Alcohol use disorder (AUD) is a widespread mental disease denoted by chronic alcohol use despite significant negative consequences for a person’s life. It affected more than 14 million persons in Europe alone and accounted for more than 5% of deaths worldwide in 2011-2012. Understanding the psychological and neurobiological mechanisms driving the development and maintenance of pathological alcohol use is key to conceptualizing new programs for prevention and therapy of AUD. There has been a variety of etiological models trying to describe and relate these mechanisms. Lately, the view of AUD as a disorder of learning and decision making has received much support proposing dual systems to be at work in AUD – one system being deliberate, forward-planning, and goal-directed and the other one reflexive, automatic, and habitual. Both systems supposedly work in parallel in a framework of value-based decision making and their balance can be flexibly adjusted in healthy agents, while a progressive imbalance favoring habitual over goal-directed choice strategies is assumed in AUD. This imbalance has been theoretically associated to neural adaptations to chronic alcohol use in corticostriatal pathways involved in reward processing, especially in ventral striatum. However, these theoretical models are grounded strongly on animal research while empirical research in the human domain remains rather sparse and inconclusive. Furthermore, alterations in value-based decision-making processes and their neural implementation might not only result from prolonged alcohol misuse but may also represent premorbid interindividual differences posing a risk factor for the development of AUD. Therefore, I here present three studies investigating the relation of alcohol use with the balance between goal-directed and habitual decision systems and with parameters modulating option valuation processes of these systems, namely delay, risk, and valence of option outcomes. To separate the investigation of these decision processes as predisposing risk for or consequence of alcohol use, two samples were examined: one sample of 201 eighteen-year-old men being neither abstinent from nor dependent on alcohol as well as one sample of 114 AUD patients in detoxification treatment and 98 control participants matched for age, sex, educational background, and smoking status. Both samples had a baseline assessment of several behavioral tasks, questionnaires, and neuropsychological testing and were followed-up over one year to examine drinking trajectories in the sample of young men and relapse in detoxified patients. The behavioral tasks included a sequential choice task using model-free and model-based reinforcement learning as operationalization of habitual and goal-directed decision making, respectively, during functional magnetic resonance imaging and four tasks probing participants’ delay discounting, probability discounting for gains and losses, and loss aversion. Study 1 presents the cross-sectional analysis of the sequential choice task in relation to baseline drinking behavior of the young-adult sample. These analyses did not reveal an association between non-pathological alcohol use and habitual and goal-directed control on neither a behavioral nor neural level except for one exploratory finding of increased BOLD responses to model-free habitual learning signals in participants with earlier onset of drinking. Study 2 examined the same task in AUD patients compared to control participants showing no difference in behavioral control or neural correlates between those groups. However, prospectively relapsing AUD patients showed lower BOLD responses associated to model-based goal-directed control than abstaining patients and control participants. Additionally, the interaction of goal-directed control and positive expectancies of alcohol effects discriminated subsequently relapsing and abstaining patients revealing an increased risk of relapse for those patients who showed higher levels of goal-directed control and low alcohol expectancies or low levels of goal-directedness and high expectancies. Study 3 examined modulating features of goal-directed and habitual option valuation – delay, risk, and valence of options – in association to alcohol use in the young-adult sample and AUD status in the sample of patients and matched control participants on a cross-sectional as well as longitudinal level. This study revealed no relation of delay, risk, and loss aversion with current alcohol use and consumption one year later in the young men. In contrast, AUD patients showed systematically more impulsive choice behavior than control participants in all four tasks: a higher preference for immediate rewards, more risky choices when facing gains and less when facing losses, and lower loss aversion. Furthermore, a general tendency to overestimate the probability of uncertain losses could predict relapse risk over the following year in AUD patients. Taken together, these results do not support the hypothesis that mechanisms of value-based decision making might be predisposing risk factors for alcohol consumption. The findings for patients already suffering from AUD are mixed: while choice biases regarding delays, risks, and valence of option outcomes seem to be altered systematically in AUD, there was no indication of an imbalance of habitual and goal-directed control. These findings challenge the assumption of a generalized outcome-unspecific shift of behavioral control from goal-directed to habitual strategies during the development of AUD and point towards several possible future avenues of research to modify or extend the theoretical model.

