative en fonction du degré de sévérité de la maladie chez un groupe d’individus à risque de développer la MA. Elle avait également pour but de déterminer la présence d’hyperactivation chez des personnes rencontrant les critères de DCS plus (ou DCS+), qui sont des individus présentant une plainte de mémoire ainsi que des marqueurs génétiques et/ou de neurodégénérescence pour la MA (Chapitre 3). La troisième étude avait pour but d’examiner les réseaux cérébraux fonctionnels associés aux régions montrant de l’hyperactivation chez des individus à risque de développer la MA. Elle avait également pour objectif d’évaluer comment l’hyperactivation et ces réseaux cérébraux fonctionnels sont reliés aux performances en mémoire (Chapitre 4). Les résultats découlant de l’étude 1 ont permis de mettre en évidence la présence d’hyperactivation chez des individus présentant un TCL et ayant ultérieurement progressé vers le stade de démence. Les trouvailles de l’étude 2 indiquent qu’une fonction quadratique décrit la relation entre des indices de sévérité de la maladie et l’activation pariétale supérieure gauche chez un groupe d’individus à risque de développer la MA (DCS+ et TCL). Par ailleurs, des niveaux supérieurs d’activation, c’est-à-dire de l’hyperactivation, étaient retrouvés dans les hippocampes + et plusieurs régions temporo-pariétales dans le groupe d’individus DCS . Une hypoactivation pariétale supérieure gauche était plutôt retrouvée chez les individus TCL. Enfin, les résultats de l’étude 3 indiquent que l’hyperactivation de régions prédéterminées est associée à la dysfonction de réseaux cérébraux fonctionnels impliqués dans les processus de mémoire associative dans le DCS+ et le TCL. De plus, ces interactions hyperactivation-réseaux étaient associées à une symptomatologie cognitive croissante. Les implications de cette thèse et ses limites sont abordées dans la discussion (Chapitre 5). / Alzheimer's disease (AD) is the most common cause of dementia in older adults. Its early diagnosis is essential to better understand the brain mechanisms underlying the phenotypical manifestation of the disease and develop consequent interventions. The study of individuals at risk of AD, for example those presenting with subjective cognitive decline (SCD) or mild cognitive impairment (MCI), offers the opportunity to examine the neuropathophysiological processes preceding the dementia stage. This would allow, among other things, to identify early biomarkers of the disease. The general aim of this thesis was to determine the presence of cerebral hyperactivation and to assess functional brain networks associated with hyperactivation. Hyperactivation is defined by the presence of higher levels of brain activation in individuals at risk of AD (i.e. SCD, MCI) in comparison to cognitively healthy controls. Hyperactivation is most often measured with functional magnetic resonance imaging (fMRI) while participants perform a cognitive task. In this thesis, the reader will first be exposed to the studies which used fMRI to examine patterns of brain activation and connectivity in individuals with a clinical diagnosis of AD, MCI or presenting with SCD. Theoretical models resulting from these studies will then be presented. The scientific issues remaining to be addressed to better understand the phenomenon of hyperactivation and its relation to functional brain networks will then be described (Chapter 1). Three empirical studies forming the core of this thesis will be presented. The first study aimed to assess the presence, localization and longitudinal evolution of hyperactivation associated with an episodic memory task in individuals meeting criteria for MCI and having subsequently progressed towards dementia (Chapter 2). The second study aimed to determine the trajectory of brain activation associated with an associative memory task as a function of disease severity in a group of individuals at risk of AD. It also aimed to determine if hyperactivation is present in viii participants meeting criteria for SCD plus (or SCD+), who are individuals presenting with memory complaint in addition to genetic and/or neurodegeneresence markers of AD (Chapter 3). The third and last study aimed to examine patterns of functional connectivity related to regions of hyperactivation, and to assess how hyperactivation and its associated functional networks relate to memory performance in individuals at risk of AD (Chapter 4). Results from the first study highlighted the presence of hyperactivation in individuals with MCI who subsequently progressed to the dementia stage. Findings from the second study revealed a quadratic function describing the relationship between proxies of disease severity (neurodegeneration, memory performance) and left superior parietal activation in a group of individuals at risk of AD (SCD+ and MCI). Moreover, higher levels of activation, i.e. hyperactivation, were found in hippocampal and temporo-parietal regions in the SCD+ group. Hypoactivation was rather found in the left superior parietal area in the MCI group. Finally, results from the third study revealed that hyperactivation of predetermined regions was associated with dysfunction of functional brain networks underlying associative memory in SCD+ and MCI. Moreover, these hyperactivation-network interactions were associated with increasing symptomatology. The implications of this thesis and its limits are addressed in the discussion section (Chapter 5).

