Le microcèbe murin (Microcebus murinus) est un primate attirant l’attention de la recherche neuroscientifique. Son anatomie cérébrale est encore mal décrite et ses réseaux cérébraux n'ont jamais été étudiés. Le premier objectif de cette thèse était de développer de nouveaux outils menant à la création d’un atlas numérique 3D du cerveau du microcèbe. Cet atlas est un outil fondamental car pouvant être utilisé pour extraire automatiquement des biomarqueurs cérébraux de diverses neuropathologies. Par la suite, nous avons mis en place des protocoles IRM et informatiques pour analyser la connectivité neuronale du microcèbe murin. Nous avons évalué pour la première fois les réseaux cérébraux de cet animal et révélé que son cerveau est organisé en régions fonctionnelles intégrées dans des réseaux fonctionnels à plus grande échelle. Ces réseaux ont été classés et comparés à des réseaux similaires chez l'homme. Cette comparaison multi-espèces a mis en évidence des règles d'organisation communes mais aussi des divergences. L'imagerie du glutamate par transfert de saturation et par échange chimique (gluCEST) est une méthode permettant de créer des cartes 3D de la distribution du glutamate. Dans une troisième étude, nous avons comparé l’activité neuronale locale, la connectivité fonctionnelle et le contraste gluCEST dans diverses régions du cerveau. Nous avons ainsi mis en évidence différentes associations entre ces trois biomarqueurs. Enfin, l’impact du vieillissement sur la connectivité fonctionnelle, l’activité neuronale locale et le contraste gluCEST a été évalué en comparant deux cohortes de microcèbes murins. / The mouse lemur (Microcebus murinus) is a primate that has attracted attention within neuroscience research. Its cerebral anatomy is still poorly described and its cerebral networks have never been investigated. The first objective of this study was to develop new tools to create a 3D digital atlas of the brain of this model and to use this atlas to automatically follow-up brain characteristics in cohorts of animals. We then implemented protocols to analyze connectivity in mouse lemurs so we could evaluate for the first time the cerebral networks in this species. We revealed that the mouse lemur brain is organised in local functional regions integrated within large scale functional networks. These latter networks were classified and compared to large scale networks in humans. This multispecies comparison highlighted common organization rules but also discrepancies. Additionally, Chemical Exchange Saturation Transfer imaging of glutamate (gluCEST) is a method that allows the creation of 3D maps weighted by the glutamate distribution. In a third study, we compared local neuronal activity, functional connectivity and gluCEST contrast in various brain regions. We highlighted various associations between these three biomarkers. Lastly, the impact of aging on local neuronal activity, functional connectivity and gluCEST has been analyzed by comparing two cohorts of lemurs.

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.:1 Introduction 1.1 General Introduction 1.2 Functional Magnetic Resonance Imaging 1.3 Resting-state fMRI 1.4 Brain Networks and Graph Theory 1.5 White-Matter Lesions and Small Vessel Disease 1.6 Transcranial Direct Current Stimulation 1.7 Dynamic Functional Connectivity 2 Publications 2.1 Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity 2.2 Early small vessel disease affects fronto-parietal and cerebellar hubs in close correlation with clinical symptoms - A resting-state fMRI study 2.3 Dynamic modulation of intrinsic functional connectivity by transcranial direct current stimulation 2.4 Three-dimensional mean-shift edge bundling for the visualization of functional connectivity in the brain 2.5 Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI 3 Summary 4 Bibliography 5. Appendix 5.1 Erklärung über die eigenständige Abfassung der Arbeit 5.2 Curriculum vitae 5.3 Publications 5.4 Acknowledgements

Clinical care for major depressive disorder (MDD) would be greatly improved if we had reliable clinical predictors of individual antidepressant treatment outcome. While, at the present time, no biomarkers have sufficiently proven utility to be ready for clinical application, several neuroimaging modalities have shown promise for such development. Attempts to combine the recently developed modality of resting-state functional Magnetic Resonance Imaging (rs-fMRI) with pharmacological challenges to explore the impact of antidepressants on resting-state brain connectivity have just begun (McCabe et al., 2011a, McCabe et al., 2011b). The aim of the current study was to investigate the effects of a single dose of the SSRI (selective serotonin reuptake inhibitor) escitalopram on resting-state functional connectivity in health.

