Human infants have been shown to have an innate capacity to acquire their mother tongue. In recent decades, the advent of the functional magnetic resonance imaging (fMRI) technique has made it feasible to explore the neural basis underlying language acquisition and processing in children, even in newborn infants (for reviews, see Kuhl & Rivera-Gaxiola, 2008; Kuhl, 2010) . Spontaneous low-frequency (< 0.1 Hz) fluctuations (LFFs) in the resting brain have been shown to be physiologically meaningful in the seminal study (Biswal et al., 1995) . Compared to task-based fMRI, resting-state fMRI (rs-fMRI) has some unique advantages in neuroimaging research, especially in obtaining data from pediatric and clinical populations. Moreover, it enables us to characterize the functional organization of the brain in a systematic manner in the absence of explicit tasks. Among brain systems, the language network has been well investigated by analyzing LFFs in the resting brain. This thesis attempts to investigate the functional connectivity within the language network in typically developing preschool children and the covariation of this connectivity with children’s language development by using the rs-fMRI technique. The first study (see Chapter 2.1; Xiao et al., 2016a) revealed connectivity differences in language-related regions between 5-year-olds and adults, and demonstrated distinct correlation patterns between functional connections within the language network and sentence comprehension performance in children. The results showed a left fronto-temporal connection for processing syntactically more complex sentences, suggesting that this connection is already in place at age 5 when it is needed for complex sentence comprehension, even though the whole functional network is still immature. In the second study (see Chapter 2.2; Xiao et al., 2016b), sentence comprehension performance and rs-fMRI data were obtained from a cohort of children at age 5 and a one-year follow-up. This study examined the changes in functional connectivity in the developing brain and their relation to the development of language abilities. The findings showed that the development of intrinsic functional connectivity in preschool children over the course of one year is clearly observable and individual differences in this development are related to the advancement in sentence comprehension ability with age. In summary, the present thesis provides new insights into the relationship between intrinsic functional connectivity in the brain and language processing, as well as between the changes in intrinsic functional connectivity and concurrent language development in preschool children. Moreover, it allows for a better understanding of the neural mechanisms underlying language processing and the advancement of language abilities in the developing brain.

Human infants have been shown to have an innate capacity to acquire their mother tongue. In recent decades, the advent of the functional magnetic resonance imaging (fMRI) technique has made it feasible to explore the neural basis underlying language acquisition and processing in children, even in newborn infants (for reviews, see Kuhl & Rivera-Gaxiola, 2008; Kuhl, 2010) . Spontaneous low-frequency (< 0.1 Hz) fluctuations (LFFs) in the resting brain have been shown to be physiologically meaningful in the seminal study (Biswal et al., 1995) . Compared to task-based fMRI, resting-state fMRI (rs-fMRI) has some unique advantages in neuroimaging research, especially in obtaining data from pediatric and clinical populations. Moreover, it enables us to characterize the functional organization of the brain in a systematic manner in the absence of explicit tasks. Among brain systems, the language network has been well investigated by analyzing LFFs in the resting brain. This thesis attempts to investigate the functional connectivity within the language network in typically developing preschool children and the covariation of this connectivity with children’s language development by using the rs-fMRI technique. The first study (see Chapter 2.1; Xiao et al., 2016a) revealed connectivity differences in language-related regions between 5-year-olds and adults, and demonstrated distinct correlation patterns between functional connections within the language network and sentence comprehension performance in children. The results showed a left fronto-temporal connection for processing syntactically more complex sentences, suggesting that this connection is already in place at age 5 when it is needed for complex sentence comprehension, even though the whole functional network is still immature. In the second study (see Chapter 2.2; Xiao et al., 2016b), sentence comprehension performance and rs-fMRI data were obtained from a cohort of children at age 5 and a one-year follow-up. This study examined the changes in functional connectivity in the developing brain and their relation to the development of language abilities. The findings showed that the development of intrinsic functional connectivity in preschool children over the course of one year is clearly observable and individual differences in this development are related to the advancement in sentence comprehension ability with age. In summary, the present thesis provides new insights into the relationship between intrinsic functional connectivity in the brain and language processing, as well as between the changes in intrinsic functional connectivity and concurrent language development in preschool children. Moreover, it allows for a better understanding of the neural mechanisms underlying language processing and the advancement of language abilities in the developing brain.