Alcohol use disorder (AUD) is a widespread mental disease denoted by chronic alcohol use despite significant negative consequences for a person’s life. It affected more than 14 million persons in Europe alone and accounted for more than 5% of deaths worldwide in 2011-2012. Understanding the psychological and neurobiological mechanisms driving the development and maintenance of pathological alcohol use is key to conceptualizing new programs for prevention and therapy of AUD. There has been a variety of etiological models trying to describe and relate these mechanisms. Lately, the view of AUD as a disorder of learning and decision making has received much support proposing dual systems to be at work in AUD – one system being deliberate, forward-planning, and goal-directed and the other one reflexive, automatic, and habitual. Both systems supposedly work in parallel in a framework of value-based decision making and their balance can be flexibly adjusted in healthy agents, while a progressive imbalance favoring habitual over goal-directed choice strategies is assumed in AUD. This imbalance has been theoretically associated to neural adaptations to chronic alcohol use in corticostriatal pathways involved in reward processing, especially in ventral striatum. However, these theoretical models are grounded strongly on animal research while empirical research in the human domain remains rather sparse and inconclusive. Furthermore, alterations in value-based decision-making processes and their neural implementation might not only result from prolonged alcohol misuse but may also represent premorbid interindividual differences posing a risk factor for the development of AUD. Therefore, I here present three studies investigating the relation of alcohol use with the balance between goal-directed and habitual decision systems and with parameters modulating option valuation processes of these systems, namely delay, risk, and valence of option outcomes. To separate the investigation of these decision processes as predisposing risk for or consequence of alcohol use, two samples were examined: one sample of 201 eighteen-year-old men being neither abstinent from nor dependent on alcohol as well as one sample of 114 AUD patients in detoxification treatment and 98 control participants matched for age, sex, educational background, and smoking status. Both samples had a baseline assessment of several behavioral tasks, questionnaires, and neuropsychological testing and were followed-up over one year to examine drinking trajectories in the sample of young men and relapse in detoxified patients. The behavioral tasks included a sequential choice task using model-free and model-based reinforcement learning as operationalization of habitual and goal-directed decision making, respectively, during functional magnetic resonance imaging and four tasks probing participants’ delay discounting, probability discounting for gains and losses, and loss aversion. Study 1 presents the cross-sectional analysis of the sequential choice task in relation to baseline drinking behavior of the young-adult sample. These analyses did not reveal an association between non-pathological alcohol use and habitual and goal-directed control on neither a behavioral nor neural level except for one exploratory finding of increased BOLD responses to model-free habitual learning signals in participants with earlier onset of drinking. Study 2 examined the same task in AUD patients compared to control participants showing no difference in behavioral control or neural correlates between those groups. However, prospectively relapsing AUD patients showed lower BOLD responses associated to model-based goal-directed control than abstaining patients and control participants. Additionally, the interaction of goal-directed control and positive expectancies of alcohol effects discriminated subsequently relapsing and abstaining patients revealing an increased risk of relapse for those patients who showed higher levels of goal-directed control and low alcohol expectancies or low levels of goal-directedness and high expectancies. Study 3 examined modulating features of goal-directed and habitual option valuation – delay, risk, and valence of options – in association to alcohol use in the young-adult sample and AUD status in the sample of patients and matched control participants on a cross-sectional as well as longitudinal level. This study revealed no relation of delay, risk, and loss aversion with current alcohol use and consumption one year later in the young men. In contrast, AUD patients showed systematically more impulsive choice behavior than control participants in all four tasks: a higher preference for immediate rewards, more risky choices when facing gains and less when facing losses, and lower loss aversion. Furthermore, a general tendency to overestimate the probability of uncertain losses could predict relapse risk over the following year in AUD