Adaptability and flexibility are some of the most important human characteristics. Learning based on new experiences enables adaptation by changing the structural connectivity of the brain through plasticity mechanisms. But the human brain can also adapt to new tasks and situations in a matter of milliseconds by dynamic coordination of functional activation. To understand how this flexibility can be achieved in the computations performed by neural networks, we have to understand how the relatively fixed structural backbone interacts with the functional dynamics. In this thesis, I will analyze these interactions between the structural network connectivity and functional activations and their dynamic interactions on different levels of abstraction and spatial and temporal scales. One of the big questions in neuroscience is how functional interactions in the brain can adapt instantly to different tasks while the brain structure remains almost static. To improve our knowledge of the neural mechanisms involved, I will first analyze how dynamics in functional brain activations can be simulated based on the structural brain connectivity obtained with diffusion tensor imaging. In particular, I will show that a dynamic model of functional connectivity in the human cortex is more predictive of empirically measured functional connectivity than a stationary model of functional dynamics. More specifically, the simulations of a coupled oscillator model predict 54\% of the variance in the empirically measured EEG functional connectivity. Hypotheses of temporal coding have been proposed for the computational role of these dynamic oscillatory interactions on fast timescales. These oscillatory interactions play a role in the dynamic coordination between brain areas as well as between cortical columns or individual cells. Here I will extend neural network models, which learn unsupervised from statistics of natural stimuli, with phase variables that allow temporal coding in distributed representations. The analysis shows that synchronization of these phase variables provides a useful mechanism for binding of activated neurons, contextual coding, and figure ground segregation. Importantly, these results could also provide new insights for improvements of deep learning methods for machine learning tasks. The dynamic coordination in neural networks has also large influences on behavior and cognition. In a behavioral experiment, we analyzed multisensory integration between a native and an augmented sense. The participants were blindfolded and had to estimate their rotation angle based on their native vestibular input and the augmented information. Our results show that subjects alternate in the use between these modalities, indicating that subjects dynamically coordinate the information transfer of the involved brain regions. Dynamic coordination is also highly relevant for the consolidation and retrieval of associative memories. In this regard, I investigated the beneficial effects of sleep for memory consolidation in an electroencephalography (EEG) study. Importantly, the results demonstrate that sleep leads to reduced event-related theta and gamma power in the cortical EEG during the retrieval of associative memories, which could indicate the consolidation of information from hippocampal to neocortical networks. This highlights that cognitive flexibility comprises both dynamic organization on fast timescales and structural changes on slow timescales. Overall, the computational and empirical experiments demonstrate how the brain evolved to a system that can flexibly adapt to any situation in a matter of milliseconds. This flexibility in information processing is enabled by an effective interplay between the structure of the neural network, the functional activations, and the dynamic interactions on fast time scales.