Title Repeated individual comparasion of heart rate on the land and in the water. Objective Find if exist significant difference of heart rate in repeated measurements between rating heart rate on dry land and in the water. Used methods The quantitative empirical research was carried out. The research method was the measurement of resting heart rate. Measurements were carried out in the form of intraindividual comparative analysis among six probands who were repeatedly tested supine for 5 minutes on land and subsequently 5 minutes in water. The water temperature was 30 řC and the water column height was 30 cm. Heart rate was measured using sporttester. Results The same tendency of reaction of the organism during repeated measurements did not confirm in 4 cases. Results in two cases confirmed the trend of decreasing heart rate during a stay in the water. The most measured resting heart rate decreased in water compared with the resting heart rate on dry land by 21.1% and increased the most by 6.1%. Keywords swimming, water, heart rate, resting heart rate, diving

Att undersöka områden i hjärnan som är aktiva utan att någon stimulans sker kan ge information om en individs standardnätverks basnivå. Denna basnivå kan användas för att identifiera avvikande spatiala mönster i hjärnan som associeras med sjukdomar och funktionsnedsättningar. Denna uppsats syftar till att undersöka hur skillnaderna ser ut för individspecifika nätverkskartor genom att jämföra tre olika anpassningar av Dual Regression, en frekventistisk och två Bayesianska modeller. Datamaterialet som analyseras i uppsatsen är från Cambridge-Buckner, en del av 1000 Functional Connectomes Project som innehåller fMRI-data. Från datamaterialet har även tillhörande förhandsskattade gruppvisa oberoende komponenter erhållits från 20 utvalda individer vilket sedan används i uppsatsen för att skatta individspecifika nätverkskartor i hjärnan för tre individer från studien. Det anpassas tre olika Dual Regressions-modeller: En frekventistisk modell med homoskedastisk varians, en Bayesiansk modell med heteroskedastisk varians med okorrelade feltermer samt en Bayesiansk modell med heteroskedastisk varians och korrelerade feltermer. För de två Bayesianska modellerna används icke-informativa priorfördelningar. Dessa olika modeller skiljer sig åt då de kan ta hänsyn till olika mängder av information genom att ha olika komplexa kovariansstrukturer. Det observeras att den frekventistiska modellen och den Bayesianska modellen med heteroskedastisk varians och okorrelerade feltermer skattar nätverk som är i stor utsträckning lika varandra. Den Bayesianska modellen med heteroskedastisk varians och korrelerade feltermer tenderar att skatta nätverk som är skild från de andra modellerna, där det ofta förekom skillnader i nätverkens former samt en del amplitudskillnader. I kovariansmatrisen för den Bayesianska modellen med heteroskedatisk varians och korrelerade feltermer observeras ett flertal höga korrelationer mellan feltermerna vilket indikerar på att det bör tas hänsyn till korrelerade feltermer. Det diskuteras även om problem som förekommer hos respektive tillvägagångssätt för att skatta modellen, där frekventistiska tillvägagångssättet inte tar hänsyn till all information i data men är enkel att anpassa. Den Bayesianska modellen med heteroskedastisk varians och okorrelerade feltermer ger liknande resultat som det frekventistiska tillvägagångssättet. Den Bayesianska modellen med heteroskedastisk varians och korrelerade feltermer ger resultat som anpassar data bättre än de andra två modellerna men är mer komplex att beräkna. / Examining regions in the brain that are active without any stimuli gives information about an individual's default brain networks. These default mode networks can be analyzed to identify deviating spatial patterns in the brain that are associated with diseases and disabilities. This thesis aims to analyze the difference in how frequentist and Bayesian Dual Regression estimates subject specific spatial-maps. We received pre-estimated groupwise independent components from 20 individuals based off of fMRI-data from the Cambridge-Buckner dataset which is part of the 1000 Functional Connectomes Project. These are later used to create subject specific spatial-maps for 3 individuals in the study. In this thesis 3 different types of Dual Regression models will be fitted: A frequentist Dual Regression, A Bayesian model with heteroscedastic variance and uncorrelated error terms and a Bayesian model with heteroscedastic variance and correlated error terms. Non-informative prior distributions are used for both Bayesian models. As these 3 models can account for varying amounts of information in the data due to varying complexity of the covariance structure some difference were observed in the subject specific maps. The frequentist Dual Regression and the Bayesian model with heteroscedastic variance and uncorrelated error terms often showed similar results, however the resulting networks from the Bayesian model with heteroscedastic variance and correlated error terms often differed from the other two models. The difference was observed both in network shapes and in activation amplitude. The covariance matrix for the Bayesian model with heteroscedastic variance and correlated error terms contained a number of high correlations between the error terms, indicating that correlation among error terms should be taken into account. Some arguments are made for respective way of fitting the model as each model has its unique advantage and disadvantage; where the frequentist model does not take into account all information from the data it is easy to fit. The Bayesian model with heteroscedastic variance and uncorrelated error terms is also relatively easy to fit and provides similar results to the frequentist model. The Bayesian model with heteroscedastic variance and correlated error terms however does account for more information and yields better results but is more computationally expensive.