patients. Taken together, these results do not support the hypothesis that mechanisms of value-based decision making might be predisposing risk factors for alcohol consumption. The findings for patients already suffering from AUD are mixed: while choice biases regarding delays, risks, and valence of option outcomes seem to be altered systematically in AUD, there was no indication of an imbalance of habitual and goal-directed control. These findings challenge the assumption of a generalized outcome-unspecific shift of behavioral control from goal-directed to habitual strategies during the development of AUD and point towards several possible future avenues of research to modify or extend the theoretical model.:Table of Contents List of Figures List of Tables List of Abbreviations Abstract Chapter 1. Perspectives on alcohol use disorder 1.1 The size of alcohol use disorder 1.1.1 Terminology of alcohol-use related disorders 1.1.2 Size and burden of alcohol consumption and alcohol use disorders 1.2 Cognitive psychological perspectives on alcohol use disorder 1.2.1 A unified framework for addiction 1.2.2 Value-based decision making 1.2.3 Goal-directed and habitual systems 1.3 Neurobiological perspectives on alcohol use disorders 1.3.1 Neural underpinnings of the reward circuit 1.3.2 Neural underpinning of goal-directed and habitual decision making 1.3.3 Striatal adaptations associated with chronic alcohol consumption 1.4 Synopsis and research questions Chapter 2. Study 1 2.1 Abstract 2.2 Introduction 2.3 Material and methods 2.3.1 Participants and procedure 2.3.2 Measures of goal-directed and habitual behavioral control 2.3.3 Measure of alcohol consumption 2.3.4 Behavioral statistical analyses 2.3.5 Functional magnetic resonance imaging data acquisition and analysis 2.4 Results 2.4.1 Sample characteristics 2.4.2 Behavioral results 2.4.3 Functional magnetic resonance imaging results 2.5 Discussion Chapter 3. Study 2 3.1 Abstract 3.2 Introduction 3.3 Methods and materials 3.3.1 Participants 3.3.2 Procedure 3.3.3 Alcohol Expectancy Questionnaire 3.3.4 Task 3.3.5 Magnetic Resonance Imaging 3.3.6 Follow-up procedure 3.3.7 Data analysis 3.3.8 fMRI analysis 3.4 Results 3.4.1 Sample characteristics 3.4.2 Task-related group differences 3.4.3 Interaction between alcohol expectancies and model-based control 3.4.4 fMRI results 3.5 Discussion Chapter 4. Study 3 4.1 Abstract 4.2 Introduction 4.3 Study 3.1 4.3.1 Material and methods 4.3.2 Results 4.4 Study 3.2 4.4.1 Material and methods 4.4.2 Results 4.5 Discussion Chapter 5. General discussion 5.1 Summary of findings and discussion 5.1.1 Goal-directed and habitual decision making and alcohol use (disorder) 5.1.2 Neuroimaging correlates of goal-directed and habitual control 5.1.3 Modulators of the valuation systems and alcohol use (disorders) 5.1.4 Integration of findings 5.2 Limitations 5.2.1 Methodological critique of the Two-Step task 5.3 Outlook for future studies 5.3.1 Tentative framework for future studies 5.4 Conclusions References Appendix A Supplementary Information of Study 1 A.1 Supplementary Methods 1 - behavioral A.2 Supplementary Methods 2 - fMRI A.3 Supplementary Results - behavioral A.4 Supplementary results - fMRI B Supplementary Information of Study 2 B.1 Computational fits B.2 Preprocessing of the functional imaging data B.3 Exclusion criteria for different analyses B.4 First level analysis of the functional imaging analysis B.5 Voxel-based morphometry B.6 Drinking Motives Questionnaire B.7 Model-free comparisons B.8 Association with time to relapse B.9 Number of detoxifications and model-based control: behavioral and neuroimaging analyses C Supplementary Information of Study 3 C.1 Differences between VBDM version used in this study compared to the VBDM version reported in Pooseh et al. (under review) C.2 Additional correlational analyses D Supplementary Information for additional analyses

The ability to provide succinct information about why a property does, or does not, hold in a given system is a key feature in the context of formal verification and model checking. It can be used both to explain the behavior of the system to a user of verification software, and as a tool to aid automated abstraction and synthesis procedures. Counterexample traces, which are executions of the system that do not satisfy the desired specification, are a classical example. Specifications of systems with probabilistic behavior usually require that an event happens with sufficiently high (or low) probability. In general, single executions of the system are not enough to demonstrate that such a specification holds. Rather, standard witnesses in this setting are sets of executions which in sum exceed the required probability bound. In this thesis we consider methods to certify and witness that probabilistic reachability constraints hold in Markov decision processes (MDPs) and probabilistic timed automata (PTA). Probabilistic reachability constraints are threshold