This doctoral thesis aims to demonstrate the relevance of resting-state functional connectivity (RSFC) for the study of brain function. RSFC refers to the spontaneous brain activity structured in intrinsic connectivity networks. These networks mirror task-based activations and show significant variations across several behavioral domains and phenotypical traits. Furthermore, changes in these networks after, for instance, pharmacological manipulations, may disentangle the specific role of several neurotransmitters systems in normal and pathological functional connectivity. While various neuroimaging techniques enable the detection of intrinsic connectivity networks, data-driven methods, such as independent component analysis, provide a robust spatial representation of brain networks that are distinguishable from physiological signals and scanner noise. Within the above-mentioned framework, this thesis presents data from two studies designed to better understand 1) individual differences in decision making reflected in intrinsic network connectivity and 2) variations in intrinsic network connectivity following serotonergic manipulations. The first part is the general introduction where I present the theoretical background, the methodology used in both experiments and an overview of the current research related to the studies of this thesis. The second chapter presents the first study, which examined the relationship between a set of value-based decision-making parameters with large-scale intrinsic connectivity networks. Findings of this study revealed that individuals who prefer to gamble in order to avoid a sure loss, exhibit stronger connectivity between the default mode and left frontoparietal systems to their adjacent brain regions, especially to those involved in prospective thinking, affective decision making and visual processing. The third chapter presents the second experimental study, which examined changes in default mode network connectivity after two tryptophan interventions to increase and decrease brain serotonin synthesis, and a control condition. Results of this study showed decreased functional connectivity between the default mode network and emotion-related regions associated with higher serotonin brain levels. Finally, the fourth chapter includes a general discussion that integrates the significance of the findings from both studies. In this section, limitations and recommendations for future research are also considered before presenting the conclusion that highlights the contribution of this work for unraveling the continuous activity of the resting brain.:1.CONTENTS 1 LIST OF FIGURES 3 LIST OF TABLES 4 ABBREVIATIONS 5 ABSTRACT 7 GENERAL INTRODUCTION 9 1.1. Resting-state functional connectivity: the silent work of the resting brain 9 1.2. Intrinsic connectivity networks 12 1.3. Independent Component Analysis 17 1.4. Summary: research objectives and study hypotheses 20 STUDY I: Risk seeking for losses modulates the functional connectivity of the default 2.mode and left frontoparietal networks in young males 22 2.1. Abstract 23 2.2. Introduction 24 2.3. Materials and Methods 26 2.4. Results 33 2.5. Discussion 41 2.6. Notes 44 2.7. Supplemental Material Study I 45 3. STUDY II: Acute Tryptophan Loading Decreases Functional Connectivity between the Default Mode Network and Emotion-Related Brain Regions 49 3.1. Abstract 50 3.2. Introduction 51 3.3. Materials and Methods 53 3.4. Results 61 3.5. Discussion 67 3.6. Acknowledgments 71 3.7. Supplemental Material Study II 72 4. GENERAL DISCUSSION 78 4.1. Research objectives and summary of results 78 4.2. Risk seeking for losses is associated with changes in default mode and frontoparietal systems 79 4.3. Higher serotonin brain synthesis decreases DMN connectivity 80 4.4. Integration of findings 81 4.5. Limitations and future directions 83 4.6. General conclusion 85 5. ZUSAMMENFASSUNG 86 Hintergrund 86 Fragestellung 86 Material und Methoden 87 Ergebnisse 88 Schlussfolgerungen 89 6. SUMMARY 90 Background 90 Research question 90 Material and Methods 91 Results 92 Conclusion 92 7.REFERENCES 93 8.ANNEX 113 8.1. Publikationsverzeichnis 113 8.3.Danksagung 115 8.4. Erklärungen zur Eröffnung des Promotionsverfahrens 116 8.5. Erklärung zur Einhaltung gesetzlicher Vorgaben118 8.6. Erklärungen zur Publikation 119

Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.