Le trouble comportemental en sommeil paradoxal idiopathique (TCSPi) précède de plusieurs années le diagnostic d’une maladie synucléinopathique. Dans cette étude, nous cherchions à déterminer si la puissance spectrale relative, les composantes rythmiques et arythmiques des spectres de puissance, ainsi que la connectivité fonctionnelle permettaient d’identifier à un temps de base les patients ayant un TCSPi qui développerait une synucléinopathie lors des suivis cliniques annuels. Un enregistrement EEG au repos et une évaluation neuropsychologique ont été conduits auprès de quatre-vingt-un participants atteints d’un TCSPi (66.89 ± 6.91 ans, 20 femmes) et des évaluations neurologiques annuelles étaient menées afin de définir si les patients montraient des symptômes d’une maladie synucléinopathique. La puissance spectrale standard ainsi qu’une estimation spectrale des composantes rythmiques et arythmiques ont été calculées. Ensuite, la connectivité fonctionnelle globale et entre chaque paire d’électrodes ont été estimée par le weighted Phase Lag Index. Après une durée de suivi de 5.01 ± 2.76 ans, 34 participants ont été diagnostiqués avec une synucléinopathie et 47 sont restés exempts de maladie. Comparativement aux participants avec un TCSPi n’ayant pas converti, ceux ayant converti montraient, lors de l’évaluation de base, une puissance spectrale relative plus élevée dans la bande thêta, une pente de la composante arythmique plus abrupte ainsi qu'une puissance rythmique plus élevée en thêta dans les régions occipitales et temporales ainsi qu’en en bêta1 dans les régions frontales. De plus, les patients TCSPi ayant converti présentaient une hyperconnectivité globale dans la bande bêta, mais une hypoconnectivité dans la bande alpha entre les régions temporo-occipitales gauches lors de l’évaluation de base comparativement à ceux n’ayant pas converti. Les altérations mesurables en EEG au repos lors de l’évaluation de base chez les participants avec TCSPi ayant converti vers une maladie synucléinopathique suggèrent une perturbation des réseaux à grande échelle affectés par la neurodégénérescence précoce des structures sous-corticales. / Idiopathic REM sleep behavior disorder (iRBD) precedes the diagnosis of synucleinopathies by several years. In this study, we aimed to determine whether relative spectral power, rhythmic and arrhythmic components of power spectra, and functional connectivity at baseline could identify patients with iRBD who will develop a synucleinopathy at follow-up. Resting-state EEG recordings and neuropsychological evaluations were conducted on eighty-one participants with iRBD (66.89 ± 6.91 years; 20 women), and annual neurological assessments were performed to define the emergence of synucleinopathy symptoms. Standard spectral power and spectral estimates of rhythmic and arrhythmic components were calculated. Additionally, global and pairwise functional connectivity were estimated using the weighted Phase Lag Index. After a follow-up period of 5.01 ± 2.76 years, 34 participants were diagnosed with a synucleinopathic disorder, while 47 remained disease-free. Compared to patients who did not convert, patients who converted at follow-up exhibited higher relative spectral power in the theta band, steeper slopes of the arrhythmic component, and increased rhythmic power in theta in posterior regions and beta1 in frontal regions at baseline evaluation. Furthermore, participants who converted showed hyperconnectivity in the beta band and hypoconnectivity in the alpha band between left temporo-occipital regions at baseline compared to participants who did not convert. The measurable alterations in resting-state EEG at baseline in participants with iRBD who phenoconverted towards a synucleinopathy suggest disruption of large-scale networks affected by early neurodegeneration of subcortical structures.

Les affects, émotions et humeurs sont des processus complexes dont le fonctionnement précis échappe toujours à la neuroscience affective. Un récent mouvement des études IRMf s’est tourné vers la recherche d’effets aux niveaux inter- et intra-individuels en raison du manque d’applicabilité individuelle des résultats provenant de moyennes de groupes basées sur des données transversales. En particulier, la recherche intra-individuelle permet l’étude de liens directs entre l’affectivité et la connectivité chez de mêmes individus à travers le temps. De précédentes études en IRMf rapportent ce type associations chez un unique participant, notre objectif a été d’étudier les effets intra-individuels communs pour un groupe d’individus. Nous avons utilisé le jeu de données Day2day, composé de 40 à 50 sessions pour 6 participants, chaque session incluant des données d’IRMf au repos ainsi que d’auto-évaluations des états affectifs. Nous avons analysé la relation entre l’affectivité et la connectivité fonctionnelle entre des régions cérébrales précédemment liées aux émotions et affects à l’aide de régressions linéaires mixtes multivariées. Nos modèles ont isolé des patrons de connectivité communs et généralisables liés aux variations intra-individuelles de l’affectivité observées au cours de plusieurs semaines et mois. Ces modèles impliquaient particulièrement l’amygdale et l’insula. Nos résultats ouvrent la possibilité de reproduire de tels modèles sur des jeux de données plus larges ainsi qu’à évaluer l’hétérogénéité entre sujets au-delà des effets moyens. La caractérisation de tels processus