conditions on the maximal or minimal probability of reaching a set of target-states in the system. The threshold condition may represent an upper or lower bound and be strict or non-strict. We show that the model-checking problem for each type of constraint can be formulated as a satisfiability problem of a system of linear inequalities. These inequalities correspond closely to the probabilistic transition matrix of the MDP. Solutions of the inequalities are called Farkas certificates for the corresponding property, as they can indeed be used to easily validate that the property holds. By themselves, Farkas certificates do not explain why the corresponding probabilistic reachability constraint holds in the considered MDP. To demonstrate that the maximal reachability probability in an MDP is above a certain threshold, a commonly used notion are witnessing subsystems. A subsystem is a witness if the MDP satisfies the lower bound on the optimal reachability probability even if all states not included in the subsystem are made rejecting trap states. Hence, a subsystem is a part of the MDP which by itself satisfies the lower-bounded threshold constraint on the optimal probability of reaching the target-states. We consider witnessing subsystems for lower bounds on both the maximal and minimal reachability probabilities, and show that Farkas certificates and witnessing subsystems are related. More precisely, the support (i.e., the indices with a non-zero entry) of a Farkas certificate induces the state-space of a witnessing subsystem for the corresponding property. Vice versa, given a witnessing subsystem one can compute a Farkas certificate whose support corresponds to the state-space of the witness. This insight yields novel algorithms and heuristics to compute small and minimal witnessing subsystems. To compute minimal witnesses, we propose mixed-integer linear programming formulations whose solutions are Farkas certificates with minimal support. We show that the corresponding decision problem is NP-complete even for acyclic Markov chains, which supports the use of integer programs to solve it. As this approach does not scale well to large instances, we introduce the quotient-sum heuristic, which is based on iteratively solving a sequence of linear programs. The solutions of these linear programs are also Farkas certificates. In an experimental evaluation we show that the quotient-sum heuristic is competitive with state-of-the-art methods. A large part of the algorithms proposed in this thesis are implemented in the tool SWITSS. We study the complexity of computing minimal witnessing subsystems for probabilistic systems that are similar to trees or paths. Formally, this is captured by the notions of tree width and path width. Our main result here is that the problem of computing minimal witnessing subsystems remains NP-complete even for Markov chains with bounded path width. The hardness proof identifies a new source of combinatorial hardness in the corresponding decision problem. Probabilistic timed automata generalize MDPs by including a set of clocks whose values determine which transitions are enabled. They are widely used to model and verify real-time systems. Due to the continuously-valued clocks, their underlying state-space is inherently uncountable. Hence, the methods that we describe for finite-state MDPs do not carry over directly to PTA. Furthermore, a good notion of witness for PTA should also take into account timing aspects. We define two kinds of subsystems for PTA, one for maximal and one for minimal reachability probabilities, respectively. As for MDPs, a subsystem of a PTA is called a witness for a lower-bounded constraint on the (maximal or minimal) reachability probability, if it itself satisfies this constraint. Then, we show that witnessing subsystems of PTA induce Farkas certificates in certain finite-state quotients of the PTA. Vice versa, Farkas certificates of such a quotient induce witnesses of the PTA. Again, the support of the Farkas certificates corresponds to the states included in the subsystem. These insights are used to describe algorithms for the computation of minimal witnessing subsystems for PTA, with respect to three different notions of size. One of them counts the number of locations in the subsystem, while the other two take into account the possible clock valuations in the subsystem.:1 Introduction 2 Preliminaries 3 Farkas certificates 4 New techniques for witnessing subsystems 5 Probabilistic systems with low tree width 6 Explications for probabilistic timed automata 7 Conclusion