Diapause is often an adaptation for survival during periods of harsh environmental conditions. Some diapausing stages do not terminate the dormancy once the favourable conditions are restored. Such prolonged diapause may be enforced by environment if a diapausing stage cannot be reached by the cues inducing termination of dormancy. However, it may also be an advantageous bet-hedging strategy to allow only a fraction of dormant stages produced in any given season to hatch the next time conditions become favourable. I tested whether such strategy can be observed in hatching patterns of dormant eggs of Daphnia obtusa - a cladoceran occurring in small Central European temporary waters. I investigated the influence of intensity of illumination on hatching success, and effect of isolating the eggs encased in ephippia from the sediment. Fraction of eggs terminating diapause, fraction of embryos successfully leaving the egg membranes, and timing of the response were assessed at 15 ˚C under four intensities of illumination (100% = 35µmol.m2 .s-1 , 75%, 50%, 25%; photoperiod 12h light: 12h dark) and in complete darkness for 21 days. My results support previous suggestions that there is no genetically-fixed bet-hedging strategy in D. obtusa. I observed high proportion of eggs which terminated diapause in all...

The cognitive decline associated with healthy aging begins in early adulthood and is important to understand as a precursor of and relative to mild cognitive impairment and Alzheimer disease. Anatomical atrophy, functional compensation, and network reorganization have been observed in populations of older adults. In the current study, we examine functional network correlates of memory performance on the Wechsler Memory Scale IV and the Mnemonic Discrimination Task (MST). We report a lack of association between global graph theory metrics and age or memory performance. In addition, we observed a positive association between lure discrimination scores from the MST and right hippocampus centrality. Upon further investigation, we confirmed that old subjects with poor memory performance had lower right hippocampus centrality scores than young subjects with high average memory performance. These novel results connect the role of the hippocampus in global brain network information flow to cognitive function and have implications for better characterizing and predicting memory decline in aging.

Indiana University-Purdue University Indianapolis (IUPUI) / The social cognitive ability to identify another’s internal state and social affective ability to share another’s emotional experience, known as empathy, are integral to healthy social functioning. During tasks, neural systems active when adolescents empathize include cognitive (medial prefrontal cortex and posterior cingulate cortex with the dorsolateral prefrontal cortex) and affective (anterior insula and anterior cingulate cortex) regions that are consistent with the adult task-based literature implicating the default mode, salience, and frontoparietal networks. However, task-based studies are limited to examining neural regions probed by the task; thus, do not capture broader patterns of information processing associated with complex processes, such as empathy. Methods of functional connectivity capture broader patterns of information processing at the level of network connectivity. Although it has clear advantages in identifying neural vulnerabilities to disorder, functional connectivity has yet to be used in adolescent investigations of empathy. Via parent- and self-report, deficits in either cognitive or affective processes central to empathy associate with the most widely agreed on classifications of behavioral disorders in adolescents – transdiagnostic symptoms of internalizing and externalizing. However, this evidence relies exclusively on self-report measures and research has yet to examine the neural connectivity underlying transdiagnostic symptoms in relation to cognitive and affective empathy. What has yet to be known is (1) how the social cognitive and affective processes of empathy are functionally connected across a heterogeneous sample of adolescents and (2) the association of cognitive, affective, and imbalanced empathy with transdiagnostic symptoms. Addressing these gaps in knowledge is an important incremental step for specifying vulnerabilities not fully captured via subjective report alone. This information can be used to improve prevention and intervention strategies. The present study will examine the functional connectivity of neural networks underlying empathy in early to mid-adolescents and their association with transdiagnostic symptoms.

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Frontal Alpha Asymmetry (FAA) has been associated with individual differences such as various aspects of personality. However, the nature of the relationship between FAA and personality traits is not yet fully understood. The present study further investigated this relationship by exploring the correlation between resting-state FAA and the Big Five personality traits: openness, agreeableness, conscientiousness, extraversion, and neuroticism. 15 healthy participants completed resting-state EEG recordings three times and the Big Five Personality Inventory (BFI) twice. The results showed only one statistically significant correlation among the 20 correlations examined, between the F4-F3 resting-state FAA and openness scores. Besides, the direction of the relationship was the opposite of what would be expected. The small sample size of this study may have contributed to results, indicating the need for future research with larger samples. Nonetheless, the current findings add to the existing literature and suggest that the relationship between resting-state FAA and personality traits may be more complex than previously thought.