neurobiologiques pourrait être d’une grande utilité en clinique comme biomarqueur transdiagnostique de l’état affectif ou potentielle cible thérapeutique. / Affects, emotions and moods are complex processes, the precise functioning of which still eludes affective neuroscience. A recent movement in fMRI has turned to research of effects at the inter- and intra-individual level in response to the lack of individual-level applicability of results from cross-sectional group mean studies. In particular, intra-individual research enables the study of direct links between affective states and underlying connectivity in individuals across time. Previous fMRI studies have described these associations in a single participant, our objective was to find shared intraindividual effects across multiple subjects. We have used the Day2day dataset, comprising 40 to 50 sessions for six participants, each session including data from resting-state fMRI scans and self-report measures of state affectivity. We have investigated the relationship between affectivity and connectivity in brain regions linked to emotions and affects using multivariate mixed linear analysis. Our models have isolated common and generalizable patterns of connectivity linked to variations in affectivity observed over multiple weeks and months. These models involved mainly the amygdala and insula. Our results incentivize the re-creation of such modelsin larger datasets, and to assess heterogeneity beyond group mean effects. The characterization of such neurobiological processes could be of great use in a clinical setting as a transdiagnostic biomarker or as a potential therapeutic target.

<p>Land use change constitutes a primary driving force in shaping social-ecological systems world wide, and its effects reach far beyond the directly impacted areas. Graph based landscape ecological tools have become established as a promising way to efficiently explore and analyze the complex, spatial systems dynamics of ecological networks in physical landscapes. However, little attention has been paid to making these approaches operational within ecological assessments, physical planning, and design. This thesis presents a network based, landscape-ecological tool that can be implemented for effective use by practitioners within physical planning and design, and ecological assessments related to these activities. The tool is based on an ecological profile system, a common generalized network model of the ecological infrastructure, graph theoretic metrics, and a spatially explicit, geographically defined representation, deployable in a GIS. Graph theoretic metrics and analysis techniques are able to capture the spatio-temporal dynamics of complex systems, and the generalized network model places the graph theoretic toolbox in a geographically defined landscape. This provides completely new insights for physical planning, and environmental assessment activities. The design of the model is based on the experience gained through seven real-world cases, commissioned by different governmental organizations within Stockholm County. A participatory approach was used in these case studies, involving stakeholders of different backgrounds, in which the tool proved to be flexible and effective in the communication and negotiation of indicators, targets, and impacts. In addition to successful impact predictions for alternative planning scenarios, the tool was able to highlight critical ecological structures within the landscape, both from a system-centric, and a site-centric perspective. In already being deployed and used in planning, assessments, inventories, and monitoring by several of the involved organizations, the tool has proved to effectively meet some of the challenges of application in a multidisciplinary landscape.</p>

While task-based functional magnetic resonance imaging (fMRI) has helped us understand the functional role of many regions in the human brain, many diseases and complex behaviors defy explanation. Alternatively, if no task is performed, the fMRI signal between distant, anatomically connected, brain regions is similar over time. These correlations in “resting state” fMRI have been strongly linked to behavior and disease. Previous work primarily calculated correlation in entire fMRI runs of six minutes or more, making understanding the neural underpinnings of these fluctuations difficult. Recently, coordinated dynamic activity on shorter time scales has been observed in resting state fMRI: correlation calculated in comparatively short sliding windows and quasi-periodic (periodic but not constantly active) spatiotemporal patterns. However, little relevance to behavior or underlying neural activity has been demonstrated. This dissertation addresses this problem, first by using 12.3 second windows to demonstrate a behavior-fMRI relationship previously only observed in entire fMRI runs. Second, simultaneous recording of fMRI and electrical signals from the brains of anesthetized rats is used to demonstrate that both types of dynamic activity have strong correlates in electrophysiology. Very slow neural signals correspond to the quasi-periodic patterns, supporting the idea that low-frequency activity organizes large scale information transfer in the brain. This work both validates the use of dynamic analysis of resting state fMRI, and provides a starting point for the investigation of the systemic basis of many neuropsychiatric diseases.