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In this thesis, a new approach for generating reaction time predictions for Bayesian cognitive models of sequential decision-making is proposed. The method is based on a Markov chain Monte Carlo algorithm that, by utilizing prior distributions and likelihood functions of possible action sequences, generates predictions about the time needed to choose one of these sequences. The plausibility of the reaction time predictions produced by this algorithm was investigated for simple exemplary distributions as well as for prior distributions and likelihood functions of a Bayesian model of habit learning. Simulations showed that the reaction time distributions generated by the Markov chain Monte Carlo sampler exhibit key characteristics of reaction time distributions typically observed in decision-making tasks. The introduced method can be easily applied to various Bayesian models for decision-making tasks with any number of choice alternatives. It thus provides the means to derive reaction time predictions for models where this has not been possible before. / In dieser Arbeit wird ein neuer Ansatz zum Generieren von Reaktionszeitvorhersagen für bayesianische Modelle sequenzieller Entscheidungsprozesse vorgestellt. Der Ansatz basiert auf einem Markov-Chain-Monte-Carlo-Algorithmus, der anhand von gegebenen A-priori-Verteilungen und Likelihood-Funktionen von möglichen Handlungssequenzen Vorhersagen über die Dauer einer Entscheidung für eine dieser Handlungssequenzen erstellt. Die Plausibilität der mit diesem Algorithmus generierten Reaktionszeitvorhersagen wurde für einfache Beispielverteilungen sowie für A-priori-Verteilungen und Likelihood-Funktionen eines bayesianischen Modells zur Beschreibung von Gewohnheitslernen untersucht. Simulationen zeigten, dass die vom Markov-Chain-Monte-Carlo-Sampler erzeugten Reaktionszeitverteilungen charakteristische Eigenschaften von typischen Reaktionszeitverteilungen im Kontext sequenzieller Entscheidungsprozesse aufweisen. Das Verfahren lässt sich problemlos auf verschiedene bayesianische Modelle für Entscheidungsparadigmen mit beliebig vielen Handlungsalternativen anwenden und eröffnet damit die Möglichkeit, Reaktionszeitvorhersagen für Modelle abzuleiten, für die dies bislang nicht möglich war.

Konventionelle, optische Transportnetze haben die Bereitstellung von High-Speed-Konnektivität in Form von langfristig installierten Verbindungen konstanter Bitrate ermöglicht. Die Einrichtungszeiten solcher Verbindungen liegen in der Größenordnung von Wochen, da in den meisten Fällen manuelle Eingriffe erforderlich sind. Nach der Installation bleiben die Verbindungen für Monate oder Jahre aktiv. Das Aufkommen von Grid Computing und Cloud-basierten Diensten bringt neue Anforderungen mit sich, die von heutigen optischen Transportnetzen nicht mehr erfüllt werden können. Dies begründet die Notwendigkeit einer Umstellung auf dynamische, optische Netze, welche die kurzfristige Bereitstellung von Bandbreite auf Nachfrage (Bandwidth on Demand - BoD) ermöglichen. Diese Netze müssen Verbindungen mit unterschiedlichen Bitratenanforderungen, mit zufälligen Ankunfts- und Haltezeiten und stringenten Einrichtungszeiten realisieren können. Grid Computing und Cloud-basierte Dienste führen in manchen Fällen zu Verbindungsanforderungen mit Haltezeiten im Bereich von Sekunden, wobei die Einrichtungszeiten im Extremfall in der Größenordnung von Millisekunden liegen können. Bei optischen Netzen für BoD muss der Verbindungsaufbau und -abbau, sowie das Netzmanagement ohne manuelle Eingriffe vonstattengehen. Die dafür notwendigen Technologien sind Flex-Grid-Wellenlängenmultiplexing, rekonfigurierbare optische Add / Drop-Multiplexer (ROADMs) und bandbreitenvariable, abstimmbare Transponder. Weiterhin sind Online-Ressourcenzuweisungsmechanismen erforderlich, um für jede eintreffende Verbindungsanforderung abhängig vom aktuellen Netzzustand entscheiden zu können, ob diese akzeptiert werden kann und welche Netzressourcen hierfür reserviert werden. Dies bedeutet, dass die Ressourcenzuteilung als Online-Optimierungsproblem behandelt werden muss. Die Entscheidungen sollen so getroffen werden, dass auf lange Sicht ein vorgegebenes Optimierungsziel erreicht wird. Die Ressourcenzuweisung bei dynamischen optischen Netzen lässt sich in die Teilfunktionen Routing- und Spektrumszuteilung (RSA), Verbindungsannahmekontrolle (CAC) und Dienstgütesteuerung (GoS Control) untergliedern. In dieser Dissertation wird das Problem der Online-Ressourcenzuteilung in dynamischen optischen Netzen behandelt. Es wird die Theorie der Markov-Entscheidungsprozesse (MDP) angewendet, um die Ressourcenzuweisung als Online-Optimierungsproblem zu formulieren. Die MDP-basierte Formulierung hat zwei Vorteile. Zum einen lassen sich verschiedene Optimierungszielfunktionen realisieren (z.B. die Minimierung der Blockierungswahrscheinlichkeiten oder die Maximierung der wirtschaftlichen Erlöse). Zum anderen lässt sich die Dienstgüte von Gruppen von Verbindungen mit spezifischen Verkehrsparametern gezielt beeinflussen (und damit eine gewisse GoS-Steuerung realisieren). Um das Optimierungsproblem zu lösen, wird in der Dissertation ein schnelles, adaptives und zustandsabhängiges Verfahren vorgestellt, dass im realen Netzbetrieb rekursiv ausgeführt wird und die Teilfunktionen RSA und CAC umfasst. Damit ist das Netz in der Lage, für jede eintreffende Verbindungsanforderung eine optimale Ressourcenzuweisung zu bestimmen. Weiterhin wird in der Dissertation die Implementierung des Verfahrens unter Verwendung eines 3-Way-Handshake-Protokolls für den Verbindungsaufbau betrachtet und ein analytisches Modell vorgestellt, um die Verbindungsaufbauzeit abzuschätzen. Die Arbeit wird abgerundet durch eine Bewertung der Investitionskosten (CAPEX) von dynamischen optischen Netzen. Es werden die wichtigsten Kostenfaktoren und die Beziehung zwischen den Kosten und der Performanz des Netzes analysiert. Die Leistungsfähigkeit aller in der Arbeit vorgeschlagenen Verfahren sowie die Genauigkeit des analytischen Modells zur Bestimmung der Verbindungsaufbauzeit wird durch umfangreiche Simulationen nachgewiesen. / Conventional optical transport networks have leveraged the provisioning of high-speed connectivity in the form of long-term installed, constant bit-rate connections. The setup times of such connections are in the order of weeks, given that in most cases manual installation is required. Once installed, connections remain active for months or years. The advent of grid computing and cloud-based services brings new connectivity requirements which cannot be met by the present-day optical transport network. This has raised awareness on the need for a changeover to dynamic optical networks that enable the provisioning of bandwidth on demand (BoD) in the optical domain. These networks will have to serve connections with different bit-rate requirements, with random interarrival times and durations, and with stringent setup latencies. Ongoing research has shown that grid computing and cloud-based services may in some cases request connections with holding times ranging from seconds to hours, and with setup latencies that must be in the order of milliseconds. To provide BoD, dynamic optical networks must perform connection setup, maintenance and teardown without manual labour. For that, software-configurable networks are needed that are deployed with enough capacity to automatically establish connections. Recently, network architectures have been proposed for that purpose that embrace flex-grid wavelength division multiplexing, reconfigurable optical add/drop multiplexers, and bandwidth variable and tunable transponders as the main technology drivers. To exploit the benefits of these technologies, online resource allocation methods are necessary to ensure that during network operation the installed capacity is efficiently assigned to connections. As connections may arrive and depart randomly, the traffic matrix is unknown, and hence, each connection request submitted to the network has to be processed independently. This implies that resource allocation must be tackled as an online optimization problem which for each connection request, depending on the network state, decides whether the request is admitted or rejected. If admitted, a further decision is made on which resources are assigned to the connection. The decisions are so calculated that, in the long-run, a desired performance objective is optimized. To achieve its goal, resource allocation implements control functions for routing and spectrum allocation (RSA), connection admission control (CAC), and grade of service (GoS) control. In this dissertation we tackle the problem of online resource allocation in dynamic optical networks. For that, the theory of Markov decision processes (MDP) is applied to formulate resource allocation as an online optimization problem. An MDP-based formulation has two relevant advantages. First, the problem can be solved to optimize an arbitrarily defined performance objective (e.g. minimization of blocking probability or maximization of economic revenue). Secondly, it can provide GoS control for groups of connections with different statistical properties. To solve the optimization problem, a fast, adaptive and state-dependent online algorithm is proposed to calculate a resource allocation policy. The calculation is performed recursively during network operation, and uses algorithms for RSA and CAC. The resulting policy is a course of action that instructs the network how to process each connection request. Furthermore, an implementation of the method is proposed that uses a 3-way handshake protocol for connection setup, and an analytical performance evaluation model is derived to estimate the connection setup latency. Our study is complemented by an evaluation of the capital expenditures of dynamic optical networks. The main cost drivers are identified. The performance of the methods proposed in this thesis, including the accuracy of the analytical evaluation of the connection setup latency, were evaluated by simulations. The contributions from the thesis provide a novel approach that meets the requirements envisioned for resource allocation in dynamic optical networks.

Diese Dissertation fasst drei wissenschaftliche Arbeiten (Artikel) zusammen, welche sich mit veränderten Entscheidungsprozessen bei substanzgebundenen- und substanzungebundenen Abhängigkeitserkrankungen beschäftigen. In Artikel I wurde beobachtet, dass Probanden mit Alkoholkonsumstörung (AD) und Probanden mit Glücksspielstörung (GD) eine ähnlich reduzierte Verlustaversion gegenüber gesunden Kontrollen (HC) aufweisen. Beide Gruppen zeigten jedoch unterschiedliche neuronale Korrelate dieser reduzierten Verlustaversion: Während AD-Probanden eine unterschiedliche funktionelle Aktivität im dorsal-lateralen-präfrontalen Kortex im Vergleich zu HC aufwiesen, zeigten GD-Probanden eine veränderte funktionelle Konnektivität zwischen Amygdala und orbito-frontalem Kortex (OFC) bzw. medial-präfrontalem Kortex. In den Artikeln II und III wurde untersucht, ob das Verhalten und die neuronale Aktivität bei einer Verlustaversionsaufgabe bei GD-Probanden moduliert wird, wie dies in ähnlichen Studien bei AD-Probanden beobachtet wurde. Tatsächlich konnten GD-Probanden von HC-Probanden auf Grundlage ihrer veränderten Glücksspielannahme während der Präsentation spielbezogener Hinweisreize unterschieden werden. Auf neuronaler Ebene (Artikel III) konnten GD-Probanden von HC-Probanden durch die neuronalen Korrelate der reizinduzierten Veränderungen im Spielverhalten in einem Netzwerk aus Amygdala, Nucleus Accumbens und OFC unterschieden werden. Da in den Studien der Fokus auf Glücksspielabhängigkeit lag, also auf einer Abhängigkeit, welche unabhängig von Substanzmissbrauch existiert, deuten die hier diskutierten Ergebnisse darauf hin, dass verminderte Verlustaversion, sowie erhöhte reizinduzierte Veränderungen im Entscheidungsverhalten – welches beides bekannte Phänomene von Substanzabhängigkeiten sind – nicht durch Substanzmissbrauch zustande kommen. Beide Phänomene scheinen vielmehr erlernte Merkmale oder sogar prädisponierende Faktoren von Abhängigkeitserkrankungen zu sein. / This dissertation summarizes three papers concerned with decision-making impairments in a substance-based and a non-substance-based addictive disorder. In Paper I, it was observed that subjects with alcohol use disorder (AD) and subjects with gambling disorder (GD) show similarly reduced loss aversion. Both groups, however, showed different neural correlates of this reduced loss aversion: While AD subjects showed different functional activity in dorsal-lateral-prefrontal cortex compared to healthy controls (HC), GD subjects showed different amygdala-orbital-frontal and amygdala-medial-prefrontal connectivity. Paper II and III investigated whether behavior and neural activity in a loss aversion task is modulated in GD subjects, as has been observed in similar studies in AD subjects. The data showed that GD subjects can be distinguished from HC subjects using a behavioral pattern of increased cue-induced gamble increase when gambling-related cues are presented in the background. On neural level (Paper III), GD subjects could be distinguished from HC subjects by neural correlates of cue-induced changes in gambling behavior in a network of amygdala, nucleus accumbens and orbital-frontal cortex. Since the focus of the studies was GD, an addiction that is independent of substance abuse, the results suggest that reduced loss aversion and increased cue-induced changes in gambling behaviors, two phenomena related to substance-based addictions, are not dependent on a substance of abuse but rather on learned characteristics or even on predisposing traits of addictive disorders.

Die flexible Anwendung von Wissen in neuen Alltagssituationen ist eine notwendige kognitive Fähigkeit. Bisherige Studien betonen die zentrale Rolle des Hippocampus beim Lernen und Verknüpfen neuer Informationen mit bereits vorhandenem Wissen. Die funktionelle Integrität des Hippocampus ändert sich jedoch im Laufe des Lebens bzw. wird durch neuropsychiatrische Erkrankungen häufig beeinflusst. Die betroffenen Personen müssen deswegen adaptive Strategien entwickeln, um behaviorale Ziele weiter zu erreichen. Daher befasst sich meine Doktorarbeit mit Adaptationsprozessen im sich entwickelnden Gehirn und im vollständig entwickelten Gehirn mit einer hippocampalen Dysfunktion. Diese Synopsis umfasst dazu drei Studien: (1) zu behavioralen Strategien im sich entwickelnden Gehirn, (2) zu behavioralen Strategien im vollständig entwickelten Gehirn nach einer Läsion und (3) zu strukturellen Veränderungen im vollständig entwickelten Gehirn nach einer Läsion. Studie 1 zeigt einen altersgebundenen Wechsel beim assoziativen Gedächtnis: Kinder, Jugendliche und junge Erwachsene benutzen verschiedene Gedächtnisstrategien beim Integrieren von Gedächtnisinhalten. Studie 2 zeigt, dass die beobachteten Gedächtnisbeeinträchtigungen bei Patienten mit rechtsseitigen hippocampalen Läsionen sich nicht alleine durch ein Defizit des assoziativen Gedächtnisses erklären lassen, sondern auf einen zusätzlichen hippocampalen Beitrag zur Gedächtnisintegration zurückzuführen sind. Studie 3 zeigt, dass sich postoperative Adaptationsprozesse auf struktureller Ebene in überraschend kurzer Zeit ereignen und dass die strukturelle Reorganisation nicht nur im Hippocampus, sondern auch in entfernteren Hirnregionen, die mit dem Hippocampus verbunden sind, stattfindet. Zusammenfassend zeigen die Ergebnisse der drei Studien, dass Adaptationsprozesse im sich entwickelnden Gehirn sowie bei Erwachsenen mit einer hippocampalen Dysfunktion sowohl auf der behavioralen als auch auf der strukturellen Ebene auftreten. / Applying knowledge flexibly to new situations is a cognitive faculty that is necessary in every-day life. Previous findings emphasise the crucial role the hippocampus plays in learning and linking new information with pre-existing knowledge. However, the functional integrity of the hippocampus changes over the lifespan and is frequently affected by neuropsychiatric disorders. The affected subjects must, therefore, develop adaptive strategies to achieve behavioural goals. Thus, my doctoral thesis deals with adaptation processes in the developing brain and in adult brains with a hippocampal dysfunction. This synopsis encompasses three studies on: (1) behavioural strategies in the developing brain, (2) behavioural strategies in the lesioned fully developed brain, and (3) structural changes in the lesioned fully developed brain. Study 1 suggests an age-related shift in the associative memory: Children, adolescents, and young adults use different memory strategies when integrating information. Study 2 suggests that the memory deficits observed in patients with right-sided hippocampal lesions are not merely a consequence of an impaired associative memory but rather result from an additional hippocampal contribution to the memory integration. Study 3 suggests that postoperative structural adaptation processes occur on a surprisingly short time-scale, and this structural reorganisation happens not only in the hippocampus but also in distant brain areas connected to the hippocampus. In conclusion, findings from these three studies show that adaptation processes in the developing brain and in adult brains with hippocampal dysfunction occur on both the behavioural